VMP
Video Deep learning
Comparison_of_deep_learning_software
Awesome-deep-learning-papers
Awesome Recurrent Neural Networks
Awesome Deep Vision
Deep Learning Papers Reading Roadmap
Oxford Deep NLP 2017 course
Tensorflow
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation
Keras Deep Learning library for Python. Runs on TensorFlow, Theano or CNTK
OpenCL Caffe
Caffe: a fast open framework for deep learning
Caffe is a deep learning framework made with expression, speed, and modularity in mind
Forward and Backward
Caffe Tutorial
Coriander Build NVIDIA® CUDA™ code for OpenCL™ 1.2 devices
The LLVM Compiler Infrastructure
Thrust is a parallel algorithms library which resembles the C++ Standard Template Library (STL).
CUDA-on-CL: a compiler and runtime for running NVIDIA® CUDA™ C++11 applications on OpenCL™ 1.2 Devices
Yann LeCun
The Jupyter Notebook
Анализ временных рядов с помощью Python
Визуализация данных c Python
Time Series Analysis (TSA) in Python – Linear Models to GARCH
Keras: The Python Deep Learning library
Keras Models
A Peek at Trends in Machine Learning
Keras Библиотеки для глубокого обучения
Installing TensorFlow for Java
Библиотека глубокого обучения Tensorflow
TensorFlow. Библиотека машинного обучения от Google
TensorFlow-Examples
TensorFlow Tutorial and Examples for beginners
Data mining: Инструментарий — Theano
Нейронные сети: практическое применение
Введение в машинное обучение с помощью Python и Scikit-Learn
The Open Images dataset
Datasets
Deep Learning Datasets
ImageNet is an image database
Image Databases
DataSet
FMA: A Dataset For Music Analysis
The CIFAR-10 dataset
CIFAR-10 – Object Recognition in Images
CIFAR-10 – Object Recognition in Images
Alex’s CIFAR-10 tutorial, Caffe style
92.45% on CIFAR-10 in Torch
Cuda
NVIDIA cuDNN
CUDA Toolkit
Deep Learning Software
Accelerated Computing Toolkit
CLBlast
Coriander Build NVIDIA® CUDA™ code for OpenCL™ 1.2 devices
tf-coriander OpenCL 1.2 implementation for Tensorflow
Deep Learning Tutorial notes and code
Deep Learning Tutorials
A list of popular github projects related to deep learning
Deep learning library featuring a higher-level API for TensorFlow.
cltorch
DeepCL
How-to-use-gpu-with-theano
Caffe installation
Easily craft fast Neural Networks on iOS! Use TensorFlow models
Apple Build more intelligent apps with machine learning
Apple Integrating a Core ML Model into Your App
coremltools 0.3.0 Community Tools for CoreML. Core ML is an Apple framework which allows developers to simply and easily integrate machine learning (ML) models into apps running on Apple devices
Pyopencl
clBLAS
NumPy
Python Numpy Tutorial
numpy 1.13.0
Obtaining NumPy & SciPy libraries
Scipy Lecture Notes One document to learn numerics, science, and data with Python
Learn Python Programming
Python Programming Examples
Учите Питон
ROCm, a New Era in Open GPU Computing
ROCm – Open Source Platform for HPC and Ultrascale GPU Computing
yusugomori DeepLearning
neural-style
Базовые принципы машинного обучения на примере линейной регрессии
Искусственный интеллект Путина довел Америку до истерики
Datasets
These datasets can be used for benchmarking deep learning algorithms:
Music Datasets
- Piano-midi.de: classical piano pieces (http://www.piano-midi.de/)
- Nottingham : over 1000 folk tunes (http://abc.sourceforge.net/NMD/)
- MuseData: electronic library of classical music scores (http://musedata.stanford.edu/)
- JSB Chorales: set of four-part harmonized chorales (http://www.jsbchorales.net/index.shtml)
- FMA: A Dataset For Music Analysis (https://github.com/mdeff/fma)
Natural Images
- MNIST: handwritten digits (http://yann.lecun.com/exdb/mnist/)
- NIST: similar to MNIST, but larger
- Perturbed NIST: a dataset developed in Yoshua’s class (NIST with tons of deformations)
- CIFAR10 / CIFAR100: 32×32 natural image dataset with 10/100 categories ( http://www.cs.utoronto.ca/~kriz/cifar.html)
- Caltech 101: pictures of objects belonging to 101 categories (http://www.vision.caltech.edu/Image_Datasets/Caltech101/)
