├── README.md
└── sources
    └── images
        ├── Readme.md
        └── nlp.png


/README.md:
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  1 | <div align="center">
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  3 |   <img src="sources/images/nlp.png" width="25%"> 
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  9 |   **<img src="https://github.com/TheDudeThatCode/TheDudeThatCode/blob/master/Assets/Rocket.gif" width="29px">From Zero to Research Scientist full resources guide. <img src="https://github.com/TheDudeThatCode/TheDudeThatCode/blob/master/Assets/Hi.gif" width="29px">**
 10 |   
 11 |   
 12 |   ![Full Guide](https://img.shields.io/badge/FullAI-Guide-brightgreen.svg)
 13 |   ![Version 0.0.1](https://img.shields.io/badge/Version-0.0.1-blue.svg)
 14 | </div>
 15 | 
 16 | ## Guide description
 17 | This guide is designated to anybody with basic programming knowledge or a computer science background interested in becoming a Research Scientist with :dart: on Deep Learning and NLP.
 18 | 
 19 | 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.
 20 | 
 21 | ## Contents:
 22 | - [Mathematical Foundation](#Mathematical-Foundations)
 23 |    - [Linear Algebra](#Linear-Algebra) 
 24 |    - [Probability](#Probability) 
 25 |    - [Calculus](#Calculus)
 26 |    - [Optimization Theory](#Optimization-Theory)
 27 | - [Machine Learning](#Machine-Learning)
 28 | - [Deep Learning](#Deep-Learning)
 29 | - [Reinforcement Learning](#Reinforcement-Learning)
 30 | - [Natural Language Processing](#Natural-Language-Processing)
 31 | 
 32 | ## Mathematical Foundations:
 33 | 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.
 34 | 
 35 | ### Linear Algebra 
 36 | 
 37 | <details>
 38 |   <summary>:infinity:</summary>
 39 |   
 40 | <!--START_SECTION:activity-->  
 41 | 
 42 |   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.
 43 | 
 44 | Resource                    | Difficulty     | Relevance 
 45 | ------------------------- | --------------- | -------------------------------
 46 | [MIT Gilbert Strang 2005 Linear Algebra 🎥][gilbertStrang] | <div class="star-ratings-top"><span>★</span><span>★</span><span>☆</span><span>☆</span><span>☆</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning) ![50%](https://progress-bar.dev/50/?title=Machine+Learning+Algorithms&color=000000) ![75%](https://progress-bar.dev/75/?title=Computer+Vision&color=ff0101)
 47 | [Linear Algebra 4th Edition by Friedberg 📘][Friedberg] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>☆</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning)
 48 | [Mathematics for Machine Learning Book: Chapter 2 📘][mmlbook] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>☆</span><span>☆</span></div>| ![50%](https://progress-bar.dev/50/?title=Deep+Learning) ![75%](https://progress-bar.dev/75/?title=Machine+Learning+Algorithms&color=000000)
 49 | [James Hamblin Awesome Lecture Series 🎥][James_Hamblin] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>☆</span><span>☆</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning)
 50 | [3Blue1Brown Essence of Linear Algebra 🎥][3blue] | <div class="star-ratings-top"><span>★</span><span>☆</span><span>☆</span><span>☆</span><span>☆</span></div>| ![25%](https://progress-bar.dev/25/?title=Machine+Learning+Algorithms&color=000000) ![100%](https://progress-bar.dev/100/?title=Deep+Learning)
 51 | [Mathematics For Machine Learning Specialization: Linear Algebra 🎥][MMLLA] | <div class="star-ratings-top"><span>★</span><span>☆</span><span>☆</span><span>☆</span><span>☆</span></div>| ![50%](https://progress-bar.dev/50/?title=Machine-Learning-Algorithms&color=000000) ![100%](https://progress-bar.dev/100/?title=Deep+Learning)
 52 | [Matrix Methods for Linear Algebra for Gilber Strang UPDATED! 🎥][matrixmethods] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>☆</span><span>☆</span></div>|  ![100%](https://progress-bar.dev/100/?title=Deep+Learning)
 53 |   <!--END_SECTION:activity-->
 54 | 
 55 | </details>
 56 | 
 57 | ### Probability
 58 | 
 59 | <details>
 60 |   <summary>:atom: </summary>
 61 |   
 62 | <!--START_SECTION:activity-->  
 63 | 
 64 | 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.
