| Name | Comment |
|---|---|
| arXiv by Cornell University | All AI/ML/DL papers appear hear |
| arXiv - Source code for papers | Source for published arXiv papers |
| Search and track arXiv AI/ML/DL related papers | Search and track on top of arXiv by Andrej Karpathy |
| Mendeley Desktop | Manage reading list of papers |
| Reddit ML forum | AI/ML subforum |
| DataTau forum | AI/ML dedicated forum |
| Measuring the Progress of AI Research | Comprehensive dataset of AI topics and advances within them |
| The Neural Network Zoo | Chart and high-level overview of different Neural Networks |
| Guide to Machine Learning by Yerevann | Very good collection of materials for different topics |
| A Tour of Machine Learning Algorithms | Overview of ML algorithms |
| AI/ML cheat sheet | Constantly updated |
| Machine Learning cheat sheet wiki | Lots of starter information |
| Deep Learning tutorial by Stanford | Very good material explaining different topics |
| Machine Learning, Deep Learning and other tutorials | Lots of information, but not organized very well |
| Kaggle past solutions | Good collection, but still missing many entries |
| List of pre-trained models | Pre-trained models for TensorFlow, PyTorch |
| Name | Comment |
|---|---|
| Deep Learning Weekly | Weekly newsletter (Deep Learning) |
| Transmission | Weekly newsletter (Deep Learning, Self-driving cars) |
| The Wild Week in AI | Weekly newsletter (AI, Deep Learning) |
| Artificial Intelligence and Deep Learning Weekly | Weekly newsletter (AI, Deep Learning) |
| City AI | Monthly newsletter (AI) |
| Linear Digressions podcast | Weekly ML/data science podcast |
| This Week in Machine Learning | Weekly news on ML from Udacity |
| Partially Derivative podcast | Weekly news |
| This Week in Machine Learning & AI podcast | |
| Data Sceptic podcast | |
| SDS podcast | Data science focused podcast |
| Talking Machines podcast | |
| The AI podcast | |
| O'Reilly Data Show podcast | |
| O'Reilly Bots podcast | |
| Data Stories podcast | |
| Learning Machines 101 podcast | |
| Data Driven podcast |
Deep Learning (Adaptive Computation and Machine Learning series), Github, Amazon and its translation ГЛУБОКОЕ ОБУЧЕНИЕ
Grokking Deep Learning
Neural Networks and Deep Learning
Machine Learning A Probabilistic Perspective
Artificial Inteligence
Machine Learning Yearning by Andrew Ng
Python Deep Learning, Packtpub
Information Theory, Inference and Learning Algorithms, Amazon
Understanding Machine Learning: From Theory to Algorithms
All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)
| Service | Details |
|---|---|
| FloydHub | Per second pricing, quick launch, very competitive prices |
| Paperspace | Reasonable pricing, 16/24GB GPU options |
| AWS P3 Spot instances | Usual AWS P2 pricing is insane, but you could get Spot instances cheaper. How to setup |
| Google Cloud GPU | $300 free credit to spend How to setup |
| Hetzner dedicated 1080 GPU server | Intel i7-6700/64GB RAM/2x500GB SSD/GeForce GTX 1080 = 99 EUR month + 99 EUR setup fee. |
| Google Cloud TPU | Still Alpha - pricign and availability unknown |
Baidu DL hardware benchmarks Benchmarking of different DL algorithms on different hardware
Benchmarks of convnets Outdatd for major convnets, but have Nervana results
Benchmarks for popular CNN models
Benchmarking TensorFlow on Cloud CPUs: Cheaper Deep Learning than Cloud GPUs
Github Repository to benchmark the performance of Cloud CPUs vs. Cloud GPUs on TensorFlow and Google Compute Engine
Benchmarking State-of-the-Art Deep Learning Software Tools and Github
| Name | Details |
|---|---|
| DeepMind Lab | A customisable 3D platform for agent-based AI research |
| OpenAI Universe | A software platform for evaluating and training intelligent agents across the world’s supply of games, websites and other applications. |
| OpenAI Gym | A toolkit for developing and comparing reinforcement learning algorithms |
| OpenAI RoboSchool | Open-source software for robot simulation, integrated with OpenAI Gym |
| Udacity car sim | A self-driving car simulator built with Unity |
| Microsoft AirSim | AirSim is a simulator for drones |
| Facebook ELF | An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games [Intro] (https://code.facebook.com/posts/132985767285406/introducing-elf-an-extensive-lightweight-and-flexible-platform-for-game-research/) |
