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Best of ML

Contents

  • Articles
  • Books
  • MOOCs
  • Tools
  • Data
  • Cheat Cheats
  • Lists
  • Miscallenious

This post is a summary of articles, websites and material in general about machine learning.

Articles

  • RNNs
    • Get an overview: The Unreasonable Effectiveness of Recurrent Neural Networks
    • Understand them: Understanding LSTM Networks
  • Using convolutional neural nets to detect facial keypoints tutorial
  • Clever Methods of Overfitting
  • Understanding the Bias-Variance Tradeoff
  • An overview of gradient descent optimization algorithms
  • cs231n: Convolutional Neural Networks (CNNs / ConvNets) (YouTube playlist)
  • Evolution Strategies
  • Warning Signs in Experimental Design and Interpretation: Not the typical ML literature, but interesting and relevant non the less as ML is driven by experiments.

Books

  • Neural Networks and Deep Learning
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning

MOOCs

  • Coursera: Machine Learning by Andrew Ng
  • CS224d: Deep Learning for Natural Language Processing
  • Machine Learning: Kurs der Universität Oxford
  • Convolutional Neural Networks for Visual Recognition: Kurs von Stanford

Tools

  • Caffe: Used often for Computer Vision, but more and more people jump to TensorFlow
  • sklearn: Python Machine learning toolkit
  • Theano: Used often for Speech Recognition
    • Lasagne: Python, supports nVidia GPU training of neural networks
      • nolearn
  • TensorFlow: C++ and Python, supports nVidia GPU training of neural networks
    • Keras.io: Extremely nice for beginners

Data

Collections

  • OpenML: A lot of datasets (it also has a Python package)
  • Kaggle

Benchmark Datasets

  • MNIST: 70 000 images of $28 \times 28$ px with labels (digits 0-9)
  • HASY: 168 233 images of $32 \times 32$ px with labels (369 classes, all of them are characters)
  • HWRT: Handwritten symbols (similar to HASY, but online data)
  • IRIS: 3 classes, 50 items per class, 3 features per item
  • KITTI: Road vision dataset

Lists:

  • metacademy.org: A lot of material when you know what to look for
  • computervisiononline.com: Eine Liste sehr vieler Datensätze
  • YACVID: Computer Vision Index To Datasets
  • dmoz.org

Cheat Cheats

  • Choosing the right estimator
  • Machine learning algorithm cheat sheet

Lists

  • Machine Learning Tutorials by Ujjwal Karn (Facebook employee)
  • Awesome Random Forest: A curated list of resources regarding tree-based methods and more, including but not limited to random forest, bagging and boosting.

Miscallenious

  • Kaggle: Machine Learning Challenges
  • Stack Exchange
    • datascience.stackexchange.com
    • stats.stackexchange.com
  • awesome-machine-learning: A list with MANY links to machine learning tools
  • Demos:
    • Neural Machine Translation: English → German, French
    • write-math.com: Symbol recognition
    • Tensorflow Playground: Demo for decision boundary of neural network
    • lecture-demo.ira.uka.de: Rosenblatt-Perceptron, GMMs, ...
    • demos.algorithmia.com/colorize-photos: Colorize a grayscale photo

Published

Feb 13, 2017
by Martin Thoma

Category

Machine Learning

Tags

  • Machine Learning 81

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