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Reformatted and added new resources (Suggestions are welcomed 🙂) (#12)
* Added .gitignore and reformatted README.md * Added new resources * Added a contributing rules as a comment * added new line in .gitignore * add stuff todo: fix dependency tree / roadmap Co-authored-by: Tanmay Agrawal <tanmay7270@gmail.com>
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| <!-- Use the vscode extensions markdownlint and Markdown All in One while contributing --> | ||
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| # McCarthy-AI-Roadmap | ||
| Roadmap to learn AI for associates at McCarthy Lab@[Next Tech Lab](https://nextech.io/home) | ||
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| Original repository : https://github.com/niladridutt/McCarthy-AI-Roadmap | ||
| Roadmap to learn AI for associates at McCarthy Lab@[Next Tech Lab](https://www.nexttechlab.io/) | ||
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| **Note** : *This is a rough roadmap to guide you on your Machine Learning journey. We recommend everyone to focus on core CS principles and industry-standard tech stacks with a strong focus on Machine Learning theory and development. This might look overwhelming at first, but it's really not.* 🙂 | ||
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| > Roadmap | ||
| > | ||
| > Dependency Tree | ||
|  | ||
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| # Primer | ||
| ## 1. <u>Data Science Stack</u> | ||
| + Best places to learn - | ||
| + https://www.python-course.eu/ | ||
| + https://www.datacamp.com/ | ||
| + https://www.pythonprogramming.net/ | ||
| + Python | ||
| + NumPy | ||
| + Pandas | ||
| + Matplotlib and Seaborn | ||
| + scikit-learn | ||
| + SciPy | ||
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| ## 2. <u>Technology Stack</u> | ||
| + Basic computer architecture: | ||
| + [GPU vs CPU](https://blogs.nvidia.com/blog/2009/12/16/whats-the-difference-between-a-cpu-and-a-gpu/) | ||
| + [File Systems](https://wiki.microfocus.com/index.php/File_System_Primer) | ||
| + [Linux](https://www.digitalocean.com/community/tutorials/an-introduction-to-linux-basics) | ||
| + [Containers - Docker](https://docs.docker.com/engine/docker-overview/) | ||
| + [Bash Cheatsheet](https://devhints.io/bash) | ||
| + [Git-Introduction](https://readwrite.com/2013/09/30/understanding-github-a-journey-for-beginners-part-1/) | ||
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| ## 3. <u>Deep Learning Frameworks </u> | ||
| + [PyTorch](https://pytorch.org/tutorials/) | ||
| + [Deep Learning with PyTorch](https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf) - excellent resource for learning | ||
| + Use [fast.ai](https://docs.fast.ai/training.html) as High level wrapper (not recommended due to instability of the library and lack of adequate documentation) | ||
| + [TensorFlow](https://www.tensorflow.org/tutorials/) | ||
| + Use [tf.keras](https://www.tensorflow.org/guide/keras) as a High level wrapper | ||
| + [Effective TensorFlow](https://github.com/vahidk/EffectiveTensorflow) | ||
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| ## 4.Reinforcement Learning - Libraries | ||
| + [TensorLayer](https://github.com/tensorlayer/tensorlayer) | ||
| + [Keras-RL](https://github.com/keras-rl/keras-rl) | ||
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| # Mooc Resources | ||
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| ## <u>Data Structures and Algorithms</u> | ||
| + [Stanford ALgorithms - Coursera](https://www.coursera.org/specializations/algorithms) or | ||
| + [Introduction to Algorithms (MIT 6.006)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/) | ||
| + [Introduction to Computational Thinking and Data Science (MIT 6.0002)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/) | ||
| + Learn any one programming language really well and compete on Codechef, Hackerrank, HackerEarth,etc | ||
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| ## <u>Machine Learning/AI MOOCs</u> | ||
| + [Machine Learning - Coursera](https://www.coursera.org/learn/machine-learning) | ||
| + [UC Berkeley CS188](https://inst.eecs.berkeley.edu/~cs188/fa18/) or | ||
| + [MIT 6.034](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/) | ||
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| Note : | ||
| + Implement Machine Learning models from scratch using Python | ||
| + Once you're comfortable implementing models from scratch, learn scikit-learn and compare performance | ||
| + Practice on Kaggle to get your skiills ---> :sunglasses: | ||
| ## 1. Data Science Stack | ||
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| ## Deep Learning MOOCs | ||
| + [TensorFlow in Practice Specialization - Coursera](https://www.coursera.org/specializations/tensorflow-in-practice?) | ||
| + [fast.ai](http://www.fast.ai/) | ||
| + [Stanford University's CS224n - NLP](https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) | ||
| * Best places to learn - | ||
| * [python-course.eu](https://www.python-course.eu/) | ||
| * [datacamp.com](https://www.datacamp.com/) | ||
| * [pythonprogramming.net](https://www.pythonprogramming.net/) | ||
| * Python | ||
| * NumPy | ||
| * Pandas | ||
| * Matplotlib and Seaborn | ||
| * scikit-learn | ||
| * SciPy | ||
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| ## Reinforcement Learning Tutorials | ||
| + [David Silver](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT) | ||
| + [Practical Reinforcement Learning](https://www.coursera.org/learn/practical-rl) | ||
| + [Practial RL - Yandex Data School](https://github.com/yandexdataschool/Practical_RL) | ||
| ## 2. Technology Stack | ||
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| * [The Missing Semester from your CS education] (https://missing.csail.mit.edu/) | ||
| * Basic computer architecture: | ||
| * [GPU vs CPU](https://blogs.nvidia.com/blog/2009/12/16/whats-the-difference-between-a-cpu-and-a-gpu/) | ||
| * [File Systems](https://wiki.microfocus.com/index.php/File_System_Primer) | ||
| * [Linux](https://www.digitalocean.com/community/tutorials/an-introduction-to-linux-basics) | ||
| * [Containers - Docker](https://docs.docker.com/engine/docker-overview/) | ||
| * [Bash Cheatsheet](https://devhints.io/bash) | ||
| * [Git-Introduction](https://readwrite.com/2013/09/30/understanding-github-a-journey-for-beginners-part-1/) | ||
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| ## 3. Deep Learning Frameworks | ||
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| * [PyTorch](https://pytorch.org/tutorials/) | ||
| * [Deep Learning with PyTorch](https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf) - excellent resource for learning | ||
| * Use [fast.ai](https://docs.fast.ai/training.html) as High level wrapper (not recommended due to instability of the library and lack of adequate documentation) | ||
| * [TensorFlow](https://www.tensorflow.org/tutorials/) | ||
| * Use [tf.keras](https://www.tensorflow.org/guide/keras) as a High level wrapper | ||
| * [Effective TensorFlow](https://github.com/vahidk/EffectiveTensorflow) | ||
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| ## 4. Reinforcement Learning - Libraries | ||
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| * [List of active projects](https://github.com/kengz/awesome-deep-rl#libraries) | ||
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| ## Mooc Resources | ||
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| ## Data Structures and Algorithms | ||
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| * [Stanford Algorithms - Coursera](https://www.coursera.org/specializations/algorithms) or | ||
| * [Introduction to Algorithms (MIT 6.006)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/) | ||
| * [Introduction to Computational Thinking and Data Science (MIT 6.0002)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/) | ||
| * Learn any one programming language really well and compete on Codechef, Hackerrank, HackerEarth, etc | ||
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| ## Machine Learning/AI MOOCs | ||
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| ## Deploying/Shipping Projects: | ||
| <b> Feel free to use any of these frameworks, all are not required </b> | ||
| + [Full Stack Deep Learning](https://fullstackdeeplearning.com/) | ||
| + [TensorFlow: Data and Deployment Specialization](https://www.coursera.org/specializations/tensorflow-data-and-deployment?) | ||
| + [Django](https://docs.djangoproject.com/en/3.0/intro/tutorial01/) | ||
| + [Flask](https://www.tutorialspoint.com/flask/index.htm) | ||
| + [Flutter](https://www.tutorialspoint.com/flutter/index.htm) | ||
| * [Machine Learning - Coursera](https://www.coursera.org/learn/machine-learning) | ||
| * [UC Berkeley CS188](https://inst.eecs.berkeley.edu/~cs188/fa18/) or | ||
| * [MIT 6.034](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/) | ||
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| **Note** : | ||
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| # Books and Further reading material | ||
| * Implement Machine Learning models from scratch using Python | ||
| * Once you're comfortable implementing models from scratch, learn scikit-learn and compare performance | ||
| * Practice on Kaggle to get your skills ---> :sunglasses: | ||
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| ## Deep Learning MOOCs | ||
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| * [TensorFlow in Practice Specialization - Coursera](https://www.coursera.org/specializations/tensorflow-in-practice?) | ||
| * [fast.ai](http://www.fast.ai/) | ||
| * [Stanford University's CS224n - NLP](https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) | ||
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| ## Reinforcement Learning Tutorials | ||
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| * [David Silver](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT) | ||
| * [Practical Reinforcement Learning](https://www.coursera.org/learn/practical-rl) | ||
| * [Practial RL - Yandex Data School](https://github.com/yandexdataschool/Practical_RL) | ||
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| ## Deploying/Shipping Projects | ||
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| ### Feel free to use any of these frameworks, all are not required | ||
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| * [Full Stack Deep Learning](https://fullstackdeeplearning.com/) | ||
| * [Full Stack Python] (https://www.fullstackpython.com/) | ||
| * [TensorFlow: Data and Deployment Specialization](https://www.coursera.org/specializations/tensorflow-data-and-deployment?) | ||
| * [Django](https://docs.djangoproject.com/en/3.0/intro/tutorial01/) | ||
| * [Flask](https://www.tutorialspoint.com/flask/index.htm) | ||
| * [Flutter](https://www.tutorialspoint.com/flutter/index.htm) | ||
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| ## Academic Courses | ||
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| * [Deep Learning (with Pytorch)](https://atcold.github.io/pytorch-Deep-Learning/) | ||
| * DS-GA 1008: Deep Learning | SPRING 2020 | ||
| * [Introduction to Deep Learning](http://introtodeeplearning.com/) | ||
| * MIT 6.S191: Introduction to Deep Learning | 2020 | ||
| * [CNNs for Visual Recognition](http://cs231n.stanford.edu) | ||
| * CS231n: CNNs for Visual Recognition, Stanford | Spring 2019 | ||
| * [NLP with Deep Learning](http://web.stanford.edu/class/cs224n/index.html#schedule) | ||
| * CS224n: NLP with Deep Learning, Stanford | Winter 2019 | ||
| * [Deep Reinforcement Learning](https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A) | ||
| * CS285: Deep Reinforcement Learning, UC Berkeley | Fall 2020 | ||
| * CS285: Deep Reinforcement Learning, UC Berkeley | Fall 2019 | ||
| * [Unsupervised Learning](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP) | ||
| * CS294-158-SP20: Deep Unsupervised Learning, UC Berkeley | Spring 2020 | ||
| * [Multi-Task and Meta Learning](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5) | ||
| * Stanford CS330: Multi-Task and Meta-Learning | 2019 | ||
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| ## Books and Further reading material | ||
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| ## Machine Learning Books for reference | ||
| + [Introduction to Statstical Learning](https://www-bcf.usc.edu/~gareth/ISL/) | ||
| + [Elements of Statistical Learning](https://web.stanford.edu/~hastie/Papers/ESLII.pdf) (A little more in-depth than ISLR) | ||
| + [Pattern Recognition And Machine Learning](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) | ||
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| * [Introduction to Statstical Learning](https://www-bcf.usc.edu/~gareth/ISL/) | ||
| * [Elements of Statistical Learning](https://web.stanford.edu/~hastie/Papers/ESLII.pdf) (A little more in-depth than ISLR) | ||
| * [Pattern Recognition And Machine Learning](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) | ||
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| Note : Learn from official tutorials/docs or GitHub repos which have detailed notebooks like [Hvass Labs](https://github.com/Hvass-Labs/TensorFlow-Tutorials) | ||
| **Note** : Learn from official tutorials/docs or GitHub repos which have detailed notebooks like [Grokking Deep Learning](https://github.com/iamtrask/Grokking-Deep-Learning) or [PyTorch Examples](https://github.com/pytorch/examples) | ||
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| ## Deep Learning Books | ||
| + [Deep Learning Book](http://www.deeplearningbook.org/) | ||
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| * [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) | ||
| * [Deep Learning Book](http://www.deeplearningbook.org/) | ||