 65 | Resource                    | Difficulty     | Relevance 
 66 | ------------------------- | --------------- | -------------------------------
 67 | [Joe Blitzstein Harvard Probability and Statistics Course 🎥][harvard] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>★</span></div>| ![50%](https://progress-bar.dev/50/?title=Machine+Learning+Algorithms&color=000000) ![25%](https://progress-bar.dev/25/?title=Deep+Learning) ![100%](https://progress-bar.dev/100/?title=Natural+Language+Processing&color=ff69b4) 
 68 | [MIT Probability Course 2011 Lecture videos 🎥][mitprob11] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>☆</span><span>☆</span></div>| ![50%](https://progress-bar.dev/50/?title=Machine+Learning+Algorithms&color=000000) ![75%](https://progress-bar.dev/75/?title=Natural+Language+Processing&color=ff69b4) 
 69 | [MIT Probability Course 2018 short videos UPDATED! 🎥][mitprob18] | <div class="star-ratings-top"><span>★</span><span>★</span><span>☆</span><span>☆<span>☆</span></div>| ![25%](https://progress-bar.dev/50/?title=Machine+Learning+Algorithms&color=000000) ![25%](https://progress-bar.dev/25/?title=Deep+Learning) ![100%](https://progress-bar.dev/100/?title=Natural+Language+Processing&color=ff69b4) 
 70 | [Mathematics for Machine Learning Book: Chapter 6 📘][mmlbook] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>☆</span><span>☆</span></div>| ![75%](https://progress-bar.dev/75/?title=Machine+Learning+Algorithms&color=000000) ![25%](https://progress-bar.dev/25/?title=Deep+Learning) ![75%](https://progress-bar.dev/75/?title=Natural+Language+Processing&color=ff69b4) 
 71 |  [Probabilistic Graphical Models CMU Advanced 🎥][cmuprob] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>★</span></div>| ![50%](https://progress-bar.dev/50/?title=Machine+Learning+Algorithms&color=000000) ![25%](https://progress-bar.dev/25/?title=Deep+Learning) ![100%](https://progress-bar.dev/100/?title=Natural+Language+Processing&color=ff69b4) 
 72 | [Probabilistic Graphical Models Stanford Daphne Advanced 🎥][stanfordprobgraph] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>★</span></div>| ![50%](https://progress-bar.dev/50/?title=Machine+Learning+Algorithms&color=000000) ![25%](https://progress-bar.dev/25/?title=Deep+Learning) ![25%](https://progress-bar.dev/25/?title=Natural+Language+Processing&color=ff69b4) 
 73 |  [A First Course In Probability Book by Ross 📘][probBook] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>☆</span></div>| ![50%](https://progress-bar.dev/50/?title=Machine-Learning-Algorithms&color=000000) 
 74 |  [Joe Blitzstein Harvard Professor Probability Awesome Book 📘][harvBook] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>☆</span><span>☆</span></div>| ![50%](https://progress-bar.dev/50/?title=Machine-Learning-Algorithms&color=000000) 
 75 |   <!--END_SECTION:activity-->
 76 | 
 77 | </details>
 78 | 
 79 | [harvBook]: https://drive.google.com/file/d/1VmkAAGOYCTORq1wxSQqy255qLJjTNvBI/view
 80 | 
 81 | ### Calculus
 82 | 
 83 | <details>
 84 |   <summary>:triangular_ruler:</summary>
 85 |   
 86 | 
 87 | 
 88 | <!--START_SECTION:activity--> 
 89 | Resource                    | Difficulty     | Relevance 
 90 | ------------------------- | --------------- | --------------------------
 91 | [Essence of Calculus by 3Blue1Brown🎥][bluecal]| <div class="star-ratings-top"><span>★</span><span>★</span><span>☆</span><span>☆</span><span>☆</span></div>|![75%](https://progress-bar.dev/75/?title=Deep+Learning)
 92 | [Single Variable Calculus MIT 2007🎥][single07]| <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>☆</span></div>|![75%](https://progress-bar.dev/75/?title=Deep+Learning)
 93 | [Strang's Overview of Calculus🎥][strangcalc]|<div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>☆</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning)
 94 | [MultiVariable Calculus MIT 2007🎥][multi07]| <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>★</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning)
 95 | [Princeton University Multivariable Calculus 2013🎥][princeton]|<div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>☆</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning)
 96 | [Calculus Book by Stewart 📘][calcbok]|<div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>☆</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning) ![25%](https://progress-bar.dev/50/?title=Machine-Learning-Algorithms&color=000000) 
 97 | [Mathematics for Machine Learning Book: Chapter 5 📘][mmlbook] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>☆</span><span>☆</span></div>| ![75%](https://progress-bar.dev/75/?title=Deep+Learning) ![50%](https://progress-bar.dev/50/?title=Machine-Learning-Algorithms&color=000000) 
 98 | 
 99 | 
100 |  <!--END_SECTION:activity-->
101 | 
102 | </details>
103 | 
104 |  ### Optimization Theory
105 |  
106 | <details>
