Deep learning Courses

Deep Learning

EECS 598: Unsupervised Feature Learning

NVIDIA’s Deep Learning Courses

https://developer.nvidia.com/deep-learning-courses

ECE 6504 Deep Learning for Perception

University of Oxford: Machine Learning: 2014-2015

University of Birmingham 2014: Introduction to Neural Computation (Level 4/M); Neural Computation (Level 3/H)(by John A. Bullinaria)

http://www.cs.bham.ac.uk/~jxb/inc.html

CMU: Deep Learning

stat212b: Topics Course on Deep Learning for Spring 2016

Good materials on deep learning

http://eclass.cc/courselists/117_deep_learning

Deep Learning: Course by Yann LeCun at Collège de France 2016(Slides in English)

CSC321 Winter 2015: Introduction to Neural Networks

ELEG 5040: Advanced Topics in Signal Processing (Introduction to Deep Learning)

Self-Study Courses for Deep Learning (NVIDIA Deep Learning Institute)

Introduction to Deep Learning

Deep Learning Courses

Creative Applications of Deep Learning w/ Tensorflow

Deep Learning School: September 24-25, 2016 Stanford, CA

CSC 2541 Fall 2016: Differentiable Inference and Generative Models

CS 294-131: Special Topics in Deep Learning (Fall, 2016)

https://berkeley-deep-learning.github.io/cs294-dl-f16/

Fork of Lempitsky DL for HSE master students.

ELEG 5040: Advanced Topics in Signal Processing (Introduction to Deep Learning)

CS 20SI: Tensorflow for Deep Learning Research

Deep Learning with TensorFlow

https://bigdatauniversity.com/courses/deep-learning-tensorflow/

Deep Learning course

CSE 599G1: Deep Learning System

CSC 321 Winter 2017: Intro to Neural Networks and Machine Learning

http://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/

Theories of Deep Learning (STATS 385)

CS230: Deep Learning Spring 2018

https://web.stanford.edu/class/cs230/

With Video Lectures

Deep Learning: Taking machine learning to the next level (Udacity)

Neural networks class - Université de Sherbrooke

Deep Learning: Theoretical Motivations

University of Waterloo: STAT 946 - Deep Learning

Deep Learning (2016) - BME 595A, Eugenio Culurciello, Purdue University

UVA DEEP LEARNING COURSE

Practical Deep Learning For Coders, Part 1

T81-558:Applications of Deep Neural Networks

CS294-129 Designing, Visualizing and Understanding Deep Neural Networks

MIT 6.S191: Introduction to Deep Learning

Edx: Deep Learning Explained

Computer Vision

Stanford CS231n: Convolutional Neural Networks for Visual Recognition (Spring 2017)

Stanford CS231n: Convolutional Neural Networks for Visual Recognition (Winter 2016)

ITP-NYU - Spring 2016

Deep Learning for Computer Vision Barcelona: Summer seminar UPC TelecomBCN (July 4-8, 2016)

DLCV - Deep Learning for Computer Vision

Advanced Computer Vision Cap6412

Natural Language Processing

CS224n: Natural Language Processing with Deep Learning

Course notes for CS224N Winter17

https://github.com/stanfordnlp/cs224n-winter17-notes

Stanford CS224d: Deep Learning for Natural Language Processing

Code for Stanford CS224D: deep learning for natural language understanding

CMU CS 11-747, Fall 2017: Neural Networks for NLP

Deep Learning for NLP - Lecture October 2015

Harvard University: CS287: Natural Language Processing

http://cs287.fas.harvard.edu/

Deep Learning for Natural Language Processing: 2016-2017

GPU Programming

Course on CUDA Programming on NVIDIA GPUs, July 27–31, 2015

An Introduction to GPU Programming using Theano

GPU Programming

Parallel Programming

Intro to Parallel Programming Using CUDA to Harness the Power of GPUs (Udacity)

