changwookjun/StudyBook
Study E-Book(ComputerVision DeepLearning MachineLearning Math NLP Python ReinforcementLearning)
| repo name | changwookjun/StudyBook |
| repo link | https://github.com/changwookjun/StudyBook |
| homepage | |
| language | |
| size (curr.) | 1102316 kB |
| stars (curr.) | 1044 |
| created | 2018-05-15 |
| license | |
Study E-Book(ComputerVision DeepLearning MachineLearning Math NLP Python ReinforcementLearning)
Contents
-
- Deep Learning - Josh Patterson & Adam Gibson.pdf
- Deep Learning with Keras by Antonio Gulli.pdf
- Deep Learning with Python A Hands-on Introduction.pdf
- Deep Learning with TensorFlow.pdf
- Deep Learning with Theano.pdf
- Fundamentals of Deep Learning.pdf
- Introduction to Deep Learning Using R.pdf
- Learning TensorFlow.pdf
- Python Deep Learning Cookbook.pdf
- Python Deep Learning.pdf
- R Deep Learning Cookbook.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
- Hands On Transfer Learning with Python_eBook.pdf
- ADVANCED_DEEP_LEARNING_WITH_KERAS.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
-
- 30_03_atelierdatamining.pdf
- Advanced-Machine-Learning-with-Python.azw3.pdf
- Bishop - Pattern Recognition And Machine Learning - Springer 2006.pdf
- Building Machine Learning Projects with TensorFlow.pdf
- Building Machine Learning Systems with Python, 2nd Edition.pdf
- Designing Machine Learning Systems with Python 2016 {PRG}.pdf
- Hands-On Data Science and Python Machine Learning.pdf
- Introduction to Machine Learning with Python.pdf
- Large Scale Machine Learning with Spark.pdf
- Learning Predictive Analytics with Python By Ashish Kumar Feb 2016 PACKT.pdf
- MATLAB Machine Learning by Michael Paluszek.pdf
- Machine Learning Algorithms.pdf
- Machine Learning in Python.pdf
- Machine_Learning.pdf
- Mastering Feature Engineering.pdf
- Mastering Machine Learning with scikit-learn, 2nd Edition.pdf
- Mastering Machine Learning with scikit-learn.pdf
- NG_MLY.pdf
- Practical Machine Learning A New Look at Anomaly Detection.pdf
- Practical Machine Learning with H2O.pdf
- Python Data Analytics.pdf
- Python Machine Learning By Example.pdf
- Python Machine Learning.pdf
- Python Real World Machine Learning - Prateek Joshi.pdf
- TensorFlow Machine Learning Cookbook.pdf
- python-machine-learning-2nd.pdf
- scikit-learn Cookbook.pdf
- Gaussian Processes for Machine Learning.pdf
- The Elements of Statistical Learning.pdf
- Hands-On Machine Learning for Algorithmic Trading [eBook].pdf
- Foundations of Data Science.pdf
- cs229-cheatsheet
- Automatic_Machine_Learning.pdf
- DataScienceHandbook.pdf
- Python Data Science Handbook.pdf
-
- Applied Text Analysis with Python.pdf
- Jacob Perkins-Python 3 Text Processing with NLTK 3.pdf
- NLTK Essentials.pdf
- Natural Language Processing with Python.pdf
- Python 3 Text Processing with NLTK 3 Cookbook.pdf
- Python Text Processing with NLTK 2.0 Cookbook.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
-
- IPython Interactive Computing and Visualization Cookbook.pdf
- Learn Python The Hard Way 3rd Edition free pdf download.pdf
- Learning NumPy Array.pdf
- Learning Pandas.pdf
- Mastering Pandas for Finance.pdf
- Mastering Pandas.pdf
- Mastering-Python.pdf
- NumPy Beginner’s Guide, 2nd Edition.pdf
- NumPy, 3rd Edition.pdf
- SciPy and NumPy.pdf
- ScipyLectures-simple.pdf
- Shaw Z.A. - Learn Python the Hard Way, 2nd Edition [2011, PDF, ENG].pdf
- Understanding GIL.pdf
- scipy-ref-0.17.0.pdf
-
-
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
-
- 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
Author
ChangWookJun / @changwookjun (changwookjun@gmail.com)