| TorchCraft | Connecting Torch to StarCraft |
| Facebook ParlAI | A framework for training and evaluating AI models on a variety of openly available dialog datasets |
| OpenAI RLLab | Framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym |
| Baidu Apollo | An open autonomous driving platform |
| ChosunTruck | Euro Truck Simulator 2 autonomous driving solution |
| Name | Language | Comment |
|---|---|---|
| TensorFlow | Python | Nobody ever got fired for choosing TensorFlow |
| Keras | Python | High-level library on top of TensorFlow, CNTK, Theano |
| PyTorch | Python | Intro in 10 minutes |
| Theano | Python | |
| Caffe | C++ | |
| CNTK | C++ | The Microsoft Cognitive Toolkit |
| Sonnet | Python | High-level library on top of TF by DeepMind |
| Nnabla | C++ | Neural Network Libraries by Sony |
| Core ML | ? | Apple OS only |
| ELL | C++ | Microsoft Embedded Learning Library - Machine Learning on mini devices like Raspberry Pi |
| WebDNN | Javascript | Optimized Web framework for running DNN |
| DeepLearn.js | Javascript | Google take on DL library in Javascript |
| Intel Nervana | Python/C++ | Intel CPU optimized MKL framework |
| LightGBM | C++ | Microsoft A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms |
| OpenPose | C++ | A Real-Time Multi-Person Keypoint Detection And Multi-Threading C++ Library |
| Polyaxon | Python | A platform that helps you build, manage and monitor deep learning models |
Start with watching awesome MIT Linear Algebra course via Youtube playlist - first 10 or so lectures would be enough for some time.
Linear algebra in 4 pages
And only then start reading books, if you need more content - good summary of Linear Algebra from Deep Learning book Chapter 2
Common Probability Distributions: The Data Scientist’s Crib Sheet
Math probability cheat sheet
Statistics cheat sheet
Calculus cheat sheet
Matrix Cookbook
The Mathematics of Machine Learning
| Name | Link | Rating | Comment |
|---|---|---|---|
| Neural Networks Demystified | Youtube playlist | ⭐⭐⭐⭐ | Good intro to neural networks - heavy on math, uses Python code |
| Stanford CS231n: Convolutional Neural Networks for Visual Recognition | Official page Github Youtube 2016 playlist Youtube 2017 videos |
⭐⭐⭐⭐⭐ | Start Deep Learning education from this one - focused on image processing. Many lectures presented by Andrej Karpathy in 2016 videos |
| Udacity Deep Learning Foundation Nanodegree Program | Official page | ⭐⭐⭐⭐⭐ | $400 for 6 months course - you would need to spend at least 10h a week to complete it. Knowledge of Python 3.x and Numpy would make your life much easier - Pandas would help too. |
| Udacity Intro to Machine Learning | Official page | ⭐⭐⭐⭐ | Basics of ML - top algorithms, process to work with data, etc. All code done in Python and scikit-learn - majority of test tasks are simple and very similiar |
| Coursera Machine Learning by Andrew Ng | Official page | ⭐⭐⭐⭐⭐ | This course is classics by now - Coursera started from it and its very good at explaining classical ML algorithms. One minor annoynce - tasks are done in Octave, but you could do them in Python using unofficial Github repo |
| Stanford CS224n: Deep Learning for Natural Language Processing | Official page Youtube playlist |
In Progress | Focus - NLP with Deep Learning. Previously known as CS224d: Deep Learning for Natural Language Processing |
| David Silver’s course on reinforcement learning | Official page Youtube playlist |
- | David was main developer of AlphaGo |
| Udacity Reinforcement Learning by Georgia Tech (CS 8803) | Official page | - | |
| Deep Learning 101 | Official page | Skip it | Tries to do overview without providing details - more like DP for non-IT person |
| Deep Learning with TensorFlow | Official page | Skip it | Very high level with not enough "meat" |
| Tensorflow and deep learning - without a PhD by Martin Görner | Youtube video | ⭐⭐⭐ | 2.5h video trying to cover everything in DL area from beginning - IMHO its too shallow |
| MIT 6.S191: Introduction to Deep Learning | Official page Youtube playlist |
- | Short intro course |
| Oxford Deep Learning for Natural Language Processing 2017 by Phil Blunsom | Official page Github Youtube playlist |