- Caltech 256: pictures of objects belonging to 256 categories (http://www.vision.caltech.edu/Image_Datasets/Caltech256/)
- Caltech Silhouettes: 28×28 binary images contains silhouettes of the Caltech 101 dataset
- STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. http://www.stanford.edu/~acoates//stl10/
- The Street View House Numbers (SVHN) Dataset – http://ufldl.stanford.edu/housenumbers/
- NORB: binocular images of toy figurines under various illumination and pose (http://www.cs.nyu.edu/~ylclab/data/norb-v1.0/)
- Imagenet: image database organized according to the WordNethierarchy (http://www.image-net.org/)
- Pascal VOC: various object recognition challenges (http://pascallin.ecs.soton.ac.uk/challenges/VOC/)
- Labelme: A large dataset of annotated images, http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php
- COIL 20: different objects imaged at every angle in a 360 rotation(http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php)
- COIL100: different objects imaged at every angle in a 360 rotation (http://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php)
Artificial Datasets
- Arcade Universe – An artificial dataset generator with images containing arcade games sprites such as tetris pentomino/tetromino objects. This generator is based on the O. Breleux’s bugland dataset generator.
- A collection of datasets inspired by the ideas from BabyAISchool:
- BabyAIShapesDatasets : distinguishing between 3 simple shapes
- BabyAIImageAndQuestionDatasets : a question-image-answer dataset
- Datasets generated for the purpose of an empirical evaluation of deep architectures (DeepVsShallowComparisonICML2007):
- MnistVariations : introducing controlled variations in MNIST
- RectanglesData : discriminating between wide and tall rectangles
- ConvexNonConvex : discriminating between convex and nonconvex shapes
- BackgroundCorrelation : controlling the degree of correlation in noisy MNIST backgrounds
Faces
- Labelled Faces in the Wild: 13,000 images of faces collected from the web, labelled with the name of the person pictured (http://vis-www.cs.umass.edu/lfw/)
- Toronto Face Dataset
- Olivetti: a few images of several different people (http://www.cs.nyu.edu/~roweis/data.html)
- Multi-Pie: The CMU Multi-PIE Face Database (http://www.multipie.org/)
- Face-in-Action (http://www.flintbox.com/public/project/5486/)
- JACFEE: Japanese and Caucasian Facial Expressions of Emotion (http://www.humintell.com/jacfee/)
- FERET: The Facial Recognition Technology Database (http://www.itl.nist.gov/iad/humanid/feret/feret_master.html)
- mmifacedb: MMI Facial Expression Database (http://www.mmifacedb.com/)
- IndianFaceDatabase: http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/)
- (e.g. The Yale Face Database (http://vision.ucsd.edu/content/yale-face-database) and The Yale Face Database B (http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html)).
Text
- 20 newsgroups: classification task, mapping word occurences to newsgroup ID (http://qwone.com/~jason/20Newsgroups/)
- Reuters (RCV*) Corpuses: text/topic prediction (http://about.reuters.com/researchandstandards/corpus/)
- Penn Treebank : used for next word prediction or next character prediction (http://www.cis.upenn.edu/~treebank/)
- Broadcast News: large text dataset, classically used for next word prediction (http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC97S44)
- Wikipedia Dataset
- Multidomain sentiment analysis dataset: http://www.cs.jhu.edu/~mdredze/datasets/sentiment/
Speech
- TIMIT Speech Corpus: phoneme classification (http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC93S1)
- Aurora : Timit with noise and additional information
Recommendation Systems
- MovieLens: Two datasets available from http://www.grouplens.org. The first dataset has 100,000 ratings for 1682 movies by 943 users, subdivided into five disjoint subsets. The second dataset has about 1 million ratings for 3900 movies by 6040 users.
