Applications of AI : Industrial

---

One Hundred Year Study on Artificial Intelligence (AI100) | “AI and Life in 2030” | AITopics |

AI index | 2017-index report | AI index public data |

---

Contents

• AI approaches
• The Multidisciplinary
• Why AI is on Rise
• AI Goals, Techniques and Tools
  • Goals
  • Methods, categories, algorithms and applications
    • Algorithm Categories
    • Algorithms and Applications by Data Type
    • Algorithms and Applications by Task
    • Regression, Univariate, Multivariate
    • Classification Unary, Binary and Multiclass
    • Regularization and Overfitting Prevention
    • Clustering
    • Ensemble Methods Supervised, Unsupervised
    • Recommender Systems and Recommendations
    • Neural Networks and Deep Learning
    • Anomaly Detection Supervised, Unsupervised, Semisupervised
    • Reinforcement Learning
    • Model selection, validation, and resampling methods
    • Model tuning bias variance tradeoff and model complexity
    • Feature extraction, feature selection, and feature engineering
    • Dimensionality Reduction
    • Information Retrieval
    • Logical Reasoning
    • Optimization and Search
    • Mathematical Finance and Risk Management
    • Ranking
    • Time-series
    • Survival
    • Forecasting
    • Simulation
    • Segmentation
    • Experimentation and Experimental Design
    • Embedded
    • Hypothesis Testing
    • Hybrid Solutions and Applications
    • Other Algorithms
    • Literature surveys
  • CV Methods
  • NLP Methods
  • Speech Methods
  • Robotics Methods

Back to Top

• AI Tools
• Hardware Options Infrastructure for AI
• AI, Cloud, API's and Platforms
• DataMining MachineLearning DeepLearning Applications
• Computer Vision Applications
• NLP Applications
• Speech Recog Applications
• AI in Robotics Applications
• Bigdata and Data Science
  • Desciplines
  • Data pipeline
  • Datascience puzzle and AI Lock in Loop
  • The Heirarchy
  • Big Data Abstraction
  • Bigdata Tools
  • Datascience Desciplines
  • Architectural Goals, Principles, and Considerations
    • Data types and sources
    • Big Data Architecture Patterns
    • Enterprise Big Data Architectural Components
    • AWS
    • Google Cloud Platform
    • By Technology
    • Oracle Architecture and Patterns Examples
    • IBM Architecture and Patterns Examples
    • Big Data Exploration Example Architecture IBM
    • Big data and analytics architecture on cloud IBM
  • Bigdata, AI, Datascience Reads
    • Advanced Analytics
    • Artificial Intelligence
    • Machine Learning
    • Data Science
    • Data Scientists
    • Statistics, Probability, and Mathematics
    • Jobs, Skills, and Salary Trends
    • Internet of Things (IoT)
    • Tools and Language Popularity, Comparisons, Trends, ...
    • Freelancing and Consulting
    • Online Learning
    • Big Data and Data Engineering
    • Databases, Schemas, and Data Modeling
    • Product
    • Methodologies
    • Computer Science and Programming
    • Business
    • Gartner Magic Quadrants
    • architectures
  • References
  • Online Visualization
  • What would you like to show
  • Cheatsheets
  • Python for Bigdata
  • R for Bigdata
  • Open Datasets

Back to Top

• AI MTDB
  • Movies
  • TV shows
  • Dcoumentaries
  • Books
• Principles and Rules
• AI Industrial Applications
  • Healthcare
  • Manufacturing
  • Legal or Law
  • Games
  • Telecommunications
  • Music
  • HR and recruitment
  • Agriculture
  • Education
  • Media and Entertainment
  • Advertising and Marketing
  • Retail and Fashion
  • Cybersecurity
  • Aviation
  • Automotive and Trasportation
  • Finance and Banking
  • Logistics
  • Customer Service experience and engagement
  • Oil and gas
  • IT industry
• AI Safety Surveys
• Quotes on AI

Back to Top

---

---

AI approaches

Back to Top

| Workshop | logic-ai |

• Symbolic AI(formal logic - (1950-1980) - "If an otherwise healthy adult has a fever, then they may have influenza"
• Bayesian inference(Statistical AI) - "If the current patient has a fever, adjust the probability they have influenza in such-and-such way"
• Machine learning and data mining - "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza"
• Artificial Neural Network - inspired by how the brain's machinery works(neurons).
• Evolutionary AI - uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and natural selection.

Note: But not only above five(extremely hyped and popular in routine business AI applications), researchers trying different approaches to achieve general AI

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering? Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?

Back to Top

---

What is the difference between data mining, statistics, machine learning and AI? Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different approaches? What exactly do they have in common and where do they differ? If there is some kind of hierarchy between them, what would it be?

In short

• Statistics quantifies numbers
• Data Mining explains patterns
• Machine Learning predicts with models
• Artificial Intelligence behaves and reasons

Statistics is just about the numbers, and quantifying the data. There are many tools for finding relevant properties of the data but this is pretty close to pure mathematics.

Data Mining is about using Statistics as well as other programming methods to find patterns hidden in the data so that you can explain some phenomenon. Data Mining builds intuition about what is really happening in some data and is still little more towards math than programming, but uses both.

Machine Learning uses Data Mining techniques and other learning algorithms to build models of what is happening behind some data so that it can predict future outcomes. Math is the basis for many of the algorithms, but this is more towards programming.

Artificial Intelligence uses models built by Machine Learning and other ways to reason about the world and give rise to intelligent behavior whether this is playing a game or driving a robot/car. Artificial Intelligence has some goal to achieve by predicting how actions will affect the model of the world and chooses the actions that will best achieve that goal. Very programming based.

more detailed explaination

The Multidisciplinary

Back to Top

---

Why AI is on Rise

Back to Top

---

AI Goals, Techniques and Tools

Goals

Back to Top

• Reasoning, problem solving
• Knowledge representation (commonsense knowledge)
• Planning
• Learning
• Natural language processing
• Perception/Vision
• Motion and manipulation
• Social intelligence
• General intelligence

---

Methods, categories, algorithms and applications

Back to Top

Algorithm Categories

Back to Top

• Decision tree learning
• Association rule learning
• Artificial neural networks
• Inductive logic programming
• Support vector machines
• Clustering
• Bayesian networks
• Reinforcement learning
• Representation learning
• Similarity and metric learning
• Sparse dictionary learning
• Genetic algorithms

---

Algorithms and Applications by Data Type

Back to Top

• Sound/Audio
  • Voice detection/recognition
  • Voice search
  • Speaker identification
  • Sentiment analysis
  • Flaw detection (engine noise)
  • Fraud detection (latent audio artifacts)
  • Speech-to-Text
• Time Series/sequence
  • Log analysis/Risk detection
  • Enterprise resource planning
  • Predictive analysis using sensor data
  • Business and economic analysis
  • Recommendation engine
  • Examples and algorithms
    • Web log
      • RNN
    • Time series in general (has time stamp)
      • RNN
    • Sensors and measures over time
      • RNN
    • Arbitrarily long sequence that may take full input data
      • RNN
      • Markova model with large hidden state space
    • Fixed length sequence
      • CNN
      • Multilayer perceptron
• Text
  • Sentiment analysis
  • Augmented search, theme detection
  • Threat detection
  • Fraud detection
  • Named-entity recognition
• Image
  • Facial recognition and expression recognition
  • People identification
  • Image search
  • Machine vision
  • Photo clustering
  • Image recognition/classification
    • Is it a certain class or multiple classes (e.g., cat, car, ...)
  • Object recognition and detection
    • Detection is the location of the recognized object in the image (i.e., localization)
      • Output is bounding box (b_x, b_y, b_h, B_w), is object recognized in image, and class label(s)
      • Loss function calculation depends on whether the object is detected in the image
      • Sliding window detection (window size and stride)
        • Pass window as input to CNN
  • Landmark detection
    • X,Y point pairs representing individual landmarks in the image
    • Useful for emotion detection, filters, pose detection, ...
  • Algorithms
    • CNN
• Video
  • Motion detection
  • Real-time threat detection
  • Gesture recognition
• Unlabeled and/or unstructured data
  • Clustering
  • Anamoly detection (detecting anamolies)
  • Search (detecting similarities)
    • Compare docs, images, sounds, etc., and return similar items
• Labeled data
  • Predictive analytics
    • Regression and classification
      • Hardware failure
      • Health degredation, failure, and disease
      • Customer churn
      • Employee churn
• Columnar/tabular
  • Classic multilayer perceptrons + feature engineering

---

Algorithms and Applications by Task

Back to Top

• Prediction
  • Regression/classification
    • RNN
• Recommendation
• Generative
  • Novel output
    • RNN
• Reconstruction
  • Example: MINST
• Recognition and computer vision
  • Changing images in time (video)
    • LSTM (temporal aspect) with convolutions layers (capture structure/features)
• NLP, NLG, NLU
  • Machine translation
    • CNN
  • Sentiment analysis
    • CNN
  • Sentence classification
    • CNN
• Personal assistant
  • Voice to text then NLP to understand what the user wants to accomplish, then generating text, voice, action
• Anamoly detection
• Reinforcement learning
• Reality capture and reality computing

---

Regression, Univariate, Multivariate

Back to Top

• Simple and multiple linear regression
• Tree-based methods (e.g., decision tree or forest)
• Generalized linear models (GLM)
  • Poisson regression, aka log-linear model
• Generalized additive model (GAM)
• Regression with shrinkage (e.g., regularization)
• Stepwise regression
• Ordinary least squares
• Artificial Neural networks (ANN) and deep learning
• Ordinal regression
• Polynomial regression
• Nearest neighbor methods (e.g., k-NN or k-Nearest Neighbors)
• Gradient tree boosting
• Logistic regression
• Nonlinear regression

Example Applications

• Stock market predictions and algorithmic trading
  • Companies
    • Kavout
    • Sentient
    • Genotick
    • Numerai
    • QPLUM

---

Classification Unary, Binary and Multiclass

Back to Top

• Linear
  • Linear discriminant analysis (LDA), aka Fisher's linear discriminant
  • Logistic regression and multinomial logistic regression
  • Bayesian classifiers (as opposed to frequentist)
    • Naive Bayes
  • Perceptron methods
• Decision trees and random forests
• Naive bayes
• Hidden markov model
• Support vector machines (SVM)
  • Least squares support vector machines
• Artificial Neural networks (ANN) and deep learning
• Kernel estimation
  • Nearest neighbor methods (e.g., k-NN or k-Nearest Neighbors)
• One vs Rest and One vs One (binary transformation)
• Gradient tree boosting

Example Applications

• Many diseases or issues, including stroke, cancer, ...
  • Cancer detection using cell-free genomes
  • Cardiovascular events prediction (e.g., heart attack, stroke)
  • Companies
    • Google DeepMind
    • IBM's Watson (Watson for Oncology)
    • Others
      • Freenome
      • CureMetrix
• Spam for email
• Smart email categorization (Gmail)
  • Primary, social, and promotion inboxes, as well as labeling emails as important
• Credit decisions
  • Companies
    • Underwrite.ai

---

Regularization and Overfitting Prevention

Back to Top

• Least absolute shrinkage and selection operator (LASSO)
• Ridge regression
• Akaike information criterion (AIC)
• Bayesian information criterion (BIC)

---

Clustering

Back to Top

• Hierarchical clustering, aka connectivity-basedclustering and Hierarchical Cluster Analysis (HCA)
  • Single-linkage clustering
  • Complete linkage clustering
  • Unweighted Pair Group Method with Arithmetic Mean (UPGMA), aka average linkage clustering
• Centroid-based clustering
  • k-means
  • k-medoids
  • k-medians
  • K-means++
  • Fuzzy c-means
• Distribution-based clustering
  • Gaussian mixture models via expectation-maximization algorithm
• Density-based clustering
  • Density-based spatial clustering of applications with noise (DBSCAN)
  • Ordering points to identify the clustering structure (OPTICS)
  • Mean-shift
• Canapoy
• Association rule learning
  • Apriori
  • Eclat
• Topic modeling (text data)
• Fractal
• Guassian mixture models

Example Applications

---

Ensemble Methods Supervised, Unsupervised

Back to Top

• Bootstrap aggregating (bagging)
  • Random Forests and ExtraTrees
• Boosting
  • AdaBoost
  • Gradient boosting
  • Boost by majority
  • BrownBoost
  • xgboost
  • MadaBoost
  • LogitBoost
  • LPBoost
  • TotalBoost
• Pasting
• Bayesian model averaging (BMA)
• Weak learner theory
• Stacking (stacked generalization) and Blending
• Bayes optimal classifier
• Bayesian parameter averaging (BPA)
• Bayesian model combination (BMC)
• Bucket of models

---

Recommender Systems and Recommendations

Back to Top

• Collaborative filtering
• Content-based filtering
• Graph-based methods

Example Applications

• Netflix
  • Increase engagement, retention, and revenues
  • Examples
    • "Because you watched ..."
    • "Top picks for ..."
    • Recommendations by category
      • Trending Now
      • Neflix originals
      • TV Documentaries
• Amazon
  • Increase average order size and therefore sales (studies show between 5.9 to 30%)
  • Examples
    • "Customers who bought this item also bought"
    • "Customers who viewed this item also viewed"
    • "What other items do customers buy after viewing this item?"
    • "Recommendations for you in ..." (e.g., "Recommended for You in Kindle Books")
    • "New for you"
    • "Inspired by your shopping trends"
    • "Inspired by your Wish List"
• Robo-advisors and portfolio rebalancing
  • Weathfront
  • Betterment
• Spotify
  • Daily Mix
• Personalized news feeds, including Facebook

---

Neural Networks and Deep Learning

Back to Top

• Feed forward neural networks (FF or FFNN) and perceptrons (P)
• Radial basis function (RBF)
• Hopfield network (HN)
• Markov chains (MC or discrete time Markov Chain, DTMC)
• Boltzmann machines (BM)
• Restricted Boltzmann machines (RBM)
• Autoencoders (AE)
• Sparse autoencoders (SAE)
• Variational autoencoders (VAE)
• Denoising autoencoders (DAE)
• Deep belief networks (DBN)
• Convolutional neural networks (CNN or deep convolutional neural networks, DCNN)
• Deconvolutional networks (DN)
• Deep convolutional inverse graphics networks (DCIGN)
• Generative adversarial networks (GAN)Recurrent neural networks (RNN)Long / short term memory (LSTM)
  • CycleGAN
  • DiscoGAN
  • StarGAN
• Gated recurrent units (GRU)
• Neural Turing machines (NTM)
• Bidirectional recurrent neural networks, bidirectional long / short term memory networks and bidirectional gated recurrent units (BiRNN/BRNN, BiLSTM and BiGRU respectively)
• Deep residual networks (DRN)
• Echo state networks (ESN)
• Extreme learning machines (ELM)
• Liquid state machines (LSM)
• Support vector machines (SVM)
• Kohonen networks (KN, also self organising (feature) map, SOM, SOFM)

Example Applications

• Feed forward neural network and Multilayer perceptron
  • Regression and classifications
• Restricted Boltzmann machine
  • Dimensionality reduction
  • Feature extraction/learning
  • Classification
  • Recommender systems
  • Topic modeling
  • Pretraining for weight initialization
• Autoencoders
  • Dimensionality reduction
  • Anomaly detection
  • Generative modeling
• Convolutional neural network
  • Image recognition
  • Video recognition
  • Automatic speech recognition (ASR)
  • Recommender systems
  • Natural language processing
• Recurrent neural network
  • Language modeling
  • Machine translation
  • Handwriting recognition
  • Speech recognition
  • Multilingual Language Processing
  • Natural language processing
• Self-organizing map
  • Dimensionality reduction
• Generative models
• Combinations
  • Image captioning (LSTM + CNN)

---

Anomaly Detection Supervised, Unsupervised, Semisupervised

Back to Top

Algorithms

• Density-based techniques - K-nearest neighbor, Local outlier factor
• Subspace and correlation-based outlier detection for high-dimensional data
• One class support vector machines
• Replicator neural networks
• Cluster analysis-based outlier detection
• Deviations from association rules and frequent itemsets
• Fuzzy logic based outlier detection
• Ensemble techniques, using feature bagging, score normalization and different sources of diversity
• PCA (Principle component analysis)

Example Applications

• Per Wikipedia
  • Intrusion detection
  • Fraud detection
  • Fault detection
  • System health monitoring
  • Event detection in sensor networks
• Manufacturing
• Data security
  • Companies
    • Cylance
    • Darktrace
• Personal security (security screenings at airports, stadiums, concerts, and other venues)
• Law enforcement
• Application performance
• Credit card fraud detection

---

Reinforcement Learning

Back to Top

• Q-learning
• Markov decision process (MDP)
• Finite MDPs
• Monte Carlo methods
• Criterion of optimality
• Brute force
• Value function estimation
• Direct policy search
• Temporal difference methods
• Generalized policy iteration
• Stochastic optimization
• Gradient ascent
• Simulation-based optimization
• Learning Automata[edit]
• Example
  • Multi-armed bandit problem

Example Applications

---

Model selection, validation, and resampling methods

Back to Top

• Cross-validation
• Hyperparameter optimization
• Bootstrap
• Mallow’s Cp
• Akaike information criterion (AIC)
• Bayesian information criterion (BIC)
• Minimum description length (MDL)

---

Model tuning: bias variance tradeoff and model complexity

Back to Top

• Validation curve
• Learning curve
• Residual sum of squares
• Goodness-of-fit metrics
• Grid search

Feature extraction, feature selection, and feature engineering

Back to Top

• Wrapper methods
• Sensitivity analysis
• PCA
• Random forests
  • Mean decrease impurity
  • Mean decrease accuracy
• Text-based
  • Stemming
  • Tokenizing
  • Synonym substitutions
• Least absolute shrinkage and selection operator (LASSO)
• Subset selection

---

Dimensionality Reduction

Back to Top

• Principle component analysis (PCA)
• Kernel PCA
• Locally-Linear Embedding (LLE)
• t-distributed Stochastic Neighbor Embedding (t-SNE)
• Factor analysis
• K-means clustering
• Canopy clustering
• Feature hashing
• Wrapper methods
• Sensitivity analysis
• Self organizing maps
• Text data
  • Term frequency (TF)
  • Inverse document frequency (IDF)
• Latent Dirichlet Allocation (LDA)

---

Information Retrieval

Back to Top

• Discounted cumulative gain (DCG)
• Discounted cumulative gain (nDCG)
• Term frequency–inverse document frequency (TF-IDF)

---

Logical Reasoning

Back to Top

• Expert systems
• Logical reasoning

---

Optimization and Search

Back to Top

• Stochastic search
• Stochastic optimization (SO) methods
• Genetic algorithms
• Simulated annealing
• Gradient search
• Linear programming
• Integrer programming
• Non-linear programming
• Active learning
• Ensemble learning
• Minimum
• Maximum
• Optimal value or optimal combination
• Metaheuristic methods
• Randomized search methods
• Tree search
• Monte Carlo tree search (MCTS)
• Evolutionary computation

---

Mathematical Finance and Risk Management

Back to Top

• Risk management
• Mathematical/quantitative Finance
• Linear Regression
• Monte Carlo methods
• Empirical risk minimization

Example Applications

---

Ranking

Back to Top

• Ranking

Example Applications

---

Time-series

Back to Top

• Time series
• Rolling means
• Autocorrelation
• Frequency vs time domains and transfers (e.g., spectral analysis)
• Trend and residual component decomposition
• ARIMA modeling for forecasting and detecting trends

Example Applications

---

Survival

Back to Top

• Survival analysis

Example Applications

---

Forecasting

Back to Top

• Last period demand
• Simple and weighted N-Period moving averages
• Simple exponential smoothing
• Poisson process model based forecasting and multiplicative seasonal indexes
• Average approach
• Naïve approach
• Drift method
• Seasonal naïve approach
• Time series methods
  • Moving average
  • Weighted moving average
  • Kalman filtering
  • Exponential smoothing
  • Autoregressive moving average (ARMA)
  • Autoregressive integrated moving average (ARIMA)
  • Extrapolation
  • Linear prediction
  • Trend estimation
  • Growth curve (statistics)
• Causal / econometric forecasting methods
  • Regression analysis
    • Parametric (linear or non-linear)
    • Non-parametric techniques
  • Autoregressive moving average with exogenous inputs (ARMAX)
• Judgmental methods
  • Composite forecasts
  • Cooke's method
  • Delphi method
  • Forecast by analogy
  • Scenario building
  • Statistical surveys
  • Technology forecasting
• Artificial intelligence methods
  • Artificial neural networks
  • Group method of data handling
  • Support vector machines
• Other
  • Simulation
  • Prediction market
  • Probabilistic forecasting and Ensemble forecasting
• Considerations
  • Seasonality and cyclic behaviour

Example Applications

---

Simulation

Back to Top

• Discrete event simulation
• Markov models
• Agent-based simulations
• Monte carlo simulations
• Systems dynamics
• Activity-based simulation
• ODES and PDES
• Fuzzy logic

Example Applications

---

Segmentation

Back to Top

• Behavioral
• Demographic
• Geographic

Example Applications

---

Experimentation and Experimental Design

• Design of Experiments (DOE)
• A/B testing

Example Applications

---

Embedded

Back to Top

• Deep learning

Example Applications

• Robotic cognition

---

Hypothesis Testing

Back to Top

• T-test - Compare two groups
• ANOVA - Compare multiple groups

---

Hybrid Solutions and Applications

Back to Top

Example Applications

• Google search
• Autonymous vehicles (Business insider)
  • Reduce accidents and related injuries and death
  • Improved traffic (via ridesharing and smart traffic lights) and fuel efficiency
  • Reduced carbon emissions
  • Faster commutes and travel time
  • Get your time back in the vehicle to do what you want
  • Efficient ride-sharing
  • Companies
    • Zoox
    • Nauto
    • nuTonomy
• Home monitoring, control, and security
  • Companies
    • Flare
• Voice-controled robotics
• Photo-realistic pictures generation from text or sketches
  • NYU article
• Music generation
  • Companies
    • Jukedeck
• Movie and script generation
• Automatically generated software code
  • Companies
    • DeepCoder (Microsoft and Cambridge)
• Authentication without passwords (using mobile phone that knows it's you)
  • Companies
    • TypingDNA
• Customer support
  • Companies
    • DigitalGenius
• Optimized directions and routes
• Plagiarism Checkers
• Robo-readers and graders
• Virtual reality
• Gaming
• Zillow’s “zestimate” feature, which estimates the price of homes
• Medical/Health
  • Companies
    • BenevolentAI
• Sales
  • Companies
    • InsideSales.com
• Crime
  • Who, Type, and location
  • Based on previous crime and social media activity
  • Companies
    • BRS Labs AISight
• Suicide risk
  • Based on a lot of different risk factors
  • Companies
    • Facebook
    • Instagram
    • Cogito
• Agriculture - predicting crop yields
  • Companies
    • Descartes Lab
    • Stanford's Sustainability and Artificial Intelligence Lab
• Uber's ETA

---

Other Algorithms

Back to Top

• Massive-scale graph
• Geospatial temporal predictive analytics
• Hyperfast analytics
• Embedded deep learning
• Cognitive machine learning and IoT
• Natural language processing, generation, and understanding
• Structured database generation
• Game theory
• Control theory
• Operations research
• Information theory
• Simulation-based optimization
• Multi-agent systems
• Swarm intelligence
• Genetic algorithms

---

Literature surveys

Back to Top

• mlsurveys
• machine-learning-surveys
• The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
• Deep Learning in Mobile and Wireless Networking: A Survey
• Deep Learning for Sentiment Analysis : A Survey
• Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
• Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
• A Brief Survey of Deep Reinforcement Learning
• A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
• Deep Learning Techniques for Music Generation - A Survey
• Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods
• A Survey on Deep Learning in Medical Image Analysis
• Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey
• Towards Bayesian Deep Learning: A Survey
• Deep Learning for IoT Big Data and Streaming Analytics: A Survey
• Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations
• A Survey on Deep Learning Toolkits and Libraries for Intelligent User Interfaces
• Visual Interpretability for Deep Learning: a Survey
• Deep Learning for Sensor-based Activity Recognition: A Survey
• Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
• A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas
• Deep Face Recognition: A Survey
• Deep Visual Domain Adaptation: A Survey
• Deep Facial Expression Recognition: A Survey
• A Non-Technical Survey on Deep Convolutional Neural Network Architectures
• Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
• Applications of Deep Learning and Reinforcement Learning to Biological Data
• Deep Learning in Neural Networks: An Overview
• Efficient Processing of Deep Neural Networks: A Tutorial and Survey
• How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature
• Universal Reinforcement Learning Algorithms: Survey and Experiments
• Bayesian Reinforcement Learning: A Survey
• Machine Learning and Cloud Computing: Survey of Distributed and SaaS Solutions
• LSTM Benchmarks for Deep Learning Frameworks
• A Survey of Domain Adaptation for Neural Machine Translation
• Video Description: A Survey of Methods, Datasets and Evaluation Metrics
• How convolutional neural network see the world - A survey of convolutional neural network visualization methods
• Mobile Face Tracking: A Survey and Benchmark
• Facial Landmark Detection: a Literature Survey
• From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
• Explainable Recommendation: A Survey and New Perspectives
• Face Recognition Techniques: A Survey
• Hierarchical Temporal Memory using Memristor Networks: A Survey
• The State of the Art in Developing Fuzzy Ontologies: A Survey
• False Information on Web and Social Media: A Survey
• Deep Facial Expression Recognition: A Survey
• Automatic Language Identification in Texts: A Survey
• First Impressions: A Survey on Computer Vision-Based Apparent Personality Trait Analysis
• A Survey on Application of Machine Learning Techniques in Optical Networks
• Detection and Resolution of Rumours in Social Media: A Survey
• A Survey on Multi-View Clustering
• Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
• Deep Learning in Mobile and Wireless Networking: A Survey
• Facial Expression Analysis under Partial Occlusion: A Survey
• A Survey Of Methods For Explaining Black Box Models
• Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
• Online Learning: A Comprehensive Survey
• Automatic differentiation in machine learning: a survey
• A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
• Machine Translation Using Semantic Web Technologies: A Survey
• A Survey of Recent Advances in Texture Representation
• Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
• Survey on Emotional Body Gesture Recognition
• Graph Embedding Techniques, Applications, and Performance: A Survey
• A Survey of Model Compression and Acceleration for Deep Neural Networks
• Spatio-Temporal Data Mining: A Survey of Problems and Methods
• Recommender Systems with Random Walks: A Survey
• Machine Translation Evaluation: A Survey
• A Brief Survey of Deep Reinforcement Learning
• Survey of Recent Advances in Visual Question Answering
• Salient Object Detection: A Survey
• Machine Learning for Survival Analysis: A Survey
• Survey on Models and Techniques for Root-Cause Analysis
• Automated text summarisation and evidence-based medicine: A survey of two domains
• Universal Reinforcement Learning Algorithms: Survey and Experiments
• Emotion in Reinforcement Learning Agents and Robots: A Survey
• Survey of Visual Question Answering: Datasets and Techniques
• Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
• Next Generation Business Intelligence and Analytics: A Survey
• A Survey of Available Corpora for Building Data-Driven Dialogue Systems
• Malicious URL Detection using Machine Learning: A Survey
• SoK: Applying Machine Learning in Security - A Survey
• Content Selection in Data-to-Text Systems: A Survey
• A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder
• Survey on the Use of Typological Information in Natural Language Processing
• Visual Question Answering: A Survey of Methods and Datasets
• Incorporating prior knowledge in medical image segmentation: a survey
• Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey
• A Survey of Semantic Segmentation
• Sentiment Analysis of Twitter Data: A Survey of Techniques
• Computational Sociolinguistics: A Survey
• Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
• A Survey on Object Detection in Optical Remote Sensing Images
• Linear Dimensionality Reduction: Survey, Insights, and Generalizations
• A Survey on Social Media Anomaly Detection
• Survey on the attention based RNN model and its applications in computer vision
• Facial Feature Point Detection: A Comprehensive Survey
• A Survey of Named Entity Recognition in Assamese and other Indian Languages
• Sentiment Analysis: A Survey
• A Survey of Data Mining Techniques for Social Media Analysis
• Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
• Survey on Sparse Coded Features for Content Based Face Image Retrieval
• Natural Language Processing - A Survey
• Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice
• TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation
• Text Detection and Recognition in images: A survey

---

CV Methods

Back to Top

• Image
• Speech
• Video
• Text and optical character
• Pattern
• Audio
• Facial
• Handwriting

Example Applications

• Recognition
  • Shazam
  • Wine
    • Companies
      • Delectable
      • Vivino
  • Facebook photo recognition (highlights faces and suggests friends to tag)
  • Speech/Voice to text (faster to talk than to type acc'g to Stanford)
    • Companies
      • Google Cloud Speech API
  • Text to speech
    • Companies
      • Amazon Polly
  • Video
    • Companies
      • Clarifai
      • Google Cloud Video Intelligence
  • OCR
    • Mobile app check deposits and uploading receipts
    • Post office address recognition
  • Object recognition
    • Companies
      • Pinterest (then used to recommend other pins)
  • Image
    • Companies
      • Clarifai
      • Captricity
      • Google Cloud Vision API
      • Amazon Rekognition
• Computer vision
  • Manufacturing
    • Inspections
    • Quality control
    • Assembly line
  • Visual surveillance
    • Companies
      • BRS Labs AISight
  • Navigation, including autonomous vehicles
    • Land, water, and space
  • Medical image processing and diagnosis
  • Military
    • Detection of enemy solidiers and vehicles
    • Missle guidance
  • Drones
    • Inspection (pipelines), surveillance, exploration (buildings), and protection
    • Companies
      • Digital Signal
      • Shield.ai
  • Item recognition
    • Companies
      • Amazon Go

Related papers

• ImageNet Classification
• Object Detection
• Object Tracking
• Low-Level Vision
  • Super-Resolution
  • Other Applications
• Edge Detection
• Semantic Segmentation
• Visual Attention and Saliency
• Object Recognition
• Human Pose Estimation
• Understanding CNN
• Image and Language
  • Image Captioning
  • Video Captioning
  • Question Answering
• Image Generation
• Other Topics

---

NLP Methods

Back to Top

• Text processing
• Lexical Analysis
• Text Mining
  • Information retrieval
  • Text categorization
  • Text clustering
  • Concept/entity extraction
  • Production of granular taxonomies
  • Sentiment analysis
  • Document summarization
  • Entity relation modeling
  • Named entity recognition
  • Recognition of Pattern Identified Entities
  • Coreference
  • Syntactic parsing
  • Part-of-speech tagging
  • Quantitative text analysis

Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU)

• Syntax
  • Lemmatization
  • Morphological segmentation
  • Part-of-speech tagging
  • Parsing
  • Sentence breaking (also known as sentence boundary disambiguation)
  • Stemming
  • Word segmentation
  • Terminology extraction
• Semantics
  • Lexical semantics
  • Machine translation
  • Named entity recognition (NER)
  • Natural language generation
  • Natural language understanding
  • Optical character recognition (OCR)
  • Question answering
  • Recognizing Textual entailment
  • Relationship extraction
  • Sentiment analysis
  • Topic segmentation and recognition
  • Word sense disambiguation
• Discourse
  • Automatic summarization
  • Coreference resolution
  • Discourse analysis
• Speech
  • Speech recognition
  • Speech segmentation
  • Text-to-speech

Example Applications

• Smart personal assistants
  • Companies
    • Alexa
    • Google Assistant
    • Siri
  • Uses
    • Internet searches and answer questions
    • Set reminders
    • Integrate with your calendar
      • Make appointments
    • Receive sports, news, and finance updates
    • Create to-do lists
    • Order items online
    • Use services (e.g., order an Uber)
    • Play music
    • Play games
    • Smart home integration
• NLG - computer generated reports and news
  • Summarizing documents
  • Story telling
  • Sports recaps
  • Companies
    • Narrative Science
• NLP and language translation
  • Voicemail transcripts
  • eDiscovery
  • Companies
    • Google Natural Language API
    • Google Cloud Translation API
    • Textio for writing optimal job descriptions
• NLU and Chatbots
  • Shopping
  • Errands
  • Day to day tasks
  • Companies
    • x.ai (personal assistant)
    • MindMeld
    • Google Inbox Smart Reply
    • Amazon Lex, includes Automatic speech recognition (ASR)
• Smart instant messaging
  • Companies
    • Google Allo smart messaging app (https://allo.google.com/)
• Text Embeddings
• Thought Vectors
• Machine Translation
• Dialogs and Conversational
• Memory and Attention Models
• Named Entity Recognition
• Natural Language Understanding
• Question Answering and Knowledge Extraction
• Text Summarization
• Text Classification