https://www.udacity.com/course/intro-to-parallel-programming–cs344

Fundamentals of Accelerated Computing with CUDA C/C++

Workshops

Deep Learning: Theory, Algorithms, and Applications

Resources

Open Source Deep Learning Curriculum

http://www.deeplearningweekly.com/pages/open_source_deep_learning_curriculum

Published: 09 Oct 2015

Deep Learning Applications

Applications

Published: 09 Oct 2015

Acceleration and Model Compression

Papers

Published: 09 Oct 2015

Image / Video Captioning

Papers

Im2Text: Describing Images Using 1 Million Captioned Photographs

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

Show and Tell

Show and Tell: A Neural Image Caption Generator

Image caption generation by CNN and LSTM

Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

Learning a Recurrent Visual Representation for Image Caption Generation

Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation

Deep Visual-Semantic Alignments for Generating Image Descriptions

Deep Captioning with Multimodal Recurrent Neural Networks

Show, Attend and Tell

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention (ICML 2015)

Automatically describing historic photographs

Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images

What value do explicit high level concepts have in vision to language problems?

Aligning where to see and what to tell: image caption with region-based attention and scene factorization

Learning FRAME Models Using CNN Filters for Knowledge Visualization (CVPR 2015)

Generating Images from Captions with Attention

Order-Embeddings of Images and Language

DenseCap: Fully Convolutional Localization Networks for Dense Captioning

Expressing an Image Stream with a Sequence of Natural Sentences

Multimodal Pivots for Image Caption Translation

Image Captioning with Deep Bidirectional LSTMs

Encode, Review, and Decode: Reviewer Module for Caption Generation

Review Network for Caption Generation

Attention Correctness in Neural Image Captioning

Image Caption Generation with Text-Conditional Semantic Attention

DeepDiary: Automatic Caption Generation for Lifelogging Image Streams

phi-LSTM: A Phrase-based Hierarchical LSTM Model for Image Captioning

Captioning Images with Diverse Objects

Learning to generalize to new compositions in image understanding

Generating captions without looking beyond objects

SPICE: Semantic Propositional Image Caption Evaluation

Boosting Image Captioning with Attributes

Bootstrap, Review, Decode: Using Out-of-Domain Textual Data to Improve Image Captioning

A Hierarchical Approach for Generating Descriptive Image Paragraphs

Dense Captioning with Joint Inference and Visual Context

Optimization of image description metrics using policy gradient methods

Areas of Attention for Image Captioning

Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning

Recurrent Image Captioner: Describing Images with Spatial-Invariant Transformation and Attention Filtering

Recurrent Highway Networks with Language CNN for Image Captioning

Top-down Visual Saliency Guided by Captions

MAT: A Multimodal Attentive Translator for Image Captioning

https://arxiv.org/abs/1702.05658

Deep Reinforcement Learning-based Image Captioning with Embedding Reward

Attend to You: Personalized Image Captioning with Context Sequence Memory Networks

Punny Captions: Witty Wordplay in Image Descriptions

https://arxiv.org/abs/1704.08224

Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner

https://arxiv.org/abs/1705.00930

Actor-Critic Sequence Training for Image Captioning

  • intro: Queen Mary University of London & Yang’s Accounting Consultancy Ltd
  • keywords: actor-critic reinforcement learning
  • arxiv: https://arxiv.org/abs/1706.09601

What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?

Stack-Captioning: Coarse-to-Fine Learning for Image Captioning

https://arxiv.org/abs/1709.03376

Self-Guiding Multimodal LSTM - when we do not have a perfect training dataset for image captioning

