Machine Learning Awesome List

Maintainer - sdukshis

Table of Contents

• Code
• Courses
• Videos
• Conferences
• Papers
• Thesis
• Books
• Journals
• Datasets
• Blogposts
• Competitions

Code

• Neural Networks
  • Theano - Python
  • TensorFlow - Python, C++
  • Lasagne - Python
  • Keras - Python
  • mxnet - Python, C++, R, Scala, Julia, Go, Matlab, js
  • Caffe - Python, C++
  • cuDNN - C++
  • Requests for research
• scikit-learn - Python
• Rodeo - Python
• BigARTM - Python

Courses

• Machine Learning
  • Машинное обучение (К.В.Воронцов)
  • Введение в машинное обучение (К.В. Воронцов)
  • Machine Learning (Andrew Ng)
  • Advanced Statistical Computing
• Neural Networks
  • CS231n Convolutional Neural Networks for Visual Recognition by Andrej Karpathy
  • Глубинное обучение (курс лекций)

Videos

• Recurrent neural networks
  • Recurrent Neural Networks by Y. Bengio

Conferences

• Neural Information Processing Systems (NIPS) - site
• International Conference on Learning Representations (ICLR) - site
• International Conference on Machine Learning (ICML) - site

Papers

• Neural Networks

  • Feed-forward networks
    • Approximation by Superpositions of a Sigmoidal Function, G, Cybenko - Paper
  • Deep learning
    • Learning multiple layers of representation, Geoffrey Hinton, 2007 - Paper
    • Learning Deep Architectures for AT, Yoshua Bengio - Paper
    • Dropout: A Simple Way to Prevent Neural Networks from Overfitting, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov - Paper
    • Reducing the Dimensionality of Data with Neural Networks, G. E. Hinton* and R. R. Salakhutdinov - Paper
    • A theoretical framework for deep transfer learning, T. Galanti, L. Wolf, and T. Hazan - Paper
    • Comparative Study of Deep Learning Software Frameworks, Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Moh ak Shah - Paper
    • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe, Christian Szegedy - [Paper](Sergey Ioffe, Christian Szegedy)
  • Recurrent Networks
    • Long Short-Term Memory, S. Hochreiter, J. Schmidhuber, 1997 - Paper
    • Long Short Term Memory Networks for Anomaly Detection in Time Series, P. Malhotra, L. Vig, G. Shroff, P. Agarwal, 2015 - Paper
    • A Clockwork RNN, Jan Koutnik, Klaus Greff, Faustino Gomez, Jürgen Schmidhuber, 2014 - Paper
    • Sequence Labelling in Structured Domains with Hierarchical Recurrent Neural Networks, Santiago Fernandez, Alex Graves, Jurgen Schmidhuber, 2007 - Paper
    • Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks, Alex Graves, Santiago Fernandez, Faustino Gomez, Jurgen Schmidhuber, 2006 - Paper
    • Learning Long-Term Dependencies with Gradient Descent is Difficult, Y. Bengio, P. Simard, and P. Frasconi, 1994 - Paper
    • Character-Aware Neural Language Models, Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush - Paper
    • Leaning longer memory in recurrent neural networks, Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu & Marc’Aurelio Ranzato - Paper
    • Recurrent neural network regularization, Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals - Paper
    • Learning to forget: continual prediction with LSTM, Felix Gers, Jurgen Schmidhuber, 1999 - Paper
    • Unitary Evolution Recurrent Neural Networks, Martin Arjovsky, Amar Shah, Yoshua Bengio, 2015 - Paper
    • Visualizing and understanding recurrent networks, Andrej Karpathy, Justin Johnson, Li Fei-Fei - Paper
    • Deep Recurrent Neural Networks for Time- Series Prediction, Sharat C. Prasad, Piyush Prasad - Paper
    • Synthesis of recurrent neural networks for dynamical system simulation, Adam Trischler, Gabriele MT D'Eleuterio - Paper
    • A Recurrent Neural Network for Modelling Dynamical Systems, Coryn A.L. Bailer-Jones , David J.C. MacKay - Paper
    • Approximation of Dynamical Time-Variant Systems by Continuous-Time Recurrent Neural Networks, Xiao-Dong Li, John K. L. Ho, and Tommy W. S. Chow - Paper
    • Approximation of Discrete-Time State-Space 12.ajectories Using Dynamic Recurrent Neural Networks, Liang Jin, Peter N. Nikiforuk, and Madan M. Gupta - Paper
    • Predictive Business Process Monitoring with LSTM Neural Networks, Niek Tax1, Ilya Verenich2,3, Marcello La Rosa2, and Marlon Dumas - Paper
  • Convolutional Neural Networks
    • Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks, Yi Zheng, Qi Liu, Enhong Chen, Yong Ge, and J Lean Zhao, 2014 - Paper
    • Convolutional Networks for Images, Speech, and Time-Series, Yann Lecun, Yoshua Bengio - Paper
    • Understanding Convolutional Neural Networks, Jayanth Koushik - Paper
    • Deep learning, Yann LeCun, Yoshua Bengio, andGeoffrey Hinton - Paper