---

Speech Methods

Back to Top

• Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups [pdf] (Breakthrough in speech recognition)
• Speech recognition with deep recurrent neural networks [pdf] (RNN)
• Towards End-To-End Speech Recognition with Recurrent Neural Networks[pdf]
• Fast and accurate recurrent neural network acoustic models for speech recognition [pdf](Google Speech Recognition System)
• Deep speech 2: End-to-end speech recognition in english and mandarin [pdf] (Baidu Speech Recognition System)
• Achieving Human Parity in Conversational Speech Recognition [pdf] (State-of-the-art in speech recognition, Microsoft)
• Speech Recognition Techniques: A Review - Different Methods Used In Voice Recognition Techniques
• Deep Learning for Speech Recognition
• Deep Learning for Distant Speech Recognition

---

Robotics Methods

Back to Top

• Evolving large-scale neural networks for vision-based reinforcement learning 2013. [pdf] [PPT]
• End-to-end training of deep visuomotor policies (2016): 1-40. [pdf] [PPT] [summary] [PPT] [code]
• Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours (2015). [arxiv] [PPT]
• Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection (2016). [arxiv] [PPT] [iee spectrum news]
• Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning (2016). [arxiv] [PPT] [PPT] [code]
• Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search (2016). [arxiv]
• Deep Reinforcement Learning for Robotic Manipulation (2016). [arxiv]
• Sim-to-Real Robot Learning from Pixels with Progressive Nets (2016). [arxiv] [PPT] [PPT] [[PPT]
• A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
• A Survey on Deep Learning Methods for Robot Vision
• A Survey of Deep Learning Techniques for Mobile Robot Applications
• RGB-D-based Human Motion Recognition with Deep Learning: A Survey
• Deep Reinforcement Learning for Robotic Manipulation-The state of the art
• Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

---

AI Tools

Back to Top

Have a quick look at these threads

• AI tools
• Outline of artificial intelligence and applications
• Outline of machine learning algorithms and applications
• Outline of computer vision and applications
• Outline of natural language processing and tools
• Outline of robotics
• Appications of AI
• Applications of deep learning
• Practical AI
• State-of-the-art-results for AI problems
• kaggle-competitions , kaggle-solutions
• crowdai-challenges
• numer.ai/rounds
• analyticsvidhya-hackathons
• ai-learning-roadmap
• AI-bookmarks
• Wikipedia
• DL4J Deep Learning Use Cases
• Wikipedia Machine Learning Portal
• Wikipedia Artificial Intelligence Portal
• Wikipedia List of Emerging Technologies
• Wikipedia Outline of Statistics
• Wikipedia Statistics Portal
• Wikipedia Outline of Mathemetics
• Wikipedia Mathemetics Portal
• WHO IS READY FOR THE COMING WAVE OF AUTOMATION?
• SAP -digitalistmag
• How Companies Are Putting AI to Work Through Deep Learning
• DisruptionTalk: Everyday List of Artificial Intelligence Examples In Use
• ARTIFICIAL INTELLIGENCE FOR BUSINESS
• ai-privacy

---

Hardware Options Infrastructure for AI

Back to Top

• AI accelerator
• Hardware for Deep Learning. Part 1 - Part 8: CPU, GPU, FPGA, ASIC, Mobile AI, Neuromorphic computing, Quantum computing
• HARDWARE DESIGN FOR MACHINE LEARNING
• Choosing Components for Personal Deep Learning Machine
• stanford: Hardware options for Machine/Deep Learning

• FPGAs and AI processors: DNN and CNN for all
• 12 AI Hardware Startups Building New AI Chips
• Neural Network Accelerator Inference
• Investing in GPUs for AI – AMD GPUs vs NVIDIA GPUs
• Tutorial on Hardware Architectures for Deep Neural Networks - MIT

• Deep Learning Hardware Limbo
• Hardware for Machine Learning
• Hardware for Deep Learning
• Recommended Systems for Machine Learning / AI TensorFlow etc..
• What you need to do deep learning
• Which hardware components (CPU, RAM, GC, etc.) are needed for a machine learning/deep learning home PC/computer to run fast?
• What might another hardware bump mean for ML? What might we see implementing a DNN using MIT’s programmable nanophotonic processor (supposing its past prototype & scaled for conventional accuracy), or IBM’s computational phase-change memory for ML?
• What computer specs (CPU, GPU, memory, etc.) are used by Deep Learning researchers? What is the computer's speed/time performance when training a DL algorithm using a standard dataset such as MNIST handwritten digit dataset?
• Computer Hardware for Machine Learning
• Building a machine learning / deep learning workstation for under $5000
• Build a super fast deep learning machine for under $1,000
• Build your 1st Deep Learning Rig: Step-by-step: How to find, buy, and construct your AI sandbox
• The Future of Machine Learning Hardware
• I: Building a Deep Learning (Dream) Machine
• Enterprise workstations and servers built for Deep Learning
• a-glimpse-into-the-future-of-deep-learning-hardware
• OpenAI: Infrastructure for Deep Learning
• A Guide to Processors for Deep Learning
• Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning
• The Great Debate of AI Architecture
• AI And Machine Learning Drive New SoC Verification Choices
• A closer look at Arm’s machine learning hardware
• An Overview of AI in the HPC Landscape
• HPC and AI – Two Communities Same Future
• HPC and AI: Intertwined Futures
• Picking a GPU for Deep Learning
• Architectures Battle for Deep Learning
• AI Hardware to Support the Artificial Intelligence Software Ecosystem
• Microsoft ‘excited’ about its secret hardware built for artificial intelligence
• OMPUTATIONAL POWER AND THE SOCIAL IMPACT OF ARTIFICIAL INTELLIGENCE
• A Wave of Purpose-Built AI Hardware Is Building
• The future of hardware is AI
• MIT-Born A.I. Hardware Startup Raises $10M Led by Chinese Tech Giant Baidu
• What’s Stopping Google’s Monopoly In AI – Limited Success In Hardware?
• Building the hardware for the next generation of artificial intelligence
• The future of hardware is AI
• Chipmakers Are Racing To Build Hardware For Artificial Intelligence
• AI World: What About Hardware?
• Facebook to open-source AI hardware design
• Forget algorithms. The future of AI is hardware!
• The red-hot AI hardware space gets even hotter with $56M for a startup called SambaNova Systems
• facebook: Introducing Big Basin: Our next-generation AI hardware

---

AI, Cloud, API's and Platforms

Back to Top

Back to Top

• AWS Artificial Intelligence
• AWS SageMaker
• AWS Machine Learning
  • Lex
  • Rekognition
  • Polly
  • Machine Learning
  • AWS Athena - An interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL
• Google Cloud Machine Learning - Machine Learning on any data, of any size
  • Machine Learning Engine
  • Jobs API
  • Video Intelligence API
  • Vision API
  • Speech API
  • Natural Language API
  • Translation API
• Platforms and other
  • Houndify - Add a voice-enabled AI to anything
  • text-processing.com - Natural Language Processing APIs and Python NLTK Demos
  • IBM Watson - Cognitive computing features in your app using IBM Watson's Language, Vision, Speech and Data APIs
  • [Microsoft Machine Learning](Machine Learning) - Powerful cloud based analytics
  • BigML
  • Carto.com
  • RISELab - Real-time Intelligence with Secure Execution
  • Crowdflower - AI for your business: Training data, machine learning and human-in-the-loop in a single platform
• Clarifai
• Recast.ai
• Dialogflow - Formerly API.AI
• IBM Watson
• Networked Insights - Audience.ai
• KnowledgeHound
• WebyClip
• Infer
• AgilOne
• SiteZeus
• Sentient's AI platform
• CamFind
• Tagalys
• Findally
• Persado
• Boomtrain
• Mozilla's Common Voice

Media and blogs

• Google: CLOUD AI
• Azure AI
• IBM: Are you putting cloud and AI to work?
• A Brief Survey of Cloud AI Services
• Cloud Services for Transfer Learning on Deep Neural Networks
• Moving Cloud AI to the Edge
• “There Can be Only One!” Well probably several in the ever changing public cloud and niche provider landscape.
• The Rise Of Artificial Intelligence As A Service In The Public Cloud
• What is the relationship between cloud computing and AI?
• How the AI cloud could produce the richest companies ever
• The combination of artificial intelligence and the cloud
• A machine learning and AI guide for enterprises in the cloud
• Five Data Trends That Will Transform Cloud And AI In 2018
• The future belongs to Cloud and AI
• How AI is transforming cloud computing
• Microsoft Embraces Intelligent Cloud And AI Future; Windows Sidelined
• When the Cloud Is Swamped, It's Edge Computing, AI to the Rescue
• In 2018, can cloud, big data, and AI stand more turmoil?
• Intel Capital pumps $72M into AI, IoT, cloud and silicon startups, $115M invested so far in 2018
• Microsoft is hiring engineers to work on A.I. chip design for its cloud
• Incorporating Google’s AI Principles into Google Cloud
• Prepping for the Oracle AI Cloud: Machine Learning
• How cloud serves as the foundation of AI
• Combining cloud computing and artificial intelligence (AI)
• Microsoft bets on faster chips, AI services to win cloud war
• IBM Launches AI-Powered Enterprise Marketing Cloud Solutions In India
• Microsoft Reorganizes to Fuel Cloud and A.I. Businesses
• The AI-First Cloud: Can artificial intelligence power the next generation of cloud computing?
• SAP Leonardo Machine Learning
• AI applied: How SAP and MapR are adding AI to their platforms
• Oracle Places Huge Bets On AI And Machine Learning To Overtake Salesforce In SaaS
• AI becomes the game changer in the public cloud
• Is the cloud the key to democratizing AI?

---

---

DataMining MachineLearning DeepLearning Applications

Back to Top

Quickly scan and skim below articles

Media and blogs

• Learn to Build a Machine Learning Application from Top Articles of 2017
• What are some interesting possible applications of machine learning?
• Top 9 Machine Learning Applications in Real World
• 9 Applications of Machine Learning from Day-to-Day Life
• 9 Complex Machine Learning Applications That Even A Beginner Can Build Today
• 10 Companies Using Machine Learning in Cool Ways
• Top 10 Industrial Applications of Machine Learning
• 10 Real-World Examples of Machine Learning and AI [2018]
• The Top 10 AI And Machine Learning Use Cases Everyone Should Know About
• Machine Learning Algorithms for Business Applications – Complete Guide
• machine-learning-applications-how-it-works-who-uses-it
• 6 Incredible Machine Learning Applications that will Blow Your Mind
• Best Machine Learning Applications: Ideas for Mobile Apps
• SAS- Machine Learning What it is and why it matters
• Top 15 Deep Learning applications that will rule the world in 2018 and beyond
• 8 Inspirational Applications of Deep Learning
• deep-learning-most-amazing-30-applications
• Quora: What are some applications of deep learning?
• 6 Deep Learning Applications a beginner can build in minutes (using Python)
• Twelve Hot Deep Learning Applications Featured at Deep Learning World
• Applications of Deep Learning
• dl-applications-book
• MIT-review: Deep Learning With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Artificial intelligence is finally getting smart.
• Mathworks: Why Deep Learning Matters
• Deep learning applications and challenges in big data analytics
• Notes from the AI frontier: Applications and value of deep learning
• DL4J: Deep Learning Use Cases
• 14 useful applications of data mining
• Data Mining - Applications & Trends
• Data Mining Applications
• 5 Data mining applications
• Data Mining Applications and Use Cases
• application-of-data-mining
• Concept and Applications of Data Mining
• Rise of Data Mining: Current and Future Application Areas
• Data mining applications in public organizations
• Examples of Data Mining Applications
• Data Mining Applications with R
• Data mining applications in engineering design, manufacturing and logistics
• Data Mining: Future Trends and Applications
• Spatial Information and Data Mining Applications
• arxiv: Data Mining Applications: A comparative Study for Predicting Student's performance
• ibm-top-10-machine-learning-use-cases-part1
• ml-Use cases by industry
• How to Apply Machine Learning to Business Problems
• Machine Learning is Fun!