- | Main Oxford NLP course |
| UC Berkeley CS 294: Deep Reinforcement Learning, Fall 2017 | Official page Youtube playlist |
- | |
| MIT 6.S094: Deep Learning for Self-Driving Cars | Official page Youtube playlist |
- | |
| Deep Learning for Speech and Language 2017 | Official page Youtube |
- | Only course dedicated to advanced speech recognition using Deep Learning |
| Fast.ai Practical Deep Learning For Coders, Part 1 | Official page Youtube |
- | |
| Fast.ai Practical Deep Learning For Coders, Part 2 | Official page Files |
- | |
| Coursera Neural Networks for Machine Learning | Official page | - | This is very very very hard course to master - think twice before taking it (course review here) :) |
| OpenDataScience Machine Learning course | Official page | - | In Russian |
| Yandex Reinforcement learning in the wild | Official page | - | Links to video inside Github folders |
| Введение в обработку естественного языка | Official page | - | In Russian |
| Introduction to Natural Language Processing | Official page | - | |
| Deep learning at Oxford 2015 | Official page Youtube |
- | |
| Stanford CS 20SI: Tensorflow for Deep Learning Research | Official page Github Youtube |
- | TensorFlow learning |
| Udacity Introduction to Computer Vision by Georgia Tech (CS 6476) | Official page | - | |
| Berkeley CS188 Intro to AI | Official page Youtube |
- | Introduction to classic AI |
| Hugo Larochelle's Neural Network class | Official page | - | Little outdated now, but still should be great |
| Deep Learning inaugural lectures by Yann LeCun | Official page | - | Lectures only |
| Coursera Probabilistic Graphical Models | Part 1 Part 2 Part 3 |
- | More than you want to know in this area - these are very challenging courses |
| Caltech Machine Learning course by Yaser S. Abu-Mostafa | Official page Youtube |
- | Also available via edX platform Learning from Data |
| Berkeley CS 294-129: Designing, Visualizing and Understanding Deep Neural Networks (2016) | Official page Youtube |
- | |
| DEEP LEARNING AND REINFORCEMENT LEARNING SUMMER SCHOOL 2017 | Official page Github |
- | Slides only |
| MIT Mathematics of Machine Learning 2015 | Official page | - | Slides only |
| Berkeley CS 294-131: Special Topics in Deep Learning | Official page | - | Slides only |
| Berkeley STAT212b: Course on Deep Learning for Spring 2016 | Official page | - | Slides only |
| Name | Comment |
|---|---|
| Jupyter notebook | Use it :) |
| NumPy | Math |
| SciPy | Math |
| CuPy | CUDA accelerated Numpy |
| Pandas | Data analysis |
| Scikit | Machine Learning algorithms |
| matplotlib | Data visualization |
| Seaborn | Statistical data visualization |
| Scikit-plot | Add plotting to scikit-learn |
| keras-vis | Visualization of Keras DL layers |
| NLTK | Natural Language Toolkit |
| Xcessiv | Web-based application for quick and scalable hyperparameter tuning and stacked ensembling |
| Luigi | Helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc |
| Taggy.ai | Helps you tag image datasets |
| Hyperopt | Distributed Asynchronous Hyper-parameter Optimization |
TensorFlow Playground
TensorBoard: Visualizing Learning
Hands-on TensorBoard (TensorFlow Dev Summit 2017)
Machine IDE
TensorFlow Mobile
MobileNets: Open-Source Models for Efficient On-Device Vision
TensorFlow: How to optimise your input pipeline with queues and multi-threading
Exponential decay for learning rate in TF
Monitoring validation loss and stop hook and NaN hook
Building a Real-Time Object Recognition App with TensorFlow Object Detection API and OpenCV
Keras as a simplified interface to TensorFlow: tutorial
Introduction to Deep Neural Networks with Keras and Tensorflow
VGG19 and VGG16 on Tensorflow
COCO-Stuff 10K dataset v1.1
LiDAR Data for Washington DC is Available as an AWS Public Dataset
The 20BN-JESTER dataset is a large collection of densly-labeled video clips that show humans performing predefinded hand gestures
The 20BN-SOMETHING-SOMETHING dataset is a large collection of densly-labeled video clips that show humans performing predefined basic actions with every day objects
Yes you should understand backprop
A Step by Step Backpropagation Example
What is the difference between test set and validation set?
