Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
7,685
101 commits
updated Mar 14, 2024
From Zero to Research Scientist full resources guide. 
This guide is designated to anybody with basic programming knowledge or a computer science background interested in becoming a Research Scientist with 🎯 on Deep Learning and NLP.
You can go Bottom-Up or Top-Down both works well and it is actually crucial to know which approach suites you the best. If you are okay with studying lots of mathematical concepts without application then use Bottom-Up. If you want to go hands-on first then use the Top-Down first.
The Mathematical Foundation part is for all Artificial Intelligence branches such as Machine Learning, Reinforcement Learning, Computer Vision and so on. AI is heavily math-theory based so a solid foundation is essential.
This branch of Math is crucial for understanding the mechanism of Neural Networks which are the norm for NLP methodologies in nowadays State-of-The-Art.
Most of Natural Language Processing and Machine Learning Algorithms are based on Probability theory. So this branch is extremely important for grasping how old methods work.
Considered a fancy name for Statistical models where its main goal is to learn from data for several usages. It is considered highly recommended to master these statistical techniques before Research as most of research is inspired by most of the Algorithms.
One of the major breakthroughs in the field of intersection between Artificial Intelligence and Computer Science. It lead to countless advances in technology and considered the standard way to do Artificial Intelligence.
It is a sub-field of AI which focuses on learning by observation/rewards.
It is a sub-field of AI which focuses on the interpretation of Human Language.
In this section, I am going to list the most influential papers that help people who want to dig deeper into the research world of NLP to catch up.
| Paper | Comment |
|---|
(top 24 of 25)
119 followers · starred Apr 2021
67 followers · starred Feb 2023
102 followers · starred Feb 2023
75 followers · starred Mar 2023
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
7,685
101 commits
updated Mar 14, 2024
From Zero to Research Scientist full resources guide.
This guide is designated to anybody with basic programming knowledge or a computer science background interested in becoming a Research Scientist with 🎯 on Deep Learning and NLP.
You can go Bottom-Up or Top-Down both works well and it is actually crucial to know which approach suites you the best. If you are okay with studying lots of mathematical concepts without application then use Bottom-Up. If you want to go hands-on first then use the Top-Down first.
The Mathematical Foundation part is for all Artificial Intelligence branches such as Machine Learning, Reinforcement Learning, Computer Vision and so on. AI is heavily math-theory based so a solid foundation is essential.
This branch of Math is crucial for understanding the mechanism of Neural Networks which are the norm for NLP methodologies in nowadays State-of-The-Art.
Most of Natural Language Processing and Machine Learning Algorithms are based on Probability theory. So this branch is extremely important for grasping how old methods work.
Considered a fancy name for Statistical models where its main goal is to learn from data for several usages. It is considered highly recommended to master these statistical techniques before Research as most of research is inspired by most of the Algorithms.
One of the major breakthroughs in the field of intersection between Artificial Intelligence and Computer Science. It lead to countless advances in technology and considered the standard way to do Artificial Intelligence.
It is a sub-field of AI which focuses on learning by observation/rewards.
It is a sub-field of AI which focuses on the interpretation of Human Language.
In this section, I am going to list the most influential papers that help people who want to dig deeper into the research world of NLP to catch up.
| Paper | Comment |
|---|
(top 24 of 25)
119 followers · starred Apr 2021
67 followers · starred Feb 2023
102 followers · starred Feb 2023
75 followers · starred Mar 2023