---

Computer Vision Applications

Back to Top

-> Video surveillance
-> Machine/vehicular object detection/identification/avoidance
-> Medical image analysis
-> Augmented reality (AR)/virtual reality (VR) development
-> Localization and mapping
-> Converting paperwork into digital data
-> Human emotion analysis
-> Ad insertions into images and video
-> Face recognition
-> Real estate development optimization

• The Computer Vision Industry
• Computer Vision: Algorithms and Applications
• Computer Vision: Algorithms and Applications
• Hackernews: Computer Vision: Algorithms and Applications (2010) (szeliski.org)
• CSAIL: Advances in Computer Vision
• Wiki: Applications of computer vision
• Computer Vision Technology Permeates Our Daily Lives
• Quora: What are the applications of computer vision?
• stanford CS: Computer Vision Applications
• The Most Exciting Applications of Computer Vision across Industries
• link.springer: Machine Vision and Applications
• 10 uses of computer vision in marketing & customer experience
• Computer Vision Applications: Home, Office and Industry
• Application of deep learning to computer vision
• Computer Vision in Robotics and Industrial Applications
• Applications of machine vision
• Facial Recognition Applications – Security, Retail, and Beyond
• Facial Recognition
• how-coders-are-fighting-bias-in-facial-recognition-software
• Face-reading AI will be able to detect your politics and IQ, professor says
• High Time to Regulate Face Recognition A.I.
• Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning
• Blocking facial recognition surveillance using AI
• New AI Tech Blinds Computer Facial Recognition Systems
• Microsoft: Face verification
• How is computer vision applied in robotics and the industry?
• important-robotics-links
• Camera Calibration
• CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision
• 3dvision.princeton
• Machine Vision System
• Computer Vision for the Car Industry
• Annotated Computer Vision Bibliography
• Application of Artificial Intelligence Methods to Content-Based Image Retrieval
• Deep Learning for Content-Based Image Retrieval: A Comprehensive Study
• product-ai
• Google AI: Large-Scale Image Retrieval with Attentive Deep Local Features
• Intelligent Image Retrieval Techniques: A Survey
• A Knowledge-based Image Retrieval System Integrating Semantic and Visual Features
• Towards intelligent image retrieval
• Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare
• Bayesian Approaches to Content-based Image Retrieva
• AI-Based CT Image Retrieval--Retrieving Similar Cases with an 85% Accuracy Rate in One-Sixth Diagnostic Time
• How Artificial Intelligence Has Changed Image Recognition Forever
• OCR Applications
• An Overview and Applications of Optical Character Recognition
• Top 5 Optical Character Recognition (OCR) Apps And Software
• What are the possible applications of optical character recognition?
• Application of OCR systems to processing and digitization of paper documents
• IBM: Object detection with AI vision is getting easier for developers
• Implement Object Recognition on Livestream
• Object recognition for free
• Is Google Tensorflow Object Detection API the easiest way to implement image recognition?
• Facebook’s AI team Releases Detectron – A Platform for Object Detection Research
• Interactive Segmentation with Convolutional Neural Networks
• Segmenting and refining images with SharpMask
• Image segmentation with Neural Net
• Object detection: an overview in the age of Deep Learning

---

NLP Applications

Back to Top

• A curated list of speech and natural language processing resources
• 10 Applications of Artificial Neural Networks in Natural Language Processing
• Natural language processing applications
• 7 Applications of Deep Learning for Natural Language Processing
• Applications of Natural Language Processing
• Natural Language Processing – Current Applications and Future Possibilities
• Natural Language Processing – Business Applications
• 4 business applications for natural language processing
• 4 Applications Of Natural Language Processing
• 5 Applications of Natural Language Processing for Businesses in 2018
• nlp-application
• Introduction to Natural Language Processing (NLP)
• Value-of-NLP-applications-varies-for-different-AI-uses
• A few applications of natural language processing…
• Six Natural Language Processing Algorithms for Web Developers
• Google pub: NaturalLanguageProcessing
• What is the Role of Natural Language Processing in Healthcare?
• Commercial Applications of Natural Language Processing
• Clinical information extraction applications: A literature review
• Natural Language Processing Empowered Mobile Computing
• Natural Language Processing (NLP) Applications
• Models of natural language understanding.

---

Speech Recog Applications

Back to Top

• speech-recognition-applications
• Voice Biometrics Month 2016: 4 Unique Applications of Voice Recognition
• Most Common Uses of Voice Recognition Software
• applications of speech recog
• 8 Innovative Ways to Use Speech Recognition for Business
• Overview of speech recognition applications
• Speech Recognition Software
• The Top Five Uses of Speech Recognition Technology
• Applications of Voice-Processing Technology in Telecommunications
• Understanding Speech RecognitionReal-world applications
• AUTOMATIC SPEECH RECOGNITION AND ITS APPLICATION TO INFORMATION EXTRACTION
• 10 best practices for voice-based applications

---

AI in Robotics Applications

Back to Top

• Robotic Application Research: Past, Present, & Future
• Fields of Application
• Robots in Retail – Examples of Real Industry Applications
• Machine Learning in Robotics – 5 Modern Applications
• Top 10 Robotic Applications in the Agricultural Industry
• Top 6 Robotic Applications in Food Manufacturing
• Applications of Robotics in Manufacturing Sector
• 5 Manufacturing Applications for Robotics in 2018
• Introduction to applications in  Robotics
• Development for Industrial Robotics Applications
• Top 6 Robotic Applications in Medicine

---

---

Bigdata and Data Science

Back to Top

---

Desciplines

Back to Top

---

Back to Top

---

Datascience puzzle and AI lock in Loop

Back to Top

---

Data pipeline

Back to Top

so, you have data --> build data pipelines

The Heirarchy

Back to Top

---

Big Data Abstraction

Back to Top

---

Bigdata Tools

Back to Top

---

Datascience Desciplines

Back to Top

---

Architectural Goals, Principles, and Considerations

Back to Top

• Consistency
• Batch (slow/cold) vs. real-time streaming (fast/hot) data processing and paths
  • Slow/cold path - batch processing
    • Batch Processing / Analysis
    • Historical Lookup
    • Auditing
  • Fast/hot path - real-time processing
    • Real-time Analytics / Machine Learning Analysis
    • Real-time Reporting
    • Notifications
• Embedded models or interfaces
• API or RPC or REST
• Deployed trained models (offline learning) vs. online learning

• Latency (near real time)
• Reliability and fault tolerance
• Availability
• Scalability/Volume handling
• Performance/speed
  • Goals and implementation - Oracle
    • Analyze and transform data in real-time
    • Optimize data structures for intended use
    • Use parallel processing
    • Increase hardware and memory
    • Database configuration and operations
    • Dedicate hardware sandboxes
    • Analyze data at rest, in-place
• Throughput
• Extensibility
• Security
• Cost/financial
• Data quality
• Skills availability
• Backup and recovery
• Locations and placement
• Privacy and sensitive data
• Disaster recovery
• Schema on read vs schema on write
  • Bringing the analytical capabilities to the data, VS
  • Bringing the data to the analytical capabilities through staging, extracting, transforming and loading
• Maturity Considerations - Oracle
  • Reference architecture
  • Development patterns
  • Operational processes
  • Governance structures and polices

---

Data types and sources

Back to Top

• Structured
  • Transactions
  • Master and reference
• Unstructured
  • Text
  • Image
  • Video
  • Audio
  • Social
• Semi-structured
  • Machine generated
• Data storage (databases)
• Sensors
• Events
• Parquet
• RFID tags
• Instore WiFi logs
• Machine Logs
  • Application
  • Events
  • Server
  • CDRs
  • Clickstream
• Text, including documents, emails, scanned documents, records, ...
• Social networks
• Public web
• Geo-location/geospatial
• Feeds
• Machine generated
• Clickstream
• Software
• Media
  • Images
  • Video
  • Audio
• Business applications
  • OLTP - Online transaction processing
  • ERP - Enterprise resource planning
  • CRM - Customer relationship management
  • SCM - Supply chain management
  • HR
  • Product/Project management
• Online chat
• Merchant listings
• DMP - Data management platform (advertising/marketing)
• CDR - Call detail records
• Surveys, questionnaires, binary questions, and sentiment
• Billing data
• Product catalog
• Network data
• Subscriber data
• Staffing
• Inventory
• POS and transactional
• eCommerce transactions
• Biometrics
• Mobile devices
• Weather data
• Traffic pattern data
• Mobile devices
• Surveillance

---

Big Data Architecture Patterns

Back to Top

• Polyglot
• Lambda
• Kappa
• IOT-A
  • Message Queue/Stream Processing (MQ/SP) block
    • Buffer data
      • Processing speed
      • Throughput handling of downstream components
      • Micro-batching can increase ingestion rate into downstream components
    • Process and filter data
      • Cleaning and removal
      • Stream processing
        • Continuous queries
        • Aggregates
        • Counts
        • Real-time machine learning/AI
    • Output
      • Real-time
      • Ingest data into downstream blocks (DB and/or DFS)
    • Example technologies
      • Kafka
      • Spark
      • Fluentd
      • Storm
  • Database (DB) block
    • Provides granular, structured, low-latency access to the data
    • Typically NoSQL
      • MongoDB
      • Cassandra
      • HBase
    • Output
      • Interactive ad-hoc querying
        • Data store API (e.g., HBase, MongoDB, ...)
        • Standard SQL interface
    • Example technologies
      • Spark
      • Drill
  • Distributed File System (DFS) block
    • Batch jobs over entire dataset
      • Aggregations
      • Reporting
      • Integration across data sources
        • E.g., with unstructured data
    • Long term storage (archiving)
    • Example technologies
      • Hive
      • Mahout

---

Enterprise Big Data Architectural Components

Back to Top

• Governance
  • Govern data quality
• Operations, Infrastructure, and DevOps
• Monitoring
• Security and privacy
  • Authentication
  • Authorization
  • Accounting
  • Data protection
  • Compliance
• Data Aquisition, Ingestion, and Integration
  • Messaging and message queues
  • ETL/ELT
  • Change data capture
  • FTP
  • API/ODBC
  • Replication
  • Bulk movement
  • Virtualization
  • Analytics types and options on ingestion - Oracle
    • Sensor-based real-time events
    • Near real-time transaction events
    • Real-time analytics
    • Near real time analytics
    • No immediate analytics
• Data Processing
  • Batch and stream processing/computing (velocity)
    • Massive scaling and processing of multiple concurrent input streams
  • Parallel computing platform
    • Clusters or grids
    • Massively parallel processing (MPP)
    • High performance computing (HPC)
  • Options - Oracle
    • Leave it at the point of capture
    • Add minor transformations
    • ETL data to analytical platform
    • Export data to desktops
  • Fast data - Oracle
    • Streams
    • Events
    • Actions
• Data Access
  • Querying
  • Real-time analytics
  • BI analytics
  • MapReduce analytics
• Data Modeling and Structure
  • Star schema
  • Snowflake schema
• Data Analysis, data mining, discovery, simulation, and optimization
  • Advanced analytics and modeling
  • Text and natural language analytics
  • Video and voice analytics
  • Geospatial analytics
  • Data visualization
  • Data mining
  • Where to do analysis - Oracle
    • At ingest – real time evaluation
    • In a raw data reservoir
    • In a discovery lab
    • In a data warehouse/mart
    • In BI reporting tools
    • In the public cloud
    • On premises
  • Data sets
  • Data science
  • Data discovery
  • In-place analytics
  • Faceted analytics
  • SQL analytics
• Data Storage and Management
  • Data lake
  • Data warehouse (volume), aka enterprise information store
    • Centralized, integrated data store
    • Powers BI analytics, reporting, and drives actionable insights
    • Responsible for integrating data
    • Structured, prepared, and stored data optimized for
      • Analytical applications and decision support
      • Querying and reporting
      • Data mining
    • In-database analytics
    • Operational analytics
    • MPP engine
    • 'Deep analytical appliance' - IBM
  • Operational data store (ODS)
  • Database Systems and DBMS
    • Relational (RDBMS)
    • NoSQL
      • Real-time analytics and insights
    • NewSQL
    • Hybrid
  • Data marts
    • Data warehouse extracted data subsets oriented to specific business lines, departments or analytical applications
    • Can be a 'live' data mart
  • File systems (Non-distributed)
  • Distributed file systems (e.g., HDFS) and Hadoop (volume and variety)
    • Real-time and MapReduce analytics and insights
    • Deep analysis of petabytes of structured and unstructured data
  • In-memory
  • Data factory
  • Data Reservoir
  • Dedicated and ad-hoc
    • Discovery labs
    • Sandboxes
• Data lifecycle management
  • Rule-based Data and Policy Tracking
  • Data compression
  • Data archiving
• Deployment Choice
  • On-premise, aka traditional IT
  • In-cloud
    • Public cloud
    • Private cloud
  • Appliance
  • Managed services
• Presentation, Analytics, and Applications (visibility)
  • Browser/web
  • Mobile
  • Desktop
  • Dashboards
  • Reports
  • Notifications and messaging
  • Scorecards
  • Charts and graphics
  • Visualization and discovery
  • Search
  • Alerting
  • EPM and BI applications
  • Recommendations

---

Back to Top

Back to Top

---

AWS

Back to Top

• Solution Development Guides
  • Reference Architectures
    • Web Application Hosting
    • Batch Processing
    • Large Scale Computing and Large Data Sets
    • Time Series Processing
  • By Application
    • Websites
    • Backup and Recovery
    • Archiving
    • DevOps
    • Big Data
    • High Performance Computing
    • Internet of Things
    • Business Applications
    • Content Delivery
    • Mobile Services
    • Scientific Computing
    • E-commerce
  • By Industry Sector
    • Financial Services
    • Digital Marketing
    • Media and Entertainment
    • Gaming
    • Enterprise IT
    • Healthcare & Life Sciences
    • Government
    • Nonprofit
    • Education
• Partner Solutions
  • Big Data
  • Storage
  • DevOps
• Case Studies
  • Analytics
  • Big Data
  • Enterprise
  • Startups
  • Web Apps

---

Google Cloud Platform

Back to Top

• Big data reference architecture diagram
• Solution Development Guides
  • Media
  • Mobile Applications
  • Big Data
  • Financial Services
  • Gaming
  • Retail & Commerce
  • Internet of Things
  • Websites and Web Apps
  • Development & Test

By Technology

Back to Top

• Apache Storm
• Apache Spark
• Apache Mahout

---

Oracle Architecture and Patterns Examples

Back to Top

• Designed to be a flexible and a “just-in-time” architecture development approach
• Key Steps
  • Establish Business Context and Scope
  • Establish an Architecture Vision
  • Assess the Current State
  • Establish Future State and Economic Model
  • Develop a Strategic Roadmap
  • Establish Governance over the Architecture

---

IBM Architecture and Patterns Examples

Back to Top

Key Functionalities

• Data ingestion
  • Optimize the process of loading data in the data store to support time-sensitive analytic goals.
• Search and survey
  • Secure federated navigation and discovery across all enterprise content.
• Data transformation
  • Convert data values from source system and format to destination system and format.
• Analytics
  • Discover and communicate meaningful patterns in data.
• Actionable decisions
  • Make repeatable, real-time decisions about organizational policies and business rules.
• Discover and explore
  • Discover, navigate, and visualize vast amounts of structured and unstructured information across many enterprise systems and data repositories.
• Reporting, dashboards, and visualizations
  • Provide reports, analysis, dashboards, and scorecards to help support the way that people think and work.
• Provisioning
  • Deploy and orchestrate on-premises and off-premises components of a big data ecosystem.
• Monitoring and service management
  • Conduct end-to-end monitoring of services in the data center and the underlying infrastructure.
• Security and trust
  • Detect, prevent, and otherwise address system breaches in the big data ecosystem.
• Collaborate and share

Solution Patterns - IBM

• Landing Zone Warehouse
• Virtual Tables
• Discovery Tables
• Streams Dynamic Warehouse
• Streams Detail with Update
• Direct Augmentation
• Warehouse Augmentation
• Streams Augmentation
• Dynamic Search Cube

Component Patterns - IBM

• Source Data
• Source Event
• Landing Area Zone ETL
  • Extract
  • Normalize
  • Clean
• Landing Area Zone Search and Survey
  • Find
  • Filter
  • Extract
• Landing Area Zone Stream Filter
• Landing Area Zone Stream Augmentation
• Landing Area Zone Warehouse Augmentation
• Landing Area Zone Index
• Exploration Mart
• Analytics Mart
• Report Mart
• Virtual Report Mart
• Virtual Search Mart
• Predictive Analytics

---

Big Data Exploration Example Architecture IBM

Back to Top

• Applications layer
  • Consists of
    • Visualization
    • Discovery
    • Analysis
    • Reporting
    • Statistics
    • Text and entity analytics
  • Access
    • SQL
    • MDX
    • Search
    • REST
• Discovery and assembly layer
  • Consists of
    • Virtual search mart
      • Faceted search
    • Analytics mart
      • Report mart
      • Discovery table
      • Search and survey
    • Report mart
      • ETL
      • Analytics
      • Streams
  • Access
    • NoSQL
    • SQL
    • Search
    • REST
• Landing layer
  • Consists of
    • Shared warehouse and ETL
      • Extract
      • Provision
  • Access
    • Search
    • REST
    • SQL
    • Files
• Source layer
  • Sensors and telemetry
  • Internet
  • Social media
  • Public data
  • Enterprise data

---

Big data and analytics architecture on cloud IBM

Back to Top

• Analytics-as-a-service
  • Consumes both data at rest and in motion
  • Applies analytical algorithms
  • Provides
    • Dashboards
    • Reports
    • Visualizations
    • Insights
    • Predictive modeling
  • Abstracts away all complexity of data collection, storage, and cleansing
• Data-as-a-service
  • Data-at-rest-service
  • Data-in-motion-service
• NoSQL tools (Hive, Pig, BigSQL, ...)
• EMR clusters (Hadoop, Cassandra, MongoDB, ...) and Traditional DW
• Big data file system (HDFS, CFS, GPFS, S3, ...)
• Infrastructure & Appliances (Baremetal or IaaS) and object storage

---

---

Bigdata, AI, Datascience Reads

Back to Top

Advanced Analytics

• Gartner Advanced Analytics Glossery
• The 4 Types of Data Analytics

---

Artificial Intelligence

Back to Top

• AI and Deep Learning in 2017 – A Year in Review
• 59 impressive things artificial intelligence can do today
• MIT - The Missing Link of Artificial Intelligence
• The AI Revolution: The Road to Superintelligence
• Artificial General Intelligence – The Holy Grail of AI
• The Non-Technical Guide to Machine Learning & Artificial Intelligence
• THE NEURAL NETWORK ZOO
• Part of Speech Tags
• The 7 Myths of AI
• AI 100: The Artificial Intelligence Startups Redefining Industries
• Sensors, Plus Brains: 17 IoT Companies Using Artificial Intelligence Tech
• Meet the 2017 CNBC Disruptor 50 companies
• MIT - The Missing Link of Artificial Intelligence
• Everyday Examples of Artificial Intelligence and Machine Learning
• What is Artificial Intelligence? An Informed Definition
• Meet the 2017 CNBC Disruptor 50 companies
• Company-specific
  • Q&A: Uber’s machine learning chief says pattern-finding computing fuels ride-hailing giant
  • Netflix - Recommending for the World
  • Netflix Recommendations: Beyond the 5 stars (Part 1)
  • Amazon Help - About Recommendations
• Video: The Promise of AI
• My Curated List of AI and Machine Learning Resources from Around the Web
• Here are 50 Companies Leading the AI Revolution
• The Race For AI: Google, Baidu, Intel, Apple In A Rush To Grab Artificial Intelligence Startups
• Emerging AI: 7 Industries Including Law, HR, Travel And Media Where AI Is Making An Impact
• The Basics of Artificial Intelligence Decoded
• Demystifying Deep Reinforcement Learning
• Applying deep learning to real-world problems
• Top AI Influencers
• Introduction to reinforcement learning and OpenAI Gym

---

Machine Learning

Back to Top

• Basic Concepts in Machine Learning
• Choosing a Machine Learning Classifier
• The 10 Algorithms Machine Learning Engineers Need to Know
• What are the pros and cons of offline vs. online learning?
• Introduction to Online Machine Learning : Simplified
• Machine Learning From Streaming Data: Two Problems, Two Solutions, Two Concerns, and Two Lessons
• Rules of Machine Learning: Best Practices for ML Engineering
• [Which machine learning algorithm should I use?](http://blogs.sas.com/content/subconsciousmusings/2017/0 4/12/machine-learning-algorithm-use/)
• How to choose algorithms for Microsoft Azure Machine Learning
• Automated Machine Learning — A Paradigm Shift That Accelerates Data Scientist Productivity @ Airbnb
• 8 Ways Machine Learning Is Improving Companies’ Work Processes