- Jester: This dataset contains 4.1 million continuous ratings (-10.00 to +10.00) of 100 jokes from 73,421 users.
- Netflix Prize: Netflix released an anonymised version of their movie rating dataset; it consists of 100 million ratings, done by 480,000 users who have rated between 1 and all of the 17,770 movies.
- Book-Crossing dataset: This dataset is from the Book-Crossing community, and contains 278,858 users providing 1,149,780 ratings about 271,379 books.
Misc
- “Musk” dataset
- CMU Motion Capture Database: (http://mocap.cs.cmu.edu/)
- Brodatz dataset: texture modeling (http://www.ux.uis.no/~tranden/brodatz.html)
- Million Song dataset: http://labrosa.ee.columbia.edu/millionsong/
- Merck Molecular Activity Challenge – http://www.kaggle.com/c/MerckActivity/data
- MIT CSAIL LabelMe, open annotation tool related tech report
- PASCAL Visual Object Classes challenges (2005-2007)
- Wordnet
- Caltech101
- Caltech256
- TREC Video Retrieval Evaluation
- Oxford buildings dataset
- Photo-tourism patches
- UIUC Car detection dataset
- CMU Face databases
- Animals on the Web data
- ETH-80
- Graz 02
- MIT Objects and Scenes
- NYU NORB dataset
- Columbia COIL
- Oxford flowers dataset
- SFU activity dataset (sports)
- Princeton events dataset
- Weizmann activity videos
- MIRFlickr dataset
- Data collections of detected faces, from Oxford VGG
- Face data from Buffy episode, from Oxford VGG
- University of Cambridge face data from films [go to Data link]
- Reuters
- Dataset list from the Computer Vision Homepage
- Image Parsing
- Various other datasets from the Oxford Visual Geometry group
- INRIA Holiday images dataset
- Movie human actions dataset from Laptev et al.
- ESP game dataset
- NUS-WIDE tagged image dataset of 269K images
- Bastian Leibe’s dataset page: pedestrians, vehicles, cows, etc.
- UMass Labeled Faces in the Wild
- FaceTracer database from Columbia
- Daimler Pedestrian Benchmark Datasets
- CUHK Search Reranking Dataset
- Leeds Butterfly Dataset
- Caltech-UCSD Birds Dataset
- Первичный анализ данных с Pandas
- Визуальный анализ данных c Python
- Классификация, деревья решений и метод ближайших соседей
- Линейные модели классификации и регрессии
- Композиции: бэггинг, случайный лес
- Построение и отбор признаков
- Обучение без учителя: PCA, кластеризация
- Обучение на гигабайтах c Vowpal Wabbit
- Анализ временных рядов с помощью Python
- Градиентный бустинг
clBLAS
NumPy
Python Numpy Tutorial
numpy 1.13.0
Obtaining NumPy & SciPy libraries
Scipy Lecture Notes One document to learn numerics, science, and data with Python
Learn Python Programming
Python Programming Examples
Учите Питон
ROCm, a New Era in Open GPU Computing
ROCm – Open Source Platform for HPC and Ultrascale GPU Computing
yusugomori DeepLearning
Искусственный интеллект Путина довел Америку до истерики
Datasets
These datasets can be used for benchmarking deep learning algorithms:
Music Datasets
- Piano-midi.de: classical piano pieces (http://www.piano-midi.de/)
- Nottingham : over 1000 folk tunes (http://abc.sourceforge.net/NMD/)
- MuseData: electronic library of classical music scores (http://musedata.stanford.edu/)
- JSB Chorales: set of four-part harmonized chorales (http://www.jsbchorales.net/index.shtml)
- FMA: A Dataset For Music Analysis (https://github.com/mdeff/fma)
Natural Images
- MNIST: handwritten digits (http://yann.lecun.com/exdb/mnist/)
- NIST: similar to MNIST, but larger
- Perturbed NIST: a dataset developed in Yoshua’s class (NIST with tons of deformations)
- CIFAR10 / CIFAR100: 32×32 natural image dataset with 10/100 categories ( http://www.cs.utoronto.ca/~kriz/cifar.html)
- Caltech 101: pictures of objects belonging to 101 categories (http://www.vision.caltech.edu/Image_Datasets/Caltech101/)
- Caltech 256: pictures of objects belonging to 256 categories (http://www.vision.caltech.edu/Image_Datasets/Caltech256/)
- Caltech Silhouettes: 28×28 binary images contains silhouettes of the Caltech 101 dataset
- STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. http://www.stanford.edu/~acoates//stl10/
- The Street View House Numbers (SVHN) Dataset – http://ufldl.stanford.edu/housenumbers/
- NORB: binocular images of toy figurines under various illumination and pose (http://www.cs.nyu.edu/~ylclab/data/norb-v1.0/)
- Imagenet: image database organized according to the WordNethierarchy (http://www.image-net.org/)
- Pascal VOC: various object recognition challenges (http://pascallin.ecs.soton.ac.uk/challenges/VOC/)
- Labelme: A large dataset of annotated images, http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php
- COIL 20: different objects imaged at every angle in a 360 rotation(http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php)
- COIL100: different objects imaged at every angle in a 360 rotation (http://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php)
Artificial Datasets
- Arcade Universe – An artificial dataset generator with images containing arcade games sprites such as tetris pentomino/tetromino objects. This generator is based on the O. Breleux’s bugland dataset generator.
- A collection of datasets inspired by the ideas from BabyAISchool:
- BabyAIShapesDatasets : distinguishing between 3 simple shapes
- BabyAIImageAndQuestionDatasets : a question-image-answer dataset
- Datasets generated for the purpose of an empirical evaluation of deep architectures (DeepVsShallowComparisonICML2007):
- MnistVariations : introducing controlled variations in MNIST
- RectanglesData : discriminating between wide and tall rectangles
- ConvexNonConvex : discriminating between convex and nonconvex shapes
- BackgroundCorrelation : controlling the degree of correlation in noisy MNIST backgrounds
Faces
- Labelled Faces in the Wild: 13,000 images of faces collected from the web, labelled with the name of the person pictured (http://vis-www.cs.umass.edu/lfw/)
- Toronto Face Dataset
- Olivetti: a few images of several different people (http://www.cs.nyu.edu/~roweis/data.html)
- Multi-Pie: The CMU Multi-PIE Face Database (http://www.multipie.org/)
- Face-in-Action (http://www.flintbox.com/public/project/5486/)
- JACFEE: Japanese and Caucasian Facial Expressions of Emotion (http://www.humintell.com/jacfee/)
- FERET: The Facial Recognition Technology Database (http://www.itl.nist.gov/iad/humanid/feret/feret_master.html)
- mmifacedb: MMI Facial Expression Database (http://www.mmifacedb.com/)
- IndianFaceDatabase: http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/)
- (e.g. The Yale Face Database (http://vision.ucsd.edu/content/yale-face-database) and The Yale Face Database B (http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html)).
