This is a collection of technical books and papers that I've enjoyed. I can't guarantee that all of these books have been acquired in a lawful way. Have fun.

• math/stats
  • Mathematics for Machine Learning
  • The Princeton Companion to Mathematics
  • Introduction to Mathematical Statistics
  • Linear Algebra Done Right
  • Doing Math with Python
  • Introduction to Statistics with Python
  • Elements of Information Theory
  • Mathematical Statistics and Data Analysis
  • Probability and Random Variables
  • Bayesian Reasoning and Machine Learning
• artificial intelligence
  • NLTK Book
  • Hands on Machine Learning
  • Handbook of Natural Language Processing
  • Deep Learning Book
  • Speech and Language Processing (https://web.stanford.edu/~jurafsky/slp3/)
  • Zero to Deep Learning
  • Artificial Intelligence: A Modern Approach
  • Elements of Statistical Learning
  • Programming Collective Intelligence
  • Introduction to Machine Learning
  • Building Machine Learning Systems with Python
  • Reinforcement Learning: An Introduction
  • Think Python
• data science
  • Python for Data Analysis
  • Python End-to-End Data Analysis
  • Doing Data Science
  • Data Science at the Command Line
  • SQL Cookbook
  • Web Scraping with Python
  • Building Real Time Data Platforms
  • Data Science and Complex Networks
• networks
  • Flask Web Development
  • CCNA Electronic Book
  • Hacking: The Art of Exploitation
  • Internet Routing Architectures
  • Communications and Networking
• computer science
  • Code
  • Grokking Algorithms
  • Cracking the Coding Interview
  • Data Structures and Algorithms in Python
  • The Structure and Interpretation of Computer Programs
  • Introduction to Algorithms
  • Programming Languages: Application and Interpretation
• operating systems
  • Unix for Poets
  • How Linux Works
  • The Linux Programming Interface
• programming
  • Mastering Python Regular Expressions
  • Effective Python
  • Fluent Python
  • Python Cookbook
  • Accelerated C++
  • JavaScript: the Definitive Guide

A simple but hard-to-beat baseline for sentence embeddings. Sanjeev Arora, Yingyu Liang, Tengyu Ma (2017). link

Describes how taking a weighted average of the word vectors in a sentence can produce high-quality sentence embeddings.s

Learning without forgetting. Zhizhong Li, Derek Hoiem (2016). link

Describes different strategies for transfer learning: fine-tuning, feature extraction, joint training, and the paper's new strategy: "learning without forgetting". This tries to solve the problem of "continually adding new prediction tasks based on adapting shared parameters without access to training data for previously learned tasks"

Deep Face Recognition: A Survey. Mei Wang, Weihong Deng (2019). link

Learning Multi-scale Features for Foreground Segmentation. Long Ang Lim, Hacer Yalim Keles (2018). link

Xception: Deep Learning with Depthwise Separable Convolutions. Francois Chollet (2017). link

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Sergey Ioffe, Christian Szegedy (2015). link

Universal Language Model Fine-tuning for Text Classification. Jeremy Howard, Sebastian Ruder (2018). link

Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov (2014). link

The Unreasonable Effectiveness of Recurrent Neural Networks. Andrej Karpathy (2015). link

How transferable are features in deep neural networks? Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson (2014). link

Patent Claim Generation by Fine-Tuning OpenAI GPT-2. Jieh-Sheng Lee and Jieh Hsiang (2019). link

Deep Facial Expression Recognition: A Survey. Shan Li and Weihong Deng (2018). link

Challenges in Representation Learning: A report on three machine learning contests. Goodfellow et al (2013). link

An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models. Alexandra Chronopoulou, Christos Baziotis, Alexandros Potamianos (2019). link

Attention Is All You Need. Ashish Vaswani et al (2017). link

A Selective Overview of Deep Learning. Jianqing Fan, Cong Ma, Yiqiao Zhong (2019). link

Self-Normalizing Neural Networks. Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter (2017). link

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall, Yarin Gal (2017). link

Learning to Reinforcement Learn. Wang et al (2017). link

Language Models are Unsupervised Multitask Learners. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever (2019). link

Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP Tasks. Steffen Eger, Paul Youssef, Iryna Gurevych (2018). link

Deep Residual Learning for Image Recognition. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun (2015). link

YOLOv3: An Incremental Improvement. Joseph Redmon, Ali Farhadi (2018). link

Universal Transformers. Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser (2019). link

R-net: Machine Reading Comprehension with Self-Matching Networks. Natural Language Computing Group, Microsoft Research Asia (2017). link

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova (2019). link

Improving Language Understanding by Generative Pre-Training. Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever (2018). link

Deep contextualized word representations. Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer (2018). link

ImageNet: A Large-Scale Hierarchical Image Database. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei (2009) link

ImageNet Classification with Deep Convolutional Neural Networks. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton (2013). link

A State-of-the-Art Survey on Deep Learning Theory and Architectures. Md Zahangir Alom et al (2019). link

Understanding deep learning requires rethinking generalization. Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals (2017). link

A Closer Look at Memorization in Deep Networks. Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, Simon Lacoste-Julien (2017). link

Probing Biomedical Embeddings from Language Models. Qiao Jin, Bhuwan Dhingra, William W. Cohen, Xinghua Lu (2019). link

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra (2017). link.

CNN Features off-the-shelf: an Astounding Baseline for Recognition. Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, Stefan Carlsson (2014). link

Know What You Don't Know: Unanswerable Questions for SQuAD. Pranav Rajpurkar, Robin Jia, Percy Liang (2018). link

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. Shaojie Bai, J. Zico Kolter, Vladlen Koltun (2018). link