---

Data Science

Back to Top

• 13 Great Data Science Infographics
• THE DATA SCIENCE VENN DIAGRAM
• Common Probability Distributions: The Data Scientist’s Crib Sheet
• The Guide to Learning Python for Data Science
• The Periodic Table of Data Science
  • The Periodic Table of Data Science PNG
• 10 Free Must-Read Books for Machine Learning and Data Science
• The 10 Statistical Techniques Data Scientists Need to Master
• The Startup Founder’s Guide to Analytics
• 40 Techniques Used by Data Scientists
• Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated
• 7 awesome data science newsletters to keep you informed
• Teaching the data science process

---

Data Scientists

Back to Top

• How to Consistently Hire Remarkable Data Scientists
• Battle of the Data Science Venn Diagrams
• Hiring a data scientist
• Top mistakes data scientists make
• Nurturing a productive data team
• What makes a great data scientist?
• How to think like a data scientist to become one
• The Life of a Data Scientist
• Data Science Career Paths: Different Roles in the Industry
• The Data Science Industry: Who Does What (Infographic)
• Data Science Falls Into Many Roles
• What’s the Difference Between Data Science Roles?
• Doing Data Science Right — Your Most Common Questions Answered
• Top 12 Interesting Careers to Explore in Big Data
• How to Job Interview a Data Scientist
• 9 Mistakes to Avoid When Starting Your Career in Data Science
• Is data scientist the most rewarding tech job? New report says yes
• Getting Into Data Science: What You Need to Know

---

Statistics, Probability, and Mathematics

Back to Top

• Descriptive Statistics Key Terms, Explained

---

Internet of Things (IoT)

Back to Top

• The Internet of Things Market

---

Jobs, Skills, and Salary Trends

Back to Top

• The 3 Highest Paid Jobs in Tech (and 17 Skills That Get You Hired)
• 2017 State of Global Tech Salaries
• The State of Data Science and Machine Learning 2017
• Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent
• What’s next for the 2017 data science and analytics job market?
• Defining Baseline Skills
• Top 12 Interesting Careers to Explore in Big Data

---

Tools and Language Popularity, Comparisons, Trends, ...

Back to Top

• Tiobe Index for Programming Languages
• A list of artificial intelligence tools you can use today
• Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results
• What Big Data, Data Science, Deep Learning software goes together?
• Python Deep Learning Frameworks Reviewed
• Best machine learning packages in R
• DL4J vs. Torch vs. Theano vs. Caffe vs. TensorFlow
• 10+ Machine Learning as a Service Platforms
• Python, Machine Learning, and Language Wars
• All the best big data tools and how to use them
• The big data ecosystem for science
• Firing on All Cylinders: The 2017 Big Data Landscape
  • The 2017 Big Data Landscape PNG
• Four Ways to Extend Jupyter Notebook
• Making Publication Ready Python Notebooks
• Stack Overflow - Developer Hiring Trends in 2017
• Top 15 Python Libraries for Data Science in 2017
• Top 15 Frameworks for Machine Learning Experts
• Github stats for Python data science libraries - April, 2017
• Architecture of Giants: Data Stacks at Facebook, Netflix, Airbnb, and Pinterest
• 10 Tools for the Novice Data Scientist

---

Freelancing and Consulting

Back to Top

• Infographic: How to Calculate Your Freelance Hourly Rate
• Freelancer 101: How to Set Up a Simple Accounting System
• New to Freelancing? 9 Things You Need to Know
• How to Become a Freelance Data Scientist

---

Online Learning

Back to Top

• Course Report
• CourseTalk
• Online Course Report
• Online Course Report - The 50 Most Popular MOOCs of All Time
• Data Science Course Rankings - Opinion Article
• Every Intro to Data Science Course on the Internet, Ranked
• Machine learning courses online
• Data Science Online Courses: A Comprehensive List
• Best Data Science Online Courses
• Online Education in Analytics, Big Data, Data Science, Machine Learning
• Data Science Academy - Free Data Science Courses
• New Online Data Science Tracks for 2017
• Data Science Online Courses: A Comprehensive List for 2017
• TOP 50 DATA SCIENCE RESOURCES: THE BEST BLOGS, FORUMS, VIDEOS AND TUTORIALS TO LEARN ALL ABOUT DATA SCIENCE
• 18 Resources to Learn Data Science Online

---

Big Data and Data Engineering

Back to Top

• Architectural Patterns for Near Real-Time Data Processing with Apache Hadoop
• Real-Time Stream Processing as Game Changer in a Big Data World with Hadoop and Data Warehouse
• Real-time Data Processing in AWS Cloud
• How much data does one need to efficiently use Hadoop instead of SQL?
• Don't use Hadoop - your data isn't that big
• Batch vs. Real Time Data Processing
• Data Lakes: Hadoop Vs. In-Memory Databases
• Data processing architectures – Lambda and Kappa
• Getting started: the 3 stages of data infrastructure
• Data Pipelines in Hadoop
• 10 Data Acquisition Strategies for Startups
• Getting started: the 3 stages of data infrastructure

---

Databases, Schemas, and Data Modeling

Back to Top

• Analytics 101: Choosing the right database
• DB-Engines Ranking
• Data Warehouse vs. OLAP Cube
• Stack Overflow - Data Warehouse vs. OLAP Cube?
• MongoDB and HBase Compared
• Dimensional modeling
• The 10 Essential Rules of Dimensional Modeling
• The importance of a date dimension in a data warehouse and BI project
• How to Choose the Right Database System: RDBMS vs. NoSql vs. NewSQL
• DAO vs ORM vs ActiveRecord vs TableGateway vs AHHHH!
• SQL is 43 years old - here’s 8 reasons we still use it today

---

Product

Back to Top

• The Product Management Hierarchy of Needs
• What are the best product management and/or product marketing training programs?
• How product marketing helps build product
• WHAT MAKES A GREAT PRODUCT MANAGER
• Books for Product Managers

---

Methodologies

Back to Top

• Transitioning from Scrum to Kanban
• Running in Circles - Why Agile Isn’t Working and What We Do Differently

---

Computer Science and Programming

Back to Top

• Programming languages
  • What is SQL and why is it important?
• Suffering-oriented programming
• Stack Overflow Developer Survey Results 2017

---

Business

Back to Top

• GitLab team handbook
• Meet the 2017 CNBC Disruptor 50 companies

---

Gartner Magic Quadrants

Back to Top

• Magic Quadrant for Business Intelligence and Analytics Platforms
• Magic Quadrant for Cloud Infrastructure as a Service, Worldwide
• Magic Quadrant for Advanced Analytics Platforms
  • Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: gainers and losers
  • Gartner 2017 Magic Quadrant for Advanced Analytics Platforms: gainers and losers
• Magic Quadrant for Data Integration

---

Architectures

Back to Top

• Lambda Architecture
• AWS Architecture Center
• AWS Big Data Partner Solutions
• GCP Architecture
• Introduction to big data classification and architecture
• An Enterprise Architect’s Guide to Big Data
• BIG DATA REFERENCE ARCHITECTURE
• Getting Started with Big Data Architecture
• BIG DATA: Architectures and Technologies
• Big Data Architecture
• Big Data Analytics Architecture

---

References

Back to Top

|| Big Data | Public Datasets | Hadoop | Data Engineering | Streaming | Apache Spark | Databases | MySQL | SQLAlchemy | InfluxDB | Neo4j | MongoDB | RethinkDB | TinkerPop | PostgreSQL | CouchDB | HBase | datascience | data-science-viz | data-science-ipython-notebooks | PythonDataScienceHandbook | go-ds| data-science-blogs | data-science | DataSciencePython | courses| data-science-from-scratch | spark-notebook | LearnDataScience | data-science-at-the-command-line | Data-Science-45min-Intros | DataScienceResources | cookiecutter-data-science | DataScienceR | awesome-time-series-database | bigdata-ecosystem

|| cheatsheets-ai | Learn Data Science open resources | List of Data Science/Big Data Resources | ISLR-python | Evaluation of Deep Learning Frameworks | Data Science Resources | Data science blogs | Machine learning algorithms | Machine Learning for Software Engineers | Microsoft Team Data Science Process Repository | Open Source Data Science Masters | The Field Guide to Data Science (Booz, Allen, Hamilton) | Amazon Web Services — a practical guide | Minimal and clean examples of machine learning algorithms | Machine Learning From Scratch ||

|| Awesome R | Awesome Data Science | Awesome Deep Learning | Awesome Python | Awesome Scala | Awesome Machine Learning | Awesome IoT | Awesome AWS | Awesome Cheatsheet | Awesome linux Software | Awesome Math ||

|| A Whirlwind Tour of Python | Scikit-learn Tutorial | theano-tutorial | IPython Theano Tutorials | Machine Learning & Deep Learning Tutorials | Python Data Science Tutorials | TensorFlow-World | GitHub-powered Jupyter nbviewer | A gallery of interesting Jupyter Notebooks | DeepSchool.io - Deep Learning tutorials in jupyter notebooks | Jupyter kernels | Data science IPython notebooks | machine_learning | Statistical Data Analysis in Python | ipython-notebooks | Spark Notebook | Statsmodels examples | Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python | Python Machine Learning book resources | Python Machine Learning book FAQ | Learning-Predictive-Analytics-with-R | Data Science from Scratch book resources | IPython Cookbook materials | Python Data Science Handbook Supplemental Materials | Hadoop Application Architectures | Advanced Analytics with Spark | Think Stats | Think Bayes | Think Python | Harvard's CS109 Data Science | General Assembly's Data Science course materials | Data Science Specialization resources | Data Science Specialization notes ||

---

Online Visualization

Back to Top

• The Python Graph Gallery
• The Data Visualisation Catalogue
• Python Plotting for EDA
• dataviz.tools
• Tableau Public Gallery
• D3 Gallery
• D3 List Gallery
• Google Charts Gallery
• Matplotlib Gallery
• Seaborn Gallery
• Bokeh Gallery
• Power BI Custom Visuals
• Shiny Gallery
• The R Graph Gallery
• ggplot 2
• Zingchart Gallery
• Visual Complexity
• Kantar Information is Beautiful Awards
• New York Times
• Visually Staff Picks
• Life Universe
• Visualgo
• See, also
• Esri Maps
• Carto Maps
• MobileVis
• Visualizing Data Resources
• Top Algorithms and Methods Used by Data Scientists

---

What would you like to show

Back to Top

---

Cheatsheets

Back to Top

Python

| Pandas Cheat Sheet - Python for Data Science | NumPy Cheat Sheet - Python for Data Science | Bokeh Cheat Sheet - DataCamp | PySpark Cheat Sheet: Spark in Python | Python For Data Science Cheat Sheet - Pandas Basic | Python 3 Cheat Sheet | Python For Data Science Cheat Sheet - Python Basics |

R

| RStudio Cheat Sheet Collection | R Reference Card for Data Mining | Data Science Resources : Cheat Sheets |