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| ## Mathematics for Machine Learning | ||
| + [The Matrix Calculus You Need For Deep Learning - - Quick refresher](https://arxiv.org/pdf/1802.01528) | ||
| + [Mathematics for Machine Learning - Intermediate ](https://mml-book.github.io/) | ||
| + [Numerical Algorithms - Advanced](https://people.csail.mit.edu/jsolomon/share/book/numerical_book.pdf) | ||
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| ## Natural Language Processing | ||
| + [Natural Language Processing by National Research University Higher School of Economics](https://www.coursera.org/learn/language-processing) | ||
| + [NLP course by Yandex Data School](https://github.com/yandexdataschool/nlp_course) | ||
| * [The Matrix Calculus You Need For Deep Learning - - Quick refresher](https://arxiv.org/pdf/1802.01528) | ||
| * [Mathematics for Machine Learning - Intermediate](https://mml-book.github.io/) | ||
| * [Numerical Algorithms - Advanced](https://people.csail.mit.edu/jsolomon/share/book/numerical_book.pdf) | ||
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| ## Natural Language Processing | ||
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| * [Natural Language Processing by National Research University Higher School of Economics](https://www.coursera.org/learn/language-processing) | ||
| * [NLP course by Yandex Data School](https://github.com/yandexdataschool/nlp_course) | ||
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| ## Reinforcement Learning Books | ||
| + [Reinforcement Learning – An Introduction](https://drive.google.com/file/d/1opPSz5AZ_kVa1uWOdOiveNiBFiEOHjkG/view) | ||
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| * [Reinforcement Learning – An Introduction](https://drive.google.com/file/d/1opPSz5AZ_kVa1uWOdOiveNiBFiEOHjkG/view) | ||
| * [Spinning Up RL](https://spinningup.openai.com/en/latest/) (Blog) | ||
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| ## Read blogs, Reddit, follow researchers on Twitter | ||
| + [Towards Data Science](https://towardsdatascience.com/) | ||
| + [Sebastian Ruder](http://ruder.io/) | ||
| + [montreal.ai](https://montrealartificialintelligence.com/) | ||
| + [thegradient](https://thegradient.pub/) | ||
| + [Reddit - Machine Learning](https://www.reddit.com/r/MachineLearning/) | ||
| + [Reddit - Deep Learning](https://www.reddit.com/r/deeplearning/) | ||
| + [https://github.com/ujjwalkarn/Machine-Learning-Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials) | ||
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| # Podcasts to Follow Interesting Developments In The Field | ||
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| + [TWIML AI Podcast](https://twimlai.com/tag/podcast/) | ||
| + [The Data Skeptic ](https://open.spotify.com/show/1BZN7H3ikovSejhwQTzNm4?si=gv3IrtPzQs6F9phaHDGpSQ) | ||
| + [The AI Podcast - Nvidia](https://soundcloud.com/theaipodcast) | ||
| + [Artificial Intelligence with Lex Fridman, MIT AI](https://open.spotify.com/show/2MAi0BvDc6GTFvKFPXnkCL) | ||
| + [Linear Digressions](http://lineardigressions.com/) | ||
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| #### Feel free to make Pull Requests stating why that particular resource should be added. | ||
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| * [Towards Data Science](https://towardsdatascience.com/) | ||
| * [Sebastian Ruder](http://ruder.io/) | ||
| * [montreal.ai](https://montrealartificialintelligence.com/) | ||
| * [thegradient](https://thegradient.pub/) | ||
| * [Reddit - Machine Learning](https://www.reddit.com/r/MachineLearning/) | ||
| * [Reddit - Deep Learning](https://www.reddit.com/r/deeplearning/) | ||
| * [https://github.com/ujjwalkarn/Machine-Learning-Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials) | ||
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| ## Podcasts to Follow Interesting Developments In The Field | ||
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| * [TWIML AI Podcast](https://twimlai.com/tag/podcast/) | ||
| * [The Data Skeptic](https://open.spotify.com/show/1BZN7H3ikovSejhwQTzNm4?si=gv3IrtPzQs6F9phaHDGpSQ) | ||
| * [The AI Podcast - Nvidia](https://soundcloud.com/theaipodcast) | ||
| * [Artificial Intelligence with Lex Fridman, MIT AI](https://open.spotify.com/show/2MAi0BvDc6GTFvKFPXnkCL) | ||
| * [Linear Digressions](http://lineardigressions.com/) | ||
| * [Yannic Kilcher](https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew) | ||
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| **Feel free to make Pull Requests stating why that particular resource should be added.** |