• Time Series Anomaly Detection

  • SAX
    • HOT SAX: Finding the Most Unusual Time Series Subsequence: Algorithms and Applications, Eamonn Keogh, Jessica Lin, Ada Fu, 2005 - Paper, Materials
  • LSTM
    • LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection, Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, 2016 - Paper
    • Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks, Bénard Wiese and Christian Omlin, 2009 - Springer
    • Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection, Jihyun Kim, Jaehyun Kim, Huong Le Thi Thu, and Howon Kim - Paper
    • Deep Recurrent Neural Network-based Autoencoders for Acoustic Novelty Detection, Erik Marchi Fabio Vesperini, Stefano Squartini, and Bjo ̈rn Schuller - Paper
    • A Novel Approach for Automatic Acoustic Novelty Detection Using a Denoising Autoencoder with Bidirectional LSTM Neural Networks, Erik Marchi, Fabio Vesperini, Florian Eyben, Stefano Squartini, Bjo ̈rn Schuller - Paper
  • Transfer learning
    • Transfer Representation-Learning for Anomaly Detection, Jerone T. A. Andrews, Thomas Tanay, Edward J. Morton, Lewis D. Griffin, 2016 - Paper
  • Anomaly Detection Based on Sensor Data in Petroleum Industry Applications, Luis Martí,1, Nayat Sanchez-Pi, José Manuel Molina, and Ana Cristina Bicharra Garcia - Paper
  • Anomaly detection in aircraft data using recurrent nueral networks (RNN), Anvardh Nanduri, Lance Sherry - Paper
  • Bayesian Online Changepoint Detection, Ryan Prescott Adams, David J.C. MacKay - Paper
  • Anomaly Detection in Aviation Data using Extreme Learning Machines, Vijay Manikandan Janakiraman, David Nielsen - Paper

• Clustering

  • Consistent Algorithms for Clustering Time Series, Azadeh Khaleghi, Daniil Ryabko, Jeremie Mary, Philippe Preux, 2016 - Paper

Thesis

• Statistical Language Models based on Neural Networks by Tomas Mikolov
• Time Series Prediction Using Neural Networks by Karol Kuna, 2015
• TRAINING RECURRENT NEURAL NETWORKS by Ilya Sutskever, 2013
• Anomaly Detection of Time Series
• Unsupervised Anomaly Detection in Sequences Using Long Short Term Memory Recurrent Neural Networks

Books

• Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis - Amazon, Safari
• Fundamentals of Deep Learning, Nikhil Buduma - Safari
• Rank Based Anomaly Detection Algorithms - Book
• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - Book
• Foundations of Data Science, Avrim Blum, John Hopcroft and Ravindran Kannan - Book

Journals

• Journal of Machine Learning Research
• Machine Learning and Data Analysis
• Machine Learning
• International Journal of Machine Learning and Cybernetics
• Data Mining and Knowledge Discovery
• Intelligent Data Analysis
• Интеллектуальные системы
• Artificial Intelligence
• Artificial Intelligence Review
• Engineering Applications of Artificial Intelligence

Datasets

• PhysioBank
• UCR Time Series Classification Archive
• NASA Shuttle Valve Data
• Yahoo Labeled Anomaly Detection Dataset
• Awesome Public Datasets
• The Numenta Anomaly Benchmark Competition For Real-Time Anomaly Detection
• An archive of datasets distributed with R
• List of datasets for machine learning research
• Disk Defect Data
• Hard Drive Data and Stats
• FMA: A Dataset For Music Analysis

Blogposts

• Estimating Rainfall From Weather Radar Readings Using Recurrent Neural Networks
• Theoretical Motivations for Deep Learning
• CMU Graphics Lab Motion Capture Database
• Common Objects in Context
• Neural Network Architectures
• Building powerful image classification models using very little data
• Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras
• Written Memories: Understanding, Deriving and Extending the LSTM
• Time Series Prediction With Deep Learning in Keras

Competitions

• Senior Data Science: Safe Aging with SPHERE
• ML Boot Camp