Data Science and Machine Learning

| Machine learning algorithm cheat sheet | 11 Steps for Data Exploration in R | Data Science Cheat Sheet | Machine Learning Periodic Table | Guide to Data Science Cheat Sheets | 50+ Data Science and Machine Learning Cheat Sheets, Updated | Comparing Supervised Learning Algorithms - Google Doc Spreadsheet | Essential Cheat Sheets for Machine Learning and Deep Learning Engineers | Scikit-Learn Cheat Sheet: Python Machine Learning |

Artificial Intelligence

| AI Cheat Sheet | Penn Treebank POS Tags | Brown Corpus |

Statistics and Mathematics

| MIT Statistics Cheat Sheet | ALL IN ONE MATHEMATICS CHEAT SHEET |

Software, Packages, and Libraries

| The Data Stack | bigdata-2016 | Big Data’s Leadership & Development

Databases and querying languages

| MongoDB - Cheat Sheet |

Shortcuts

| Jupyter Notebook Keyboard Shortcuts|

Markup and Syntax

| GitHub markdown cheatsheet | GitHub markdown guide |

Back to Top

---

Python for Bigdata

Back to Top

---

R for Bigdata

Back to Top

---

Open Datasets

Back to Top

• Awesome Public Datasets
• UCI Machine Learning Repository
• sklearn.datasets
• Rdatasets list
• 17 places to find datasets for data science projects
• Machine Learning Data Set Repository
• The Greatest Public Datasets for AI
• AWS Public Datasets Home
• AWS Public Datasets
• 100+ Interesting Data Sets for Statistics
• Kaggle Datasets
• FiveThirtyEight data
• Google BigQuery Public Datasets
• Stanford Large Network Dataset Collection
• THE MNIST DATABASE of handwritten digits
• THE Wikipedia Corpus
• Deep learning datasets
• Open Data for Deep Learning
• Datasets for Data Mining and Data Science
• Wikipedia List of datasets for machine learning research
• 100+ Interesting Data Sets for Statistics
• NYC OpenData
• Data.gov
• FiveThirtyEight datasets and code
• data.world
• Great IoT, Sensor and other Data Sets Repositories
• Quora - Where can I find large datasets open to the public?
• Subreddit
• AI and Deep Learning in 2017 – A Year in Review - Datasets section
• 70 Amazing Free Data Sources You Should Know

Dataset By Category

• Streaming Open Data
  • Satori - Live streaming open data
• Classification
  • http://archive.ics.uci.edu/ml/datasets/Wine+Quality
  • http://archive.ics.uci.edu/ml/datasets/Wine
  • http://archive.ics.uci.edu/ml/datasets/User+Knowledge+Modeling
  • http://archive.ics.uci.edu/ml/datasets/Student+Performance
  • http://archive.ics.uci.edu/ml/datasets/seeds
  • http://archive.ics.uci.edu/ml/datasets/Iris
  • http://archive.ics.uci.edu/ml/datasets/Dow+Jones+Index
  • http://archive.ics.uci.edu/ml/datasets/BLOGGER
• Clustering
  • http://archive.ics.uci.edu/ml/datasets/User+Knowledge+Modeling
  • http://archive.ics.uci.edu/ml/datasets/Sponge
  • http://archive.ics.uci.edu/ml/datasets/seeds
  • http://archive.ics.uci.edu/ml/datasets/Dow+Jones+Index
• Recommendender
  • http://grouplens.org/datasets/movielens/
  • http://www2.informatik.uni-freiburg.de/~cziegler/BX/
  • http://grouplens.org/datasets/hetrec-2011/
  • https://en.wikipedia.org/wiki/Wikipedia:Database_download#English-language_Wikipedia
  • http://planet.openstreetmap.org/planet/full-history/
  • http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
  • http://www.cs.cornell.edu/people/pabo/movie-review-data/
  • http://archive.ics.uci.edu/ml/datasets/Entree+Chicago+Recommendation+Data
• Regression
  • http://archive.ics.uci.edu/ml/datasets/Wine+Quality
  • http://archive.ics.uci.edu/ml/datasets/Student+Performance
  • http://archive.ics.uci.edu/ml/datasets/Automobile
  • http://archive.ics.uci.edu/ml/datasets/Auto+MPG
• Computer Vision and Recognition
  • http://www.cvpapers.com/datasets.html
  • http://www.image-net.org/
• IoT

Specific Data Sets

• 3 Million Instacart Orders, Open Sourced

Data Portals and Meta portals

• DataPortals.org
• Open Data Monitor
• Quandl

Data Marketplaces and Monetization

• Dawex

---

---

AI MTDB

Back to Top

Movies

|| 2001: A Space Odyssey | A.I. Artificial Intelligence | Automata | Blade Runner | Chappie | Ex Machina | Her | I, Robot | Prometheus | The Terminator series | Transcendence | The Matrix Trilogy | WarGames | Chappie (2015) | Metropolis (1927) | Star Wars series | The Hitchhiker's Guide to the Galaxy (2005) | Avengers series | Automata (2014) | Stealth (2005) | Enthiran (2010) | TRON: Legacy (2010) | The Creation of the Humanoids (1962) ||

TV Shows

Back to Top

|| Black Mirror | Intelligence | Minority Report | Almost Human | Battlestar Galactica | Caprica | Numb3rs | Humans | Person of Interest | Small Wonder | Mr. Robot | Total Recall 2070 | Westworld | Terminator: The Sarah Connor Chronicles ||

Documentaries

Back to Top

|| The Rise of AI Deep Learning - Documentary 2018 HD | Ray Kurzweil - The Singularity Is Near | Lo and Behold, Reveries of the Connected World (2016) | AlphaGo | Revolutionaries: Artificial Intelligence | Road to AI | Artificial Intelligence and Robotics | Singularity Or Bust | The Smartest Machine on Earth | Humans Need not Apply | Technocalyps | Hans Rosling's 200 Countries, 200 Years, 4 Minutes - The Joy of Stats - BBC Four | Future Intelligence | Hans Rosling: Let my dataset change your mindset | BBC Documentaries 2016: The Joy of Data | Science Documentary 2016 | Big Data | Dangers of artificial intelligence documentary (2018) | BBC Documentary 2018 Artificial Intelligence | Great Debate - Artificial Intelligence: Who is in control? (OFFICIAL) | Future World 2030: Dr Michio Kaku's predictions. Documentary 2018 | The World In 2050 | Deep Learning: Intelligence from Big Data | How to Become a Data Scientist | The Future of AI: from Deep Learning to Deep Understanding, Ben Goertzel | Ted- Artificial intelligence ||

Books

• Life 3.0: Being Human in the Age of Artificial Intelligence
• Our Final Invention: Artificial Intelligence and the End of the Human Era
• How to Create a Mind: The Secret of Human Thought Revealed
• The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
• The Singularity is Near: When Humans Transcend Biology
• Rise of the Robots
• The Emperor's New Mind: Concerning Computers, Minds and the Laws of Physics
• The Society of Mind
• The Age of Spiritual Machines: When Computers Exceed Human Intelligence
• The Economic Singularity: Artificial Intelligence and the Death of Capitalism
• The Diamond Age: Or, A Young Lady's Illustrated Primer
• I Have No Mouth and I Must Scream
• Accelerando
• Superintelligence: Paths, Dangers, Strategies
• Phantoms in the Brain: Probing the Mysteries of the Human Mind
• On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines
• AI-book-list
• Popular Artificial Intelligence Books

---

Principles and Rules

Back to Top

• Principle of least priviledge - Requires that in a particular abstraction layer of a computing environment, every module (such as a process, a user, or a program, depending on the subject) must be able to access only the information and resources that are necessary for its legitimate purpose
• Peter principle - The selection of a candidate for a position is based on the candidate's performance in their current role, rather than on abilities relevant to the intended role
  • Employees only stop being promoted once they can no longer perform effectively, and "managers rise to the level of their incompetence."
• Pareto principle
  • 20% of the features will account for 80% of the value
  • 20% of the work will produce 80% of the value
• Ninety/Ninety Rule
  • The first 90 percent of the code accounts for the first 90 percent of the development time. The remaining 10 percent of the code accounts for the other 90 percent of the development time. (180% of time)
• SUCCESs - Made to stick principles
  • Simplicity
  • Unexpectedness
  • Concreteness
  • Credibility
  • Emotions
  • Stories
• Build, measure, learn
  • Maximize learning through incremental and iterative engineering
  • Build relates to MVP, ie, the simplest product to show customers to get most learning at that time
  • Goal is always to maximize learning and not build fully featured beta/prototype
  • Lean startup version: hypothesis, design experiments, test, insight

Laws

• Parkinson's law of triviality - Members of an organisation give disproportionate weight to trivial issues
• Parkinson's law - Work expands so as to fill the time available for its completion
• Brooks's law - Adding manpower to a late software project makes it later
• Hofstadter's law - It always takes longer than you expect, even when you take into account Hofstadter's Law
• Little's law - The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW.

Quotes, Idioms, and Sayings

• Nothing is more permanent than a temporary solution
• The world doesn't need wrong answers in record time
• Work on the business, not in the business
• Fail to plan is to plan to fail
• Silence equals agreement
• Deliver results rather than excuses
• Don't prove own importance by vetoing good ideas and road-blocking productive work
• Accept total accountability and make it happen
• When making decisions, slower ultimate success is better than rapid permanent failure
• YAGNI (from XP) - You aren’t gonna need it
  • "Always implement things when you actually need them, never when you just foresee that you need them."
• DTSTTCPW (from XP) - "do the simplest thing that could possibly work"
• Skip blame and complain game and get things done despite major obstacles. Victim mentality is the kiss of death.
• KISS - Keep it simple, stupid
• A good plan implemented today is better than a perfect plan implemented tomorrow
• MLP - minimum lovable product
• A problem without a solution is a complaint
• Working on the right thing is as—if not more important—than how hard you are working
• You can’t have five North Stars, you can’t have five most important goals…
• Prioritize goals that are ‘critical’ ahead of goals that are ‘beneficial

General terms, models, acronymns, and concepts

Back to Top

• Priorities for business (in order)
  • People
  • Products
  • Profits
• Iron triangle success
  • Build MVP based on perceived value and market research with associated scope and let that choose the time and resources required
• GRIT
  • Generosity
  • Respect
  • Integrity
    • Accountability
  • Truth
• Give credit, don't take it
• The most effective way to solve any problem is to put together all of the people with the skills required to solve it, i.e., a cross-functional or multi-disciplinary team
• A startup is a temporary organization designed to search for a repeatable and scalable business model
• When people don’t take responsibility
  • Reputations decline
  • Timelines are extended
  • Performance falls short
  • Frustration shoots up
  • Goals are adapted
  • Accusation and blame escalates
  • Gossip soars. “What’s up with Fred? He never get’s things done on time.
  • Excuses abound. Irresponsible people give “good” reason for irresponsibility.
  • Higher ups complain
  • Stress increase
• Curse of knowledge
  • The curse of knowledge is a cognitive bias that leads better-informed parties to find