jshin49/deep-nlp-reading-list

Deep Learning / Machine Learning reading list - mainly related to NLP

Deep NLP Reading List

This serves as my own detailed roadmap and reading list/notes for studying Deep Learning and/with NLP. Each section will refer to useful materials that can help, including MOOCs, blog posts, books, lecture notes, papers, and other awesome paper lists and roadmaps.

Table of Contents

0. Mathematical Foundations
   • Basics
   • Advanced
1. Machine Learning
2. Deep Learning
3. Statistical NLP
4. Deep Learning for NLP
   • Text Classification
   • Word Embeddings
   • Question Answering
   • End-to-End Dialog
   • Neural Machine Translation
   • Multi-task Learning
   • Memory Augmented
   • Meta Learning

Mathematical Foundations

Basics

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If you are confident in these math subjects, you can just skip this part or simply take a look at some refreshers.

• Linear Algebra
  • Refreshers
    • Youtube Playlist: Essence of Linear Algebra
      • Approxmiately 2 hour long videos with Very Good Visualizations and clear explanations
    • Khan Academy Linear Algebra
  • MIT Linear Algebra
• Multivariable Calculus
  • Refreshers
    • Youtube Playlist: Essence of Calculus
    • Highlights of Calculus: Video lectures by Prof. Gilbert Strang, MIT
    • Khan Academy Multivariable Calculus
  • MIT Multivariable Calculus
• Probability and Statistics
  • Refreshers
    • Deep Learning Book Chapter 3 - Probability and Information Theory
    • Chapters 1, 2, and 11 of 'Pattern Recognition and Machine Learning' by Bishop (2006)
    • Khan Academy Probability and Statistics
  • Harvard STAT110
  • Readings
    • Chapters 2~6 of 'Machine Learning A Probabilistic Perspective' by Murphy (2012)
    • Lecture Notes: 'Probability and Statistics for Data Science'

Advanced

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The following subjects are some advanced materials that could be useful in understanding many Deep Learning theories and NLP. Particularly relevant ones are bolded.

• Information Theory
  • Refreshers
    • Deep Learning Book Chapter 3 - Probability and Information Theory
    • Khan Academy Information Theory
• Statistical Inference
  • CMU Intermediate Statistics
• Advanced Probability
• Random Matrix Theory
  • MIT Eigenvalues of Random Matrices
• Stochastic Processes
  • Coursera Stochastic Processes
  • MIT Discrete Stocahstic Processes
  • UIUC Notes on Random Processes
• Opimization Theory
• Convex Optimization
• Vector Calculus
• Numerical Linear Algebra
  • fast.ai Computational Linear Algebra: Focuses on applying what we learned from Linear Algebra to practical Data Science tasks.
• Abstract Algebra
• Real and Complex Analysis
• Theories of Deep Learning
  • Stanford STATS385

Machine Learning

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Machine Learning without Deep Learning.

• Refreshers
  • Kyunghyun Cho's ML w/o DL Lecture Notes
• Introductory
  • Andrew Ng's Machine Learning on Coursera
  • Yaser Abu-Mostafa's Learning From Data
• Advanced
  • Tom Mitchell's Machine Learning
  • CMU Intro to ML 10-701
  • CMU Advanced Intro to ML
• Readings
  • 'Pattern Recognition and Machine Learning' by Bishop (2006)
  • 'Machine Learning A Probabilistic Perspective' by Murphy (2012)

Deep Learning

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• Refreshers
  • Chapters 1, 2, 3, and 4 of Kyunghyun Cho's Natural Language Understanding with Distributed Representation Lecture Notes
  • Andrew Ng's Deep Learning courses deeplearning.ai
• CMU Introduction to Deep Learning
• Stanford CS231n Convolutional Neural Networks for Computer Vision
  • Youtube Playlist
• Books
  • Deep Learning Book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  • Neural Networks and Deep Learning by Michael Neilson
• Papers: Full list organized by topics and models can be found in Deep-Learning-Papers-Reading-Roadmap, or Columbia's seminar course Advanced Topics in Deep Learning - Reading List
  • Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444 [pdf]: A high-level survey paper by the three giants
  • Y. LeCun, L. Bottou, Y. Bengio and P. Haffner. "Gradient-Based Learning Applied to Document Recognition." Proceedings of the IEEE, 86(11):2278-2324. 1998 (Seminal Paper: LeNet) [pdf]: LeNet: Image Classification on Handwritten Digits
  • Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012. [pdf]: Big hit of Deep Learning, AlexNet
• Blog Posts
  • The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy
  • WildML Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs

Statistical NLP

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• Refreshers
  • Chapters 6~9.3 of Goldberg book
• Columbia Michael Collins' COMS W4705: Natural Language Processing: This course covers a lot of traditional techniques often used in NLP.
  • Notes on Statistical NLP
  • Video Lectures on Coursera: Can't find the course anymore, but there are Youtube videos
• Chris Manning's CS 224N/Ling 284 — Natural Language Processing before merging with Richard Socher's CS224D, covers some missing pieces of Michael Collins' class, along with more real life applications such as Machine Translation.
  • Video Lectures on Youtube
• Readings
  • 'Foundations of Statistical Natural Language Processing' by Manning and Schütze (1999)
  • Speech and Language Processing drafts by Dan Jurafsky and James Martin

Deep Learning for NLP

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Here I mainly organize papers I have read or plan to read. Among the ones I read, some accompany notes in a separate .md file linked.

• Overview
  • Goldberg, A Primer on Neural Network Models for Natural Language Processing
  • Kyunghyun Cho's Lecture Notes of Natural Language Understanding with Distributed Representation Lecture Notes
• Books
  • Goldberg
• Courses
  • Stanford CS224N Natural Language Processing with Deep Learning
    • The archived version for 2017 Winter Version
    • Youtube Playlist
  • Oxford Deep NLP
    • Youtube Playlist(Unofficial)
  • CMU CS 11-747 Neural Networks for NLP
    • Youtube Playlist](https://www.youtube.com/playlist?list=PL8PYTP1V4I8ABXzdqtOpB_eqBlVAz_xPT)

Text Classification

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• Abusive Language
• Sentiment Analysis

Word Embeddings

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• Language Modeling
• Contextualized Word Embeddings
• Probailistic Word Embeddings
• Interpretable Word Embeddings

Question Answering

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• SQuAD 1.0 Models

End-to-End Dialog

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• Goal-oriented
  • Dialog State Tracking
  • Latent Intents
  • Knowledge Base
  • Model Architectures
  • Datasets
  • Using RL
• Chit Chat

Neural Machine Translation

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• Multi-linguality

Multi-task Learning

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Memory Augmented

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• Memory Networks
• Pointer Networks
• Neural Turing Machines

Meta Learning

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• MAML