https://arxiv.org/abs/1709.05038

Contrastive Learning for Image Captioning

Phrase-based Image Captioning with Hierarchical LSTM Model

Convolutional Image Captioning

https://arxiv.org/abs/1711.09151

Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning

https://arxiv.org/abs/1712.02051

Improved Image Captioning with Adversarial Semantic Alignment

Object Counts! Bringing Explicit Detections Back into Image Captioning

Defoiling Foiled Image Captions

SemStyle: Learning to Generate Stylised Image Captions using Unaligned Text

Improving Image Captioning with Conditional Generative Adversarial Nets

https://arxiv.org/abs/1805.07112

CNN+CNN: Convolutional Decoders for Image Captioning

https://arxiv.org/abs/1805.09019

Diverse and Controllable Image Captioning with Part-of-Speech Guidance

https://arxiv.org/abs/1805.12589

Learning to Evaluate Image Captioning

Topic-Guided Attention for Image Captioning

Context-Aware Visual Policy Network for Sequence-Level Image Captioning

Exploring Visual Relationship for Image Captioning

Boosted Attention: Leveraging Human Attention for Image Captioning

Image Captioning as Neural Machine Translation Task in SOCKEYE

https://arxiv.org/abs/1810.04101

Unsupervised Image Captioning

https://arxiv.org/abs/1811.10787

Attend More Times for Image Captioning

https://arxiv.org/abs/1812.03283

Object Descriptions

Generation and Comprehension of Unambiguous Object Descriptions

Video Captioning / Description

Jointly Modeling Deep Video and Compositional Text to Bridge Vision and Language in a Unified Framework

Translating Videos to Natural Language Using Deep Recurrent Neural Networks

Describing Videos by Exploiting Temporal Structure

SA-tensorflow: Soft attention mechanism for video caption generation

Sequence to Sequence – Video to Text

Jointly Modeling Embedding and Translation to Bridge Video and Language

Video Description using Bidirectional Recurrent Neural Networks

Bidirectional Long-Short Term Memory for Video Description

3 Ways to Subtitle and Caption Your Videos Automatically Using Artificial Intelligence

Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation

Grounding and Generation of Natural Language Descriptions for Images and Videos

Video Captioning and Retrieval Models with Semantic Attention

  • intro: Winner of three (fill-in-the-blank, multiple-choice test, and movie retrieval) out of four tasks of the LSMDC 2016 Challenge (Workshop in ECCV 2016)
  • arxiv: https://arxiv.org/abs/1610.02947

Spatio-Temporal Attention Models for Grounded Video Captioning

Video and Language: Bridging Video and Language with Deep Learning

Recurrent Memory Addressing for describing videos

Video Captioning with Transferred Semantic Attributes

Adaptive Feature Abstraction for Translating Video to Language

Semantic Compositional Networks for Visual Captioning

Hierarchical Boundary-Aware Neural Encoder for Video Captioning

Attention-Based Multimodal Fusion for Video Description

Weakly Supervised Dense Video Captioning

Generating Descriptions with Grounded and Co-Referenced People

Multi-Task Video Captioning with Video and Entailment Generation

Dense-Captioning Events in Videos

Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning

https://arxiv.org/abs/1706.01231

Reinforced Video Captioning with Entailment Rewards

End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering

From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning

https://arxiv.org/abs/1708.02478

Grounded Objects and Interactions for Video Captioning

https://arxiv.org/abs/1711.06354

Integrating both Visual and Audio Cues for Enhanced Video Caption

https://arxiv.org/abs/1711.08097

Video Captioning via Hierarchical Reinforcement Learning

https://arxiv.org/abs/1711.11135

Consensus-based Sequence Training for Video Captioning

https://arxiv.org/abs/1712.09532

Less Is More: Picking Informative Frames for Video Captioning

https://arxiv.org/abs/1803.01457

End-to-End Video Captioning with Multitask Reinforcement Learning

https://arxiv.org/abs/1803.07950

End-to-End Dense Video Captioning with Masked Transformer

Reconstruction Network for Video Captioning

Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning

Jointly Localizing and Describing Events for Dense Video Captioning

Contextualize, Show and Tell: A Neural Visual Storyteller

https://arxiv.org/abs/1806.00738

RUC+CMU: System Report for Dense Captioning Events in Videos

Streamlined Dense Video Captioning

Projects

Learning CNN-LSTM Architectures for Image Caption Generation: An implementation of CNN-LSTM image caption generator architecture that achieves close to state-of-the-art results on the MSCOCO dataset.

screengrab-caption: an openframeworks app that live-captions your desktop screen with a neural net

Tools

CaptionBot (Microsoft)

Blogs

Captioning Novel Objects in Images

http://bair.berkeley.edu/jacky/2017/08/08/novel-object-captioning/

Published: 09 Oct 2015

Deep Learning and Autonomous Driving

Courses

(Toronto) CSC2541: Visual Perception for Autonomous Driving, Winter 2016

(MIT) 6.S094: Deep Learning for Self-Driving Cars

How to Land An Autonomous Vehicle Job: Coursework

Papers

An Empirical Evaluation of Deep Learning on Highway Driving

Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving

Optical Flow augmented Semantic Segmentation networks for Automated Driving

AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving

Design of Real-time Semantic Segmentation Decoder for Automated Driving

Hierarchical Multi-task Deep Neural Network Architecture for End-to-End Driving

https://arxiv.org/abs/1902.03466

DeepDriving

DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

End to End Learning for Self-Driving Cars

End-to-End Deep Learning for Self-Driving Cars

Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?

BRAIN4CARS: Cabin Sensing for Safe and Personalized Driving

Brain4Cars: Sensory-Fusion Recurrent Neural Models for Driver Activity Anticipation

Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture

Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture

Long-term Planning by Short-term Prediction

Learning a Driving Simulator

Comma.ai open-sources the data it used for its first successful driverless trips

Autonomous driving challenge: To Infer the property of a dynamic object based on its motion pattern using recurrent neural network

Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

Learning from Maps: Visual Common Sense for Autonomous Driving

SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks

  • intro: Accepted at the Deep Learning for Action and Interaction Workshop, 30th Conference on Neural Information Processing Systems (NIPS 2016)
  • arxiv: https://arxiv.org/abs/1611.08788

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention

Virtual to Real Reinforcement Learning for Autonomous Driving

Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Deep Reinforcement Learning framework for Autonomous Driving

https://arxiv.org/abs/1704.02532

Systematic Testing of Convolutional Neural Networks for Autonomous Driving

https://arxiv.org/abs/1708.03309

MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving

https://arxiv.org/abs/1709.04821

CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving

LaneNet: Real-Time Lane Detection Networks for Autonomous Driving

Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision

https://arxiv.org/abs/1808.10393

Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability

Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving

https://arxiv.org/abs/1810.00345

Multi-task Learning with Attention for End-to-end Autonomous Driving

Projects

Caffe-Autopilot: Car autopilot software that uses C++, BVLC Caffe, OpenCV, and SFML

Self Driving Car Demo

Autoware: Open-source software for urban autonomous driving

Open Sourcing 223GB of Driving Data

Machine Learning for RC Cars

Self Driving (Toy) Ferrari

Lane Finding Project for Self-Driving Car ND

Instructions on how to get your development environment ready for Udacity Self Driving Car (SDC) Challenges

DeepDrive: self-driving car AI

DeepDrive setup: Run a self-driving car simulator from the comfort of your own PC

DeepTesla: End-to-End Learning from Human and Autopilot Driving

http://selfdrivingcars.mit.edu/deeptesla/

DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

Autonomous Driving in Reality with Reinforcement Learning and Image Translation

End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception

https://arxiv.org/abs/1801.06734

Blogs

Self-driving cars: How far away are we REALLY from autonomous cars?(7 Aug 2015)

http://www.alphr.com/cars/1001329/self-driving-cars-how-far-away-are-we-really-from-autonomous-cars

Practice makes perfect: Driverless cars will learn from their mistakes(9 Oct 2015)

http://www.alphr.com/cars/1001713/practice-makes-perfect-driverless-cars-will-learn-from-their-mistakes

Eyes on the Road: How Autonomous Cars Understand What They’re Seeing

Human-in-the-loop deep learning will help drive autonomous cars

http://venturebeat.com/2016/06/25/human-in-the-loop-deep-learning-will-help-drive-autonomous-cars/

Using reinforcement learning in Python to teach a virtual car to avoid obstacles

Autonomous RC car using Raspberry Pi and Neural Networks

The Road Ahead: Autonomous Vehicles Startup Ecosystem

https://medium.com/the-mission/the-road-ahead-autonomous-vehicles-startup-ecosystem-3c91d546673d#.gft1xyh9l

Deep Driving - A revolutionary AI technique is about to transform the self-driving car

https://www.technologyreview.com/s/602600/deep-driving/

Visualizations for regressing wheel steering angles in self driving cars with Keras

Published: 09 Oct 2015

Audio / Image / Video Generation

Papers

Optimizing Neural Networks That Generate Images

Learning to Generate Chairs, Tables and Cars with Convolutional Networks

DRAW: A Recurrent Neural Network For Image Generation

What is DRAW (Deep Recurrent Attentive Writer)?

Colorizing the DRAW Model

Understanding and Implementing Deepmind’s DRAW Model

Generative Image Modeling Using Spatial LSTMs

Conditional generative adversarial nets for convolutional face generation

Generating Images from Captions with Attention

Attribute2Image: Conditional Image Generation from Visual Attributes

Autoencoding beyond pixels using a learned similarity metric

Deep Visual Analogy-Making

Pixel Recurrent Neural Networks

Generating images with recurrent adversarial networks

Pixel-Level Domain Transfer

Generative Adversarial Text to Image Synthesis

Conditional Image Generation with PixelCNN Decoders

Inverting face embeddings with convolutional neural networks

Unsupervised Cross-Domain Image Generation

PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications

Generating Interpretable Images with Controllable Structure

Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

Image Generation and Editing with Variational Info Generative AdversarialNetworks

DeepFace: Face Generation using Deep Learning

Multi-View Image Generation from a Single-View

Generative Cooperative Net for Image Generation and Data Augmentation

https://arxiv.org/abs/1705.02887

Statistics of Deep Generated Images

https://arxiv.org/abs/1708.02688

Sketch-to-Image Generation Using Deep Contextual Completion

https://arxiv.org/abs/1711.08972

Energy-relaxed Wassertein GANs(EnergyWGAN): Towards More Stable and High Resolution Image Generation

https://arxiv.org/abs/1712.01026

Spatial PixelCNN: Generating Images from Patches

https://arxiv.org/abs/1712.00714

Visual to Sound: Generating Natural Sound for Videos in the Wild

Semi-supervised FusedGAN for Conditional Image Generation

https://arxiv.org/abs/1801.05551

Image Transformer

Unpaired Multi-Domain Image Generation via Regularized Conditional GANs

https://arxiv.org/abs/1805.02456

Transferring GANs: generating images from limited data

Cross Domain Image Generation through Latent Space Exploration with Adversarial Loss

https://arxiv.org/abs/1805.10130

Face Image Generation

Fader Networks: Manipulating Images by Sliding Attributes

Person Image Generation

Disentangled Person Image Generation

Pose Guided Person Image Generation

Deformable GANs for Pose-based Human Image Generation

Unpaired Pose Guided Human Image Generation

https://arxiv.org/abs/1901.02284

Video Generation

MoCoGAN: Decomposing Motion and Content for Video Generation

Attentive Semantic Video Generation using Captions

https://arxiv.org/abs/1708.05980

Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture

Towards an Understanding of Our World by GANing Videos in the Wild

Video Generation from Single Semantic Label Map

Deep Generative Model

Digit Fantasies by a Deep Generative Model

Conditional generative adversarial nets for convolutional face generation

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

Torch convolutional GAN: Generating Faces with Torch

One-Shot Generalization in Deep Generative Models

Generative Image Modeling using Style and Structure Adversarial Networks

Synthesizing Dynamic Textures and Sounds by Spatial-Temporal Generative ConvNet

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

ArtGAN: Artwork Synthesis with Conditional Categorial GANs

Learning to Generate Chairs with Generative Adversarial Nets

https://arxiv.org/abs/1705.10413

Blogs

Torch convolutional GAN: Generating Faces with Torch

Generating Large Images from Latent Vectors

http://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/

Generating Faces with Deconvolution Networks

Attention Models in Image and Caption Generation

Deconvolution and Checkerboard Artifacts

Projects

Generate cat images with neural networks

TF-VAE-GAN-DRAW

  • intro: A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation).
  • github: https://github.com/ikostrikov/TensorFlow-VAE-GAN-DRAW

Generating Large Images from Latent Vectors

Generating Large Images from Latent Vectors - Part Two

Analyzing 50k fonts using deep neural networks

Generate cat images with neural networks

Generate human faces with neural networks

A TensorFlow implementation of DeepMind’s WaveNet paper

Published: 09 Oct 2015

Adversarial Attacks and Defences

Papers

Published: 09 Oct 2015

Recognition, Detection, Segmentation and Tracking

Classification / Recognition

Published: 09 Oct 2015

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