Text
- 20 newsgroups: classification task, mapping word occurences to newsgroup ID (http://qwone.com/~jason/20Newsgroups/)
- Reuters (RCV*) Corpuses: text/topic prediction (http://about.reuters.com/researchandstandards/corpus/)
- Penn Treebank : used for next word prediction or next character prediction (http://www.cis.upenn.edu/~treebank/)
- Broadcast News: large text dataset, classically used for next word prediction (http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC97S44)
- Wikipedia Dataset
- Multidomain sentiment analysis dataset: http://www.cs.jhu.edu/~mdredze/datasets/sentiment/
Speech
- TIMIT Speech Corpus: phoneme classification (http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC93S1)
- Aurora : Timit with noise and additional information
Recommendation Systems
- MovieLens: Two datasets available from http://www.grouplens.org. The first dataset has 100,000 ratings for 1682 movies by 943 users, subdivided into five disjoint subsets. The second dataset has about 1 million ratings for 3900 movies by 6040 users.
- Jester: This dataset contains 4.1 million continuous ratings (-10.00 to +10.00) of 100 jokes from 73,421 users.
- Netflix Prize: Netflix released an anonymised version of their movie rating dataset; it consists of 100 million ratings, done by 480,000 users who have rated between 1 and all of the 17,770 movies.
- Book-Crossing dataset: This dataset is from the Book-Crossing community, and contains 278,858 users providing 1,149,780 ratings about 271,379 books.
Misc
- “Musk” dataset
- CMU Motion Capture Database: (http://mocap.cs.cmu.edu/)
- Brodatz dataset: texture modeling (http://www.ux.uis.no/~tranden/brodatz.html)
- Million Song dataset: http://labrosa.ee.columbia.edu/millionsong/
- Merck Molecular Activity Challenge – http://www.kaggle.com/c/MerckActivity/data
- MIT CSAIL LabelMe, open annotation tool related tech report
- PASCAL Visual Object Classes challenges (2005-2007)
- Wordnet
- Caltech101
- Caltech256
- TREC Video Retrieval Evaluation
- Oxford buildings dataset
- Photo-tourism patches
- UIUC Car detection dataset
- CMU Face databases
- Animals on the Web data
- ETH-80
- Graz 02
- MIT Objects and Scenes
- NYU NORB dataset
- Columbia COIL
- Oxford flowers dataset
- SFU activity dataset (sports)
- Princeton events dataset
- Weizmann activity videos
- MIRFlickr dataset
- Data collections of detected faces, from Oxford VGG
- Face data from Buffy episode, from Oxford VGG
- University of Cambridge face data from films [go to Data link]
- Reuters
- Dataset list from the Computer Vision Homepage
- Image Parsing
- Various other datasets from the Oxford Visual Geometry group
- INRIA Holiday images dataset
- Movie human actions dataset from Laptev et al.
- ESP game dataset
- NUS-WIDE tagged image dataset of 269K images
- Bastian Leibe’s dataset page: pedestrians, vehicles, cows, etc.
- UMass Labeled Faces in the Wild
- FaceTracer database from Columbia
- Daimler Pedestrian Benchmark Datasets
- CUHK Search Reranking Dataset
- Leeds Butterfly Dataset
- Caltech-UCSD Birds Dataset
- Первичный анализ данных с Pandas
- Визуальный анализ данных c Python
- Классификация, деревья решений и метод ближайших соседей
- Линейные модели классификации и регрессии
- Композиции: бэггинг, случайный лес
- Построение и отбор признаков
- Обучение без учителя: PCA, кластеризация
- Обучение на гигабайтах c Vowpal Wabbit
- Анализ временных рядов с помощью Python
- Градиентный бустинг
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Нейросеть DeepCoder учится программировать, заимствуя код у других программ
DeepCoder: Learning to Write Programs
Искуственный интеллект научился писать код
Our mission at DeepCode is to change the way we create programs by using powerful artificial intelligence and machine learning methods.
TensorBoard: Visualizing Learning
GitHub TensorFlow
Applied Deep Learning for Computer Vision with Torch
tensorflow
Deep learning with dynamic computation graphs in TensorFlow Fold
Opencl-opens-doors-deep-learning-training-fpga
Opencl-amd-deep-learning
Deep learning GitHub
AMD представила Radeon Instinct – в фокусе машинное обучение
Deep-learning-with-python-pydata-seattle-2015
Deep learning for computational biology
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