Programming
Software development: programming languages, coding, libraries and frameworks, software engineering and architecture, source control, developer tools, databases, and infrastructure.
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Software Heritage Graph Dataset doi.org
Graph of the development history and file metadata of >80 million software projects from various forges (GitHub, Gitlab, Debian, PyPI, Google Code, etc) in a deduplicated and unified representation (paper here).
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IEEE Transactions on Software Engineering computer.org
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CoREBench comp.nus.edu.sg
Collection of 70 realistically Complex Regression Errors that were systematically extracted from the repositories and bug reports of four open-source software projects: Make, Grep, Findutils, and Coreutils.
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Boa boa.cs.iastate.edu
Domain-specific language and infrastructure that eases mining software repositories.
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Directory of MSR Datasets authecesofteng.github.io
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TestMiner andrehora.github.io
Software testing analysis for GitHub repositories.
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Contributors github.com
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Eric Douglas github.com
founder of OSSU
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Elixir elixir-lang.org
, a new functional programming language for the web based on the battle-tested Erlang Virtual Machine!
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Growing Neat Software Architecture from Jupyter Notebooks youtube.com
A primer on how to structure your Machine Learning projects when using Jupyter Notebooks.
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Gradient Descent youtube.com
Okay, I already listed Andrew NG's Coursera class above, but this video especially is quite pertinent as an introduction and defines the gradient descent algorithm.
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Deep Learning Lecture 12: Recurrent Neural Nets and LSTMs youtube.com
Unfolding of RNN graphs is explained properly, and potential problems about gradient descent algorithms are exposed.
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Computer Science youtube.com
Yet another YouTube playlist I composed, this time about various CS topics.
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Signal Processing Playlist youtube.com
A YouTube playlist I composed about DFT/FFT, STFT and the Laplace transform - I was mad about my software engineering bachelor not including signal processing classes (except a bit in the quantum physics class).
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The real reason most ML projects fail towardsdatascience.com
Focus on clear business objectives, avoid pivots of algorithms unless you have really clean code, and be able to know when what you coded is "good enough".
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Gradient descent algorithms in a saddle point sebastianruder.com
Visualize how different optimizers interacts with a saddle points.
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Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source research.googleblog.com
Parsey McParseface's birth, a neural syntax tree parser.
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Neural Networks and Deep Learning, ch.2 neuralnetworksanddeeplearning.com
Overview on how does the backpropagation algorithm works.
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Window Functions en.wikipedia.org
Wikipedia page that lists some of the known window functions - note that the Hann-Poisson window is specially interesting for greedy hill-climbing algorithms (like gradient descent for example).
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Gradient descent algorithms in an almost flat landscape devblogs.nvidia.com
Visualize how different optimizers interacts with an almost flat landscape.
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Uncle Bob's Principles Of OOD butunclebob.com
Not only the SOLID principles are needed for doing clean code, but the furtherless known REP, CCP, CRP, ADP, SDP and SAP principles are very important for developping huge software that must be bundled in different separated packages.
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Some other books I have read books.google.ca
Some books listed here are less related to deep learning but are still somehow relevant to this list.
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Neural Turing Machines arxiv.org
Outstanding for letting a neural network learn an algorithm with seemingly good generalization over long time dependencies. Sequences recall problem.
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SentEval: An Evaluation Toolkit for Universal Sentence Representations arxiv.org
A Python framework to benchmark your sentence representations on many datasets (NLP tasks).
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ParlAI: A Dialog Research Software Platform arxiv.org
Another Python framework to benchmark your sentence representations on many datasets (NLP tasks).
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Clean Coder amazon.ca
Learn how to be professional as a coder and how to interact with your manager. This is important for any coding career.
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TensorFlow Lite for Android (Coding TensorFlow) youtube.com
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TensorLayerX tensorlayerx.readthedocs.io
Using TensorFlow like PyTorch. (Api docs)
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R Interface to TensorFlow tensorflow.rstudio.com
R interface to TensorFlow APIs, including Estimators, Keras, Datasets, etc.
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Tensorflow-Probability tensorflow.org
Probabilistic programming built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware.
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TensorFlow Estimators tensorflow.org
high-level TensorFlow API that greatly simplifies machine learning programming (originally tensorflow/skflow)
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Old and New algorithm for Blind Deconvolution videolectures.net
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Optimization Algorithms in Machine Learning videolectures.net
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TensorFlow 2.0 in Action manning.com
by Thushan Ganegedara. This practical guide to building deep learning models with the new features of TensorFlow 2.0 is filled with engaging projects, simple language, and coverage of the latest algorithms.
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Deep Learning with Python machinelearningmastery.com
Develop Deep Learning Models on Theano and TensorFlow Using Keras by Jason Brownlee
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Understanding The Tensorflow Estimator API lighttag.io
A conceptual overview of the Estimator API, when you'd use it and why.
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Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source googleresearch.blogspot.com
Release of SyntaxNet, "an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding systems.
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Practical Python and OpenCV pyimagesearch.com
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Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics people.csail.mit.edu
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Flickr Material Database people.csail.mit.edu
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Explicit-Formulas Database hyperelliptic.org
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RSA Algorithm di-mgt.com.au
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Software implementation of the NIST elliptic curves over prime fields delta.cs.cinvestav.mx
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TensorLayer: A Versatile Library for Efficient Deep Learning Development arxiv.org
This paper describes a versatile Python library that aims at helping researchers and engineers efficiently develop deep learning systems. (Winner of The Best Open Source Software Award of ACM MM 2017)
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Comparative Study of Deep Learning Software Frameworks arxiv.org
The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings
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A Gentle Tutorial of the EM Algorithm nikola-rt.ee.washington.edu
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