107 |   <summary> 📉 </summary>
108 |   
109 | 
110 | <!--START_SECTION:activity--> 
111 | -Resource                    | Difficulty     | Relevance 
112 | ------------------------- | --------------- | --------------------------
113 | [CMU optimization course 2018🎥][cmuopti]| <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>★</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning) ![25%](https://progress-bar.dev/25/?title=Machine-Learning-Algorithms&color=000000) 
114 | [CMU Advanced optimization course🎥][cmuadvopti]| <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>★</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning) 
115 | [Stanford Famous optimization course 🎥][stanfordopti]| <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>★</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning) 
116 | [Boyd Convex Optimization Book 📕][boyd] | <div class="star-ratings-top"><span>★</span><span>★</span><span>★</span><span>★</span><span>★</span></div>| ![100%](https://progress-bar.dev/100/?title=Deep+Learning) 
117 |  <!--END_SECTION:activity-->
118 | 
119 | </details>
120 | 
121 | -------------------------------------------------------------------------------- 
122 | 
123 | ## Machine Learning
124 | 
125 | 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.
126 | 
127 | Resource                    | Difficulty Level 
128 | ------------------------- | ---------------
129 | [Mathematics for Machine Learning Part 2 📚][fullmmlbook] |![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
130 | [Pattern Recognition and Machine Leanring📚][patternML]|![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
131 | [Elements of Statistical Learning 📚][eesl]|![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
132 | [Introduction to Statistical Learning  📚][introSL]|![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg) 
133 | [Machine Learning: A Probabilistic Perspective 📚][murphyml]|![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
134 | [Berkley CS188 Introduction to AI course 🎥][cs188]|![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg) 
135 | [MIT Classic AI course taught by Prof. Patrick H. Winston 🎥][mitai]|![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg) 
136 | [Stanford AI course 2018 🎥][stai18]|![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
137 | [California Institute of Technology Learning from Data course 🎥][caltldc]|![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
138 | [CMU Machine Learning 2015 10-601 🎥][cmuml2015]|![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
139 | [CMU Statistical Machine Learning 10-702 🎥][cmu702]|![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
140 | [Information Theory, Pattern Recognition ML course 2012 🎥][PR2012]|![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
141 | [Large Scale Machine Learning Toronto University 2015 🎥][toronto2015]|![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
142 | [Algorithmic Aspects of Machine Learning MIT 🎥][Mitaspects]|![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
143 | [MIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015 🎥][mitfallslt]|![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
144 | [Undergraduate Machine Learning Course University of British Columbia 2013 🎥][ubc2013]|![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg) 
145 | 
146 | 
147 | -------------------------------------------------------------------------------- 
148 | 
149 | [murphyml]: http://noiselab.ucsd.edu/ECE228/Murphy_Machine_Learning.pdf
150 | [introSL]: https://www.ime.unicamp.br/~dias/Intoduction%20to%20Statistical%20Learning.pdf
151 | [patternML]:http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf
152 | [eesl]: https://web.stanford.edu/~hastie/Papers/ESLII.pdf
153 | [fullmmlbook]: https://mml-book.com/
154 | [ubc2013]:https://www.youtube.com/watch?v=w2OtwL5T1ow&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6
155 | [mitfallslt]: https://www.youtube.com/playlist?list=PLyGKBDfnk-iDj3FBd0Avr_dLbrU8VG73O
156 | [Mitaspects]: https://www.youtube.com/playlist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx
157 | [toronto2015]:https://video-archive.fields.utoronto.ca/view/2800
158 | [PR2012]: http://videolectures.net/course_information_theory_pattern_recognition/
159 | [cmu702]: https://www.youtube.com/playlist?list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r
160 | [cmuml2015]: http://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml
161 | [caltldc]: https://work.caltech.edu/lectures.html
162 | [cs188]: https://inst.eecs.berkeley.edu/~cs188/fa18/
163 | [mitai]: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-1-introduction-and-scope/
164 | [stai18]: https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX
165 | 
166 | ## Deep Learning 
167 | 
168 | 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.
169 | 
170 | Resource                    | Difficulty Level 
171 | ------------------------- | ---------------
172 | [Deep Learning Book by Ian Goodfellow 📚][Ian] |![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
173 | [UCL DeepMind Deep Learning 🎥][ucl2020] |![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
174 | [Advanced Talks by Deep Learning Pioneers 🎥][talkie] | ![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
175 | [Stanford Autumn 2018 Deep Learning Lectures 🎥][18standeep] | ![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
176 | [FAU Deep Learning 2020 Series 🎥][fau] | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg) 
177 | [CMU Deep Learning course 2020 🎥][cmudeep] | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg) 
178 | [Stanford Convolutional Neural Network 2017 🎥][stanfcnn] | ![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
179 | [Oxford Deep Learning Awesome Lectures 2015 🎥][oxforddeep] |![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
180 | [Stanford NLP with Deep Learning 2019 🎥][stanfordnlp2019] |![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
181 | [Deep Learning from Probability and Statistics POV 🎥][alideep] | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg)
182 | [Advanced Deep Learning UCL 2017 course + Reinforcement Learning 🎥][ucladvrein] | ![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
183 | [Deep Learning UC Berkley 2020 Course 🎥][berkley2020] | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg)
184 | [NYU Deep Learning with Pytorch hands on 🎥][DeepPy] | ![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
185 | [Classic Jeoffrey Hinton Old course OUTDATED 🎥][jeoff] | ![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
186 | [Pieter Abdeel Deep Unsupervised Learning 🎥][abdeeladv] | ![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
187 | [Hugo Larochelle Deep Learning series 🎥][hugodeep] | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg)
188 | [Deep Learning Book Explanation Series 🎥][deepbookexp] | ![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
189 | [Deep Learning Introduction by Durham University 🎥][Durham] | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg)
190 | [Fast.ai Practical Deep Learning 🎥][fast1] | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg)
191 | [Fast.ai Deep Learning From Foundations 🎥][fast2] | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg)
192 | [Deep Learning with Python (Keras Author) 📚][keras] | ![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
193 | --------------------------------------------------------------------------------  
194 | 
195 | ## Reinforcement Learning 
196 | 
197 | It is a sub-field of AI which focuses on learning by observation/rewards. 
198 | 
199 | Resource                    | Difficulty Level 
200 | ------------------------- | ---------------
201 | [Introduction to Reinforcement Learning 📚][rlbook] | ![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
202 | [David Silver Deep Mind Introductory Lectures 🎥][dsIntrodu] | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg)
203 | [Stanford 2018 cs234 Reinforcement Learning🎥 ][cs234] |![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
204 | [Stanford 2019 cs330 Meta Learning advanced course 🎥][cs330] | ![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
205 | [Sergie Levine 2018 UC Berkley Lecture Videos 🎥][ucb2018rl] | ![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
206 | [Waterloo cs885 Reinforcement Learning 🎥][cs885] | ![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
207 | [Sergie Levine 2020 Deep Reinforcement Learning 🎥][sergie2020rl] | ![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
208 | [Reinforcement Learning Specialization Coursera GOLDEN courses🎥 (Though it is not free but you can apply for financial aid)][courseraRL] |![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
209 | 
210 | --------------------------------------------------------------------------------  
211 | 
212 | ## Natural Language Processing
213 | 
214 | It is a sub-field of AI which focuses on the interpretation of Human Language. 
