This page contains resources, blogs, projects, papers, codes, links and other materials needed to get exposure in Deep Learning. Feel free to share it to people interested in this domain.
Blogs
- Andrej Karpathy
- Christopher Olah
- Denny Britz
- Tim Dettmers
- Jon Gauthier
- Jonas Degrave
- Deep Learning.net
- Charles H Martin
- Trask
- Thomas Kipf
- Jason Brownlee
- Adrian Rosebrock
Classes
- Deep Learning - Google
- Deep Learning Specialization - Andrew NG
- CS229 Machine Learning - Andrew NG
- Deep Learning - Udacity
- Deep Learning at Oxford - Nando de Freitas (University of Oxford)
- Neural Networks for Machine Learning - Geoffrey Hinton (Google, University of Toronto)
- Deep Learning for Computer Vision - Rob Fergus (Facebook, NYU)
- Learning from Data - Yasser Abu-Mostafa (Caltech)
- Deep Learning for Natural Language Processing (Stanford)
- NVIDIA Deep Learning Institute
- Deep Learning for Business - Jong-Moon Chung
- Deep Learning in Python - Data Camp
- Machine Learning for Musicians and Artists - Goldsmiths, University of London
- 6.S191: Introduction to Deep Learning - Massachusetts Institute of Technology
- 6.S094: Deep Learning for Self-Driving Cars - Massachusetts Institute of Technology
- Deep Learning for Natural Language Processing - University of Oxford
- CS231n: Convolutional Neural Networks for Visual Recognition - Stanford University
- Learn TensorFlow and deep learning, without a Ph.D. - Google
- Deep Learning A-Z™: Hands-On Artificial Neural Networks - Udemy
- Zero to Deep Learning™ with Python and Keras - Udemy
- Deep Learning Prerequisites: The Numpy Stack in Python - Udemy
- Data Science: Deep Learning in Python - Udemy
- Deep Learning Prerequisites: Linear Regression in Python - Udemy
- Deep Learning Prerequisites: Logistic Regression in Python - Udemy
- Deep Learning: Convolutional Neural Networks in Python - Udemy
- Data Science: Deep Learning in Python - Udemy
- Modern Deep Learning in Python - Udemy
- Deep Learning: Recurrent Neural Networks in Python - Udemy
- Unsupervised Deep Learning in Python - Udemy
- Natural Language Processing with Deep Learning in Python - Udemy
- Unleash Deep Learning: Begin Visually with Caffe and DIGITS - Udemy
Technical Presentations
- GPUs and Machine Learning: A Look at cuDNN- Sharan Chetlur (NVIDIA)
- Deep Learning at Scale - Ren Wu (Baidu)
- Deep Learning: What's Next - Andrew Ng (Baidu)
Libraries
- Keras, is a Deep Learning library for Python, that is simple, modular, and extensible. Created by Franchois Chollet, Artificial Intelligence Researcher, Google.
- Caffe, created by Yangqing Jia and developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
- Theano, primarily developed by a machine learning group at the Université de Montréal.
- TensorFlow, originally developed by the Google Brain team for internal Google use before being released under the Apache 2.0 open source license on November 9, 2015.
- MXNet, an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices.
- Torch, a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.
- sklearn-theano, OverFeat and GoogleNet feature extractor for Python.
Books
- Deep Learning book, by Ian Goodfellow and Yoshua Bengio and Aaron Courville.
Surveys
- Deep learning, Yann LeCun, Yoshua Bengio and Geoffrey Hinton.
- A Taxonomy of Deep Convolutional Neural Nets for Computer Vision, Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri, Nikita Prabhu, Srinivas S S Kruthiventi, R. Venkatesh Babu.
- A Critical Review of Recurrent Neural Networks for Sequence Learning, Zachary C. Lipton, John Berkowitz.
Must read papers
- Deep Sparse Rectifier Neural Networks - [Paper], Xavier Glorot, Antoine Bordes, Yoshua Bengio.
- Efficient Backprop - [Paper], Yaan LeCun et al.