Learning to Reason with Neural Module Networks
An end to end implementation of a Machine Learning pipeline
An Overview of Multi-Task Learning in Deep Neural Networks and Paper
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Github SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
PathNet: Evolution Channels Gradient Descent in Super Neural Networks and Intro
Data Science Bowl 2017 Can you improve lung cancer detection? and Winner solution, 2nd place
Neural Translation of Musical Style
A library for benchmarking vulnerability to adversarial examples
Visual Interaction Networks and Github
(Xavier initialization) Understanding the difficulty of training deep feedforward neural networks and An Explanation of Xavier Initialization
Layer-sequential unit-variance (LSUV) initialization All you need is a good init Keras Github
(ReLU) Deep Sparse Rectifier Neural Networks
(LRelU) Rectifier Nonlinearities Improve Neural Network Acoustic Models
(PReLU + Xavier for ReLU) Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
(ThresholdedReLU) Zero-bias autoencoders and the benefits of co-adapting features
(ELU) Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
(SELU) Self-Normalizing Neural Networks, Github + Github research and Reddit
(CReLu) Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Compare distribution of SELU, ReLU, LReLU and other activation functions and Evaluation on ImageNet-2012
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift and comments
Understanding the backward pass through Batch Normalization Layer
Initialization of deep networks
Practical recommendations for gradient-based training of deep architectures
Efficient BackProp by Yann LeCun
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Improving neural networks by preventing co-adaptation of feature detectors
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Why dropouts prevent overfitting in Deep Neural Networks
Concrete Dropout and Github
Hyperparameter optimization for Neural Networks
The Marginal Value of Adaptive Gradient Methods in Machine Learning
YellowFin: An automatic tuner for momentum SGD and Github
Tips for Training Recurrent Neural Networks
Must Know Tips/Tricks in Deep Neural Networks (by Xiu-Shen Wei)
The Black Magic of Deep Learning - Tips and Tricks for the practitioner
Deep Learning workshop - Chapter 11 Practical Methodology, Video 1, Video 2
Snapshot Ensembles: Train 1, get M for free
FreezeOut: Accelerate Training by Progressively Freezing Layers
Train your deep model faster and sharper — two novel techniques
Hyperopt – Finding the optimal hyper parameters
Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
How to use cross-validation in predictive modeling
(Convolution resize vs deconv)Deconvolution and Checkerboard Artifacts
An overview of gradient descent optimization algorithms
Why Momentum Really Works
Gentle Introduction to the Adam Optimization Algorithm for Deep Learning
How to Escape Saddle Points Efficiently and Paper
1-Bit Stochastic Gradient Descent and its Application to Data-Parallel Distributed Training of Speech DNNs
Facebook Visdom
Picasso: A free open-source visualizer for Convolutional Neural Networks and Github
How to Visualize Your Recurrent Neural Network with Attention in Keras
Visualizing MNIST: An Exploration of Dimensionality Reduction
How to Use t-SNE Effectively
Understanding Deep Image Representations by Inverting Them
Systematic evaluation of CNN advances on the ImageNet
9 Key Deep Learning Papers, Explained
CS231n CNN intro
Deep Learning #3: More on CNNs & Handling Overfitting
Transfer Learning from CS231n
What is Transfer Learning? by Sebastian Ruder
Network Dissection: Quantifying Interpretability of Deep Visual Representations and Github
(Mean-Max Pooling) Dual Path Networks and Github
Deconvolutional Networks
What are deconvolutional layers?