215 | 
216 | Resource                    | Difficulty Level 
217 | ------------------------- | ---------------
218 | [Jurafsky Speech and Language Processing 📚][jurafskybook]|![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
219 | [Christopher Manning Foundations of Statistical NLP📚][fsnlp]| ![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
220 | [Christopher Manning Introduction to Information Retrieval📚][manninginformationr]| ![Advanced](https://img.shields.io/badge/Level-Advanced-red.svg)
221 | [cs224n Natural Language Processing with Deep Learning GOLDEN 2019🎥][stanfordnlp2019] |![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
222 | [Oxford Natural Language Processing with Deep Learning 2017🎥][oxfordnlp] |![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
223 | [Michigan Introduction to NLP🎥][michigannlp]  | ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg)
224 | [cs224u Natural Language Understanding 2019 🎥][stanfordnlu] |![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
225 | [cmu 2021 Neural Nets for NLP 2021🎥][cmunlp2021]|![Intermediate](https://img.shields.io/badge/Level-Intermediate-yellow.svg)
226 | [Jurafsky and Manning Introduction to Natural Language Processing🎥][jurafskynlp]| ![Introductory](https://img.shields.io/badge/Level-Introductory-brightgreen.svg)
227 | 
228 | ### Must Read NLP Papers:
229 | 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.
230 | Paper                    | Comment
231 | ------------------------- | ---------------
232 | # TODO
233 | 
234 | 
235 | 
236 | 
237 | 
238 | 
239 | 
240 | 
241 | 
242 | [manninginformationr]: https://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf
243 | [fsnlp]: https://github.com/shivamms/books/blob/master/nlp/Foundations%20of%20Statistical%20Natural%20Language%20Processing%20-%20Christopher%20D.%20Manning.pdf
244 | [jurafskybook]: https://web.stanford.edu/~jurafsky/slp3/
245 | [jurafskynlp]: https://www.youtube.com/watch?v=zQ6gzQ5YZ8o&list=PLoROMvodv4rOFZnDyrlW3-nI7tMLtmiJZ
246 | [cmunlp2021]: https://www.youtube.com/watch?v=vnx6M7N-ggs&list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV
247 | [stanfordnlu]: https://www.youtube.com/watch?v=tZ_Jrc_nRJY&list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20
248 | [michigannlp]:https://www.youtube.com/watch?v=n25JjoixM3I&list=PLLssT5z_DsK8BdawOVCCaTCO99Ya58ryR 
249 | [oxfordnlp]: https://www.youtube.com/watch?v=RP3tZFcC2e8&list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm
250 | [courseraRL]: https://www.coursera.org/specializations/reinforcement-learning
251 | [sergie2020rl]: https://www.youtube.com/watch?v=JHrlF10v2Og&list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
252 | [cs885]: https://www.youtube.com/playlist?list=PLdAoL1zKcqTXFJniO3Tqqn6xMBBL07EDc
253 | [ucb2018rl]: https://www.youtube.com/watch?v=ue9aS17d5iI&list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37&index=2
254 | [cs330]: https://www.youtube.com/watch?v=0rZtSwNOTQo&list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5
255 | [cs234]: https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u
256 | [dsIntrodu]: https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
257 | [rlbook]: http://incompleteideas.net/book/RLbook2020.pdf
258 | [Ian]: https://github.com/janishar/mit-deep-learning-book-pdf/blob/master/complete-book-pdf/Ian%20Goodfellow%2C%20Yoshua%20Bengio%2C%20Aaron%20Courville%20-%20Deep%20Learning%20(2017%2C%20MIT).pdf
259 | [fast2]: https://course19.fast.ai/part2
260 | [fast1]: https://course.fast.ai/
261 | [abdeeladv]: https://www.youtube.com/watch?v=V9Roouqfu-M&list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP
262 | [durham]: https://www.youtube.com/watch?v=s2uXPz3wyCk&list=PLMsTLcO6etti_SObSLvk9ZNvoS_0yia57
263 | [deepbookexp]: https://www.youtube.com/watch?v=vi7lACKOUao&list=PLsXu9MHQGs8df5A4PzQGw-kfviylC-R9b