Architectures
- VGGNet - Very Deep Convolutional Networks for Large-Scale Image Recognition - [Home], [arXiv], [Slides], [PDF], [GitHub], [TensorFlow], [Keras VGG16], [Keras VGG19]
- GoogleNet - Going Deeper with Convolutions - [arXiv], [GitHub], [Torch], [Keras], [Blog]
- Inception-V2 - Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift - [arXiv], [GitHub], [Keras], [Blog]
- Inception-V3 - Rethinking the Inception Architecture for Computer Vision - [arXiv], [Torch], [TensorFlow], [Blog], [Keras]
- Inception-V4 - Inception-Resnet And The Impact Of Residual Connections On Learning - [arXiv], [Keras], [Torch]
- ResNet - Deep Residual Learning for Image Recognition - [arXiv], [Torch], [Torch], [Keras]
- ResNet V2 - Identity Mappings in Deep Residual Networks - [arXiv], [GitHub], [GitHub]
- ResNeXt - Aggregated Residual Transformations for Deep Neural Networks - [arXiv], [GitHub], [Torch]
Deep Learning for Computer Vision
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning - [Page], [GitHub], [arXiv]
- Deep Visual-Semantic Alignments for Generating Image Descriptions - [PDF], [GitHub], [Presentation]
Deep Learning for Face Recognition
- DeepID - Deep Learning Face Representation from Predicting 10,000 Classes - [Paper], [GitHub]
- DeepID2 - Deep Learning Face Representation by Joint Identification-Verification - [arXiv]
- DeepID3 - Face Recognition with Very Deep Neural Networks - [arXiv]
- Learning Face Representation from Scratch - [arXiv]
- Face Search at Scale: 80 Million Gallery - [arXiv]
- A Discriminative Feature Learning Approach for Deep Face Recognition - [arXiv]
- One Millisecond Face Alignment with an Ensemble of Regression Trees - [arXiv]
- DeepFace - Closing the Gap to Human-Level Performance in Face Verification - [Paper], [Slides], [GitHub]
- Deep Face Recognition - Closing the Gap to Human-Level Performance in Face Verification - [Paper], [Home], [Keras]
- FaceNet - A Unified Embedding for Face Recognition and Clustering - [Paper], [TensorFlow], [GitHub]
- Targeting Ultimate Accuracy - Face Recognition via Deep Embedding - [Paper]
Deep Learning for Robotics
- A Survey of Machine Learning Approaches to Robotic Path-Planning - [Paper]
- Deep Learning Driven Visual Path Prediction from a Single Image - [arXiv]
- A Novel Path Planning Method for Biomimetic Robot based on Deep Learning - [Link]
- DeepVO: A Deep Learning approach for Monocular Visual Odometry - [arXiv]
- An Improved Q-learning Algorithm for Path-Planning of a Mobile Robot - [Paper]
- Optical Flow and Deep Learning Based Approach to Visual Odometry - [Thesis]
- Learning Visual Odometry with a Convolutional Network - [Paper]
- Learning to See by Moving - [arXiv]
Deep Learning for Music
- Music Generation with Deep Learning - [Paper]
- Deep Learning for Music - [Paper]
- GRUV: Algorithmic Music Generation using Recurrent Neural Networks - [Paper], [GitHub]
- A Recurrent Neural Network Music Generation Tutorial - [Tutorial]
- Composing Music With Recurrent Neural Networks - [Tutorial]
- Modeling and generating sequences of polyphonic music with the RNN-RBM - [Tutorial]
- Deep learning for assisting the process of music composition - [Tuturial]
- Using machine learning to generate music - [Blog]
- WaveNet: A Generative Model for Raw Audio - [Blog]
- Deep Learning for Music - [Blog]
- Generating sound with recurrent neural networks - [Blog]
- Generating Multi-track Music with Deep Learning - [Blog]
Deep Learning for Fine Grained Recognition
Plant/Flower Recognition
- Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction - [Paper]
- Fine-tuning Deep Convolutional Networks for Plant Recognition - [Paper]
- Plant species classification using deep convolutional neural network - [Paper]
- Plant classification using convolutional neural networks - [Paper]
- Deep-plant: Plant identification with convolutional neural networks - [Paper]
- Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification - [Paper]
- Plant Leaf Identification via A Growing Convolution Neural Network with Progressive Sample Learning - [Paper]
Food Recognition
- DeepFood - Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment - [arXiv]
- Im2Calories - towards an automated mobile vision food diary - [Paper]
- Food Image Recognition by Using Convolutional Neural Networks (CNNs) - [arXiv]
- Food Classification with Deep Learning in Keras / Tensorflow - [Blog], [GitHub]
Instrument Recognition
- Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks - [arXiv], [GitHub]
Musical Instrument Recognition
- Deep Convolutional Networks on the Pitch Spiral for Musical Instrument Recognition - [Paper], [GitHub]
Star-galaxy Classification
Logo Recognition
- Deep Learning for Logo Recognition - [arXiv]
In case if you found something useful to add to this article or you found a bug in the code or would like to improve some points mentioned, feel free to write it down in the comments. Hope you found something useful here.