Convolution arithmetic tutorial
Architecture of Convolutional Neural Networks (CNNs) demystified
Densely Connected Convolutional Networks and Github
Convolutional Methods for Text
Interpreting neurons in an LSTM network
Wide Residual Networks, Github, Keras version
Using 3D Convolutional Neural Networks for Speaker Verification and Github
A simple neural network module for relational reasoning Github Keras Github Pytorch
Gesture recognition via CNN. Implemented in Keras + Theano + OpenCV
Meta-Learning with Temporal Convolutions
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
An Empirical Exploration of Recurrent Network Architectures
Visualizing and Understanding Recurrent Networks
Video - Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
Performance RNN: Generating Music with Expressive Timing and Dynamics
Recurrent Additive Networks
Attention and Augmented Recurrent Neural Networks
Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN
COCO-Stuff dataset and Github
Show and Tell: A Neural Image Caption Generator
DRAW: A Recurrent Neural Network For Image Generation
Colorful Image Colorization
Neural Style Transfer: A Review of academic papers
A Neural Algorithm of Artistic Style
Convolutional neural networks for artistic style transfer
Picking an optimizer for Style Transfer
Intro to Style transfer
Experiments with style transfer
How do these "neural network style transfer" tools work?
Github repositories with code for style transfer:
- https://github.com/slavivanov/Style-Tranfer
- https://github.com/hnarayanan/stylist
- https://github.com/hnarayanan/artistic-style-transfer
- https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py
- https://github.com/erilyth/DeepLearning-Challenges/tree/master/Art_Generation
- https://github.com/jcjohnson/neural-style
- https://github.com/xunhuang1995/AdaIN-style
- https://github.com/ProGamerGov/Neural-Tools
Using Other Neural Models
Artistic style transfer for videos and Torch implementation
Code for "How to Generate Art - Intro to Deep Learning #8' by Siraj Raval on YouTube
A Neural Algorithm of Artistic Style
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization and Github
Visual Attribute Transfer through Deep Image Analogy and Github
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Controlling Perceptual Factors in Neural Style Transfer
cGAN-based Manga Colorization Using a Single Training Image
Incorporating long-range consistency in CNN-based texture generation
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
Semantic Segmentation using Fully Convolutional Networks over the years
A list of all papers on Semantic Segmentation and the datasets they use
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation and Github
Deep Learning for Photo Editing
Very Deep Convolutional Networks for Large-Scale Image Recognition
The "something something" video database for learning and evaluating visual common sense
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
Object-Extent Pooling for Weakly Supervised Single-Shot Localization
The Devil is in the Decoder
YOLO9000: Better, Faster, Stronger
Annotating Object Instances with a Polygon-RNN
The More You Know: Using Knowledge Graphs for Image Classification
Neural Machine Translation (seq2seq) Tutorial and Intro
Massive Exploration of Neural Machine Translation Architectures
Neural Machine Translation in Linear Time
Introduction to Neural Machine Translation with GPUs (part 1)
On the State of the Art of Evaluation in Neural Language Models
Introduction to pointer networks
Video Sequence to Sequence Deep Learning (Quoc Le, Google)
TensorFlow Sequence-to-Sequence Models
Sequence to Sequence Learning with Neural Networks
Introducing tf-seq2seq: An Open Source Sequence-to-Sequence Framework in TensorFlow
Dynamic seq2seq in TensorFlow, step by step
A neural chatbot using sequence to sequence model with attentional decoder
Deep Learning for Chatbots, Part 1 – Introduction
SEQUENCE-TO-SEQUENCE RNNS FOR TEXT SUMMARIZATION
Multi-Scale Context Aggregation by Dilated Convolutions
The Definitive Guide to Natural Language Processing
Ultimate Guide to Understand & Implement Natural Language Processing (with codes in Python)
Attention and Memory in Deep Learning and NLP
Stanford Named Entity Recognizer (NER)
OpenNMT An open-source neural machine translation system
Text summarization with TensorFlow
Generating Sentences from a Continuous Space
Code for "How to Make a Text Summarizer - Intro to Deep Learning #10" by Siraj Raval on Youtube
Recurrent Neural Networks with Word Embeