Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Tensorflow | 175,358 | 327 | 77 | 17 hours ago | 46 | October 23, 2019 | 2,145 | apache-2.0 | C++ | |

An Open Source Machine Learning Framework for Everyone | ||||||||||

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🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. | ||||||||||

Pytorch | 67,645 | 146 | 16 hours ago | 23 | August 10, 2022 | 12,240 | other | Python | ||

Tensors and Dynamic neural networks in Python with strong GPU acceleration | ||||||||||

Keras | 58,548 | 330 | a day ago | 68 | May 13, 2022 | 387 | apache-2.0 | Python | ||

Deep Learning for humans | ||||||||||

Cs Video Courses | 56,273 | 8 days ago | 17 | |||||||

List of Computer Science courses with video lectures. | ||||||||||

Faceswap | 45,645 | 2 days ago | 27 | gpl-3.0 | Python | |||||

Deepfakes Software For All | ||||||||||

D2l Zh | 44,160 | 1 | 3 days ago | 45 | March 25, 2022 | 34 | apache-2.0 | Python | ||

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60多个国家的400多所大学用于教学。 | ||||||||||

Tensorflow Examples | 42,312 | 8 months ago | 218 | other | Jupyter Notebook | |||||

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2) | ||||||||||

100 Days Of Ml Code | 40,344 | 3 months ago | 61 | mit | ||||||

100 Days of ML Coding | ||||||||||

Deepfacelab | 40,302 | 3 days ago | 534 | gpl-3.0 | Python | |||||

DeepFaceLab is the leading software for creating deepfakes. |

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Readme

**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.

- Mathematical Foundation
- Machine Learning
- Deep Learning
- Reinforcement Learning
- Natural Language Processing

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. Resource | Difficulty | Relevance ------------------------- | --------------- | ------------------------------- Joe Blitzstein Harvard Probability and Statistics Course |

| MIT Probability Course 2011 Lecture videos | | MIT Probability Course 2018 short videos UPDATED! | | Mathematics for Machine Learning Book: Chapter 6 | | Probalistic Graphical Models CMU Advanced | | Probalistic Graphical Models Stanford Daphne Advanced | | A First Course In Probability Book by Ross | | Joe Blitzstein Harvard Professor Probability Awesome Book | |-Resource | Difficulty | Relevance |
---|---|---|

CMU optimization course 2018 | ||

CMU Advanced optimization course | ||

Stanford Famous optimization course | ||

Boyd Convex Optimization Book |

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 ------------------------- | ---------------

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