Resources
Books (Applied ML)
Introduction to Machine Learning with Python , Muller & Guido | O'ReillyLearning from Data , Yaser Mostafa
Deep Learning, Yoshua Bengio
About: Contains everything you need to know about deep learning. So no need to refer anything else. First 5 chapters talk about foundational aspects of deep learning, not the core topic, like: statistics, probability, optimization, etc. From 6th chapter onwards, they'll go into deep learning, CNN, RNN, etc.
Pdf on GitHub
Online version
Applied Predictive Modeling
Fundamentals of Data Visualization , Claus Wilke | O'Reilly
About: Classic book on data visualization. You'll read this book multiple times, esp when trying to present data.
Extras: Pattern Recognition, Christopher Bishop
Extras: Probabilistic Machine Learning, Kevin Murphy
Machine Learning Papers
A Fast Learning Algorithm for Deep Belief Nets - Geoffrey Hinton, 2006
Shows how to train a deep neural network capable of recognizing handwritten digits with state-of-the-art precision (>98%)
Gradient-Based Learning Applied to Document Recognition - Yann LeCun, 1998ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton, 2012Playing Atari with Deep Reinforcement Learning - Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller, 2013Deep Residual Learning for Image Recognition - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, 2015BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, 2018Attention is All You Need - Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, 2017
Learning Resources
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