Contents
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- Deep Learning - Josh Patterson & Adam Gibson.pdf
- Deep Learning with Python A Hands-on Introduction.pdf
- Fundamentals of Deep Learning.pdf
- Introduction to Deep Learning Using R.pdf
- Learning TensorFlow.pdf
- deeplearning.pdf
- deeplearningbook.pdf
- deeplearningbook_bookmarked.pdf
- oreilly-hands-on-machine-learning-with-scikit-learn-and-tensorflow-1491962291.pdf
- CS 20_Tensorflow for Deep Learning Research
- 01 _ Lecture slide _ Overview of Tensorflow.pdf
- 02_Lecture slide_TensorFlow Operations.pdf
- 03 _ Lecture slide _ Basic Models in TensorFlow.pdf
- 04 Eager Execution + word2vec.pdf
- 05_Slide_Managing your experiment.pdf
- 06_Introduction to Computer Vision and convolutional network.pdf
- 07 _ Covnets in TensorFlow.pdf
- 08_Style transfer.pdf
- 10_Lecture_Slides_VAE in TensorFlow.pdf
- 11 _ Slides _ Introduction to RNNs.pdf
- 12_Slides_Machine Translation.pdf
- 14_Slides_A TensorFlow Chatbot.pdf
- 16_Slides_Tensor2Tensor.pdf
- CS20_intro_to_RL.pdf
- march9guestlecture.pdf
- DeepLearning_chapter-wise-pdf
- table-of-contents.pdf
- acknowledgements.pdf
- notation.pdf
- chapter-1-introduction.pdf
- part-1-basics.pdf
- part-1-chapter-2.pdf
- part-1-chapter-3.pdf
- part-1-chapter-4.pdf
- part-1-chapter-5.pdf
- part-2-deep-network-modern-practices.pdf
- part-2-chapter-6.pdf
- part-2-chapter-7.pdf
- part-2-chapter-8.pdf
- part-2-chapter-9.pdf
- part-2-chapter-10.pdf
- part-2-chapter-11.pdf
- part-2-chapter-12.pdf
- part-3-deep-learning-research.pdf
- part-3-chapter-13.pdf
- part-3-chapter-14.pdf
- part-3-chapter-15.pdf
- part-3-chapter-16.pdf
- part-3-chapter-17.pdf
- part-3-chapter-18.pdf
- part-3-chapter-19.pdf
- part-3-chapter-20.pdf
- bibliography.pdf
- index.pdf
- d2l-en.pdf
- Dive into DeepLearning.pdf
- ee559 Deep learning
- Hands-on-Machine-Learning-with-Scikit-2E.pdf
- deeplearning_2019_spring.pdf
- Deep-Learning-with-PyTorch.pdf
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- 30_03_atelierdatamining.pdf
- Bishop - Pattern Recognition And Machine Learning - Springer 2006.pdf
- Building Machine Learning Systems with Python, 2nd Edition.pdf
- MATLAB Machine Learning by Michael Paluszek.pdf
- Machine Learning in Python.pdf
- Machine_Learning.pdf
- Mastering Feature Engineering.pdf
- Mastering Machine Learning with scikit-learn, 2nd Edition.pdf
- NG_MLY.pdf
- Practical Machine Learning A New Look at Anomaly Detection.pdf
- Practical Machine Learning with H2O.pdf
- Python Real World Machine Learning - Prateek Joshi.pdf
- Gaussian Processes for Machine Learning.pdf
- The Elements of Statistical Learning.pdf
- Foundations of Data Science.pdf
- cs229-cheatsheet
- Automatic_Machine_Learning.pdf
- DataScienceHandbook.pdf
- Python Data Science Handbook.pdf
- Foundations of Data Science(Microsoft).pdf
- Bayesian_Data_Analysis_Third_edition.pdf
- Joseph K. Blitzstein, Jessica Hwang-Introduction to Probability.pdf
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- Applied Text Analysis with Python.pdf
- Natural Language Processing with Python.pdf
- Text Analytics with Python A Practical Real-World Approach to Gaining Actionable Insights from your Data.pdf
- The Text Mining HandBook.pdf
- eisenstein-nlp-notes.pdf
- oxford-cs-deepnlp-2017
- Lecture 1a - Introduction.pdf
- Lecture 1b - Deep Neural Networks Are Our Friends.pdf
- Lecture 2a- Word Level Semantics.pdf
- Lecture 2b - Overview of the Practicals.pdf
- Lecture 3 - Language Modelling and RNNs Part 1.pdf
- Lecture 4 - Language Modelling and RNNs Part 2.pdf
- Lecture 5 - Text Classification.pdf
- Lecture 6 - Nvidia RNNs and GPUs.pdf
- Lecture 7 - Conditional Language Modeling.pdf
- Lecture 8 - Conditional Language Modeling with Attention.pdf
- Lecture 9 - Speech Recognition.pdf
- Lecture 10 - Text to Speech.pdf
- Lecture 11 - Question Answering.pdf
- Lecture 12- Memory Lecture.pdf
- Lecture 13 - Linguistics.pdf
- Speech and Language Processing.pdf
- Embeddings in Natural Language Processing.pdf
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Dissecting Reinforcement Learning
- Dissecting Reinforcement Learning-Part1.pdf
- Dissecting Reinforcement Learning-Part2.pdf
- Dissecting Reinforcement Learning-Part3.pdf
- Dissecting Reinforcement Learning-Part4.pdf
- Dissecting Reinforcement Learning-Part5.pdf
- Dissecting Reinforcement Learning-Part6.pdf
- Dissecting Reinforcement Learning-Part7.pdf
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- Lecture 1: Introduction to Reinforcement Learning
- Lecture 2: Markov Decision Processes
- Lecture 3: Planning by Dynamic Programming
- Lecture 4: Model-Free Prediction
- Lecture 5: Model-Free Control
- Lecture 6: Value Function Approximation
- Lecture 7: Policy Gradient Methods
- Lecture 8: Integrating Learning and Planning
- Lecture 9: Exploration and Exploitation
- Lecture 10: Case Study: RL in Classic Games
- Lecture 11: Case Study: Deep RL
- Video-lectures available here
ChangWookJun / @changwookjun (changwookjun@gmail.com)