1. Machine Leaning and Deep Learning
Deep Learning-EPFL EE559-2019
ee559-handout-1-1-from-anns-to-deep-learning.pdf
ee559-handout-1-2-current-success.pdf
ee559-handout-1-3-what-is-happening.pdf
ee559-handout-1-4-tensors-and-linear-regression.pdf
ee559-handout-1-5-high-dimension-tensors.pdf
ee559-handout-1-6-tensor-internals.pdf
ee559-handout-10-1-GAN.pdf
ee559-handout-10-2-Wasserstein-GAN.pdf
ee559-handout-10-3-conditional-GAN.pdf
ee559-handout-10-4-persistence.pdf
ee559-handout-11-1-RNN-basics.pdf
ee559-handout-11-2-LSTM-and-GRU.pdf
ee559-handout-11-3-word-embeddings-and-translation.pdf
ee559-handout-2-1-loss-and-risk.pdf
ee559-handout-2-2-overfitting.pdf
ee559-handout-2-3-bias-variance-dilemma.pdf
ee559-handout-2-4-evaluation-protocols.pdf
ee559-handout-2-5-basic-embeddings.pdf
ee559-handout-3-1-perceptron.pdf
ee559-handout-3-2-LDA.pdf
ee559-handout-3-3-features.pdf
ee559-handout-3-4-MLP.pdf
ee559-handout-3-5-gradient-descent.pdf
ee559-handout-3-6-backprop.pdf
ee559-handout-4-1-DAG-networks.pdf
ee559-handout-4-2-autograd.pdf
ee559-handout-4-3-modules-and-batch-processing.pdf
ee559-handout-4-4-convolutions.pdf
ee559-handout-4-5-pooling.pdf
ee559-handout-4-6-writing-a-module.pdf
ee559-handout-5-1-cross-entropy-loss.pdf
ee559-handout-5-2-SGD.pdf
ee559-handout-5-3-optim.pdf
ee559-handout-5-4-l2-l1-penalties.pdf
ee559-handout-5-5-initialization.pdf
ee559-handout-5-6-architecture-and-training.pdf
ee559-handout-5-7-writing-an-autograd-function.pdf
ee559-handout-6-1-benefits-of-depth.pdf
ee559-handout-6-2-rectifiers.pdf
ee559-handout-6-3-dropout.pdf
ee559-handout-6-4-batch-normalization.pdf
ee559-handout-6-5-residual-networks.pdf
ee559-handout-6-6-using-GPUs.pdf
ee559-handout-7-1-CV-tasks.pdf
ee559-handout-7-2-image-classification.pdf
ee559-handout-7-3-object-detection.pdf
ee559-handout-7-4-segmentation.pdf
ee559-handout-7-5-dataloader-and-surgery.pdf
ee559-handout-8-1-looking-at-parameters.pdf
ee559-handout-8-2-looking-at-activations.pdf
ee559-handout-8-3-visualizing-in-input.pdf
ee559-handout-8-4-optimizing-inputs.pdf
ee559-handout-9-1-transposed-convolutions.pdf
ee559-handout-9-2-autoencoders.pdf
ee559-handout-9-3-denoising-and-variational-autoencoders.pdf
ee559-handout-9-4-NVP.pdf
Tensorflow for Deep Learning Research-Stanford CS 20-2018
A First Course in Machine Learning-2012.pdf
AutoML Machine Learning-Methods, Systems, Challenges-2018.pdf
Building Machine Learning Systems with Python-2nd Edition-2015.pdf
Data Mining, Inference, and Prediction-2017.pdf
Data Science from Scratch- First Principles with Python-2015.pdf
Deep Learning With Python-Develop Deep Learning Models on Theano and TensorFlow Using Keras-2017.pdf
Deep Learning with Keras-2017.pdf
Deep Learning with Python A Hands-on Introduction-2017.pdf
Deep Learning with Python-Francois_Chollet-En-2018.pdf
Deep Learning with Python-Francois_Chollet-中文-Python深度学习-2018.pdf
Deep Learning with Tensorflow-2017.pdf
Deep Learning-Josh Patterson & Adam Gibson-2017.pdf
Deep_Learning-Ian_Goodfellow-En-2016.pdf
Deep_Learning-Ian_Goodfellow-中文-2017.pdf
Deep_Learning-台大李宏毅-En-2016.pdf
Designing Machine Learning Systems with Python-2016.pdf
Foundations of Data Science-2018.pdf
Fundamentals of Deep Learning-2017.pdf
Gaussian Processes for Machine Learning-2006.pdf
Hands on Machine Learning with Scikit Learn and TensorFlow-En-2017.pdf
Hands on Machine Learning with Scikit Learn and TensorFlow-中文-机器学习实用指南-2017.pdf
Introduction to Machine Learning with Python-2016.pdf
Introduction to Machine Learning-sencond-edition-EN-2010.pdf
Learning Generative Adversarial Networks-2017.pdf
Learning TensorFlow-2017.pdf
MATLAB Machine Learning by Michael Paluszek-2017.pdf
Machine Learning Yearning-Andrew Ng-2018.pdf
Machine Learning for OpenCV-2017.pdf
Machine Learning in Action-EN-2012.pdf
Machine Learning in Action-中文-2012.pdf
Machine Learning in Python-2015.pdf
Machine Learning with Python Scikit-Learn-2015.pdf
Machine Learning-A Probabilistic Perspective-2012.pdf
Mastering Feature Engineering-2016.pdf
Mastering Machine Learning with scikit-learn-2017.pdf
Pattern Recognition And Machine Learning _中文-马春鹏-2014.pdf
Pattern Recognition And Machine Learning-EN-2006.pdf
Practical Machine Learning with H2O-2016.pdf
Practical Machine Learning-A New Look at Anomaly Detection-2014.pdf
Pro Deep Learning with TensorFlow-2017.pdf
Python Machine Learning-2015.pdf
Python Real World Machine Learning - Prateek Joshi-2016.pdf
Tensorflow Machine Learning Cookbook-2017.pdf
Tensorflow实战Google深度学习框架-2017.pdf
机器学习(西瓜书)_周志华-中文-2016.pdf
5. Computer Vision (CV) Book
6. Reinforcement Learning Books
You can’t perform that action at this time.