264 | [hugodeep]: https://www.youtube.com/watch?v=SGZ6BttHMPw&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
265 | [jeoff]: https://www.youtube.com/watch?v=cbeTc-Urqak&list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9
266 | [DeepPy]: https://www.youtube.com/watch?v=0bMe_vCZo30&list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq
267 | [berkley2020]: https://www.youtube.com/watch?v=Va8WWRfw7Og&list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW
268 | [ucladvrein]: https://www.youtube.com/watch?v=iOh7QUZGyiU&list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs
269 | [alideep]: https://www.youtube.com/watch?v=fyAZszlPphs&list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE
270 | [stanfordnlp2019]: https://www.youtube.com/watch?v=8rXD5-xhemo&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z
271 | [oxforddeep]: https://www.youtube.com/watch?v=PlhFWT7vAEw&list=RDQMa66mIb9tImc&start_radio=1
272 | [stanfcnn]: https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv
273 | [cmudeep]: https://www.youtube.com/watch?v=0Oqpax2Q2hc&list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe
274 | [fau]: https://www.youtube.com/watch?v=p-_Stl0t3kU&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj
275 | [18standeep]: https://www.youtube.com/watch?v=PySo_6S4ZAg&list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb
276 | [talkie]: https://www.youtube.com/watch?v=vFYkyk_GmWM&list=PLhb1t0L7sKy2q7on_7dpgOACs3qpNbfkR&index=2
277 | [ucl2020]: https://www.youtube.com/watch?v=7R52wiUgxZI&list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF
278 | [boyd]: https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf
279 | [cmuopti]: https://www.youtube.com/watch?v=Di9f47LAzHQ&list=PLRPU00LaonXQ27RBcq6jFJnyIbGw5azOI
280 | [cmuadvopti]: https://www.youtube.com/watch?v=yBO4E1FARaA&list=PLjTcdlvIS6cjdA8WVXNIk56X_SjICxt0d
281 | [stanfordopti]: https://www.youtube.com/watch?v=McLq1hEq3UY&list=PL3940DD956CDF0622
282 | [calcbok]: http://index-of.co.uk/Mathematics/Calculus%20-%20J.%20Stewart.pdf
283 | [princeton]: https://www.youtube.com/watch?v=uDByROsGzuk&list=PLGqzsq0erqU7h6_bpE-CgJp4iX5aRju28
284 | [multi07]: https://www.youtube.com/watch?v=PxCxlsl_YwY&list=PL4C4C8A7D06566F38
285 | [strangcalc]: https://www.youtube.com/watch?v=X9t-u87df3o&list=PLBE9407EA64E2C318
286 | [single07]: https://www.youtube.com/watch?v=7K1sB05pE0A&list=PL590CCC2BC5AF3BC1
287 | [matrixmethods]: https://www.youtube.com/watch?v=Cx5Z-OslNWE&list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k
288 | [bluecal]: https://www.youtube.com/watch?v=WUvTyaaNkzM&list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x
289 | [probBook]: http://www.seyedkalali.com/wp-content/uploads/2016/11/A-First-Course-in-Probability-8th-ed.-Sheldon-Ross.pdf
290 | [stanfordprobgraph]: https://www.youtube.com/watch?v=GqMzbbaN6T4&list=PLzERW_Obpmv-_TkPEmCyzaJUGHtl7S01i
291 | [cmuprob]: https://www.youtube.com/watch?v=oqvdH_8lmCA&list=PLoZgVqqHOumTqxIhcdcpOAJOOimrRCGZn
292 | [mitprob18]: https://www.youtube.com/watch?v=1uW3qMFA9Ho&list=PLUl4u3cNGP60hI9ATjSFgLZpbNJ7myAg6
293 | [mitprob11]: https://www.youtube.com/watch?v=j9WZyLZCBzs&list=PLUl4u3cNGP61MdtwGTqZA0MreSaDybji8
294 | [harvard]: https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo
295 | [MMLLA]: https://www.youtube.com/watch?v=T73ldK46JqE&list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3
296 | [3blue]: https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
297 | [gilbertStrang]: https://www.youtube.com/watch?v=QVKj3LADCnA&list=PL49CF3715CB9EF31D
298 | [Friedberg]: https://www.academia.edu/43200796/Linear_Algebra
299 | [mmlbook]: https://mml-book.github.io/book/mml-book.pdf
300 | [James_Hamblin]: https://www.youtube.com/watch?v=HAoL5fPmgrw&list=PLNr8B4XHL5kGDHOrU4IeI6QNuZHur4F86
301 | [keras]: https://www.manning.com/books/deep-learning-with-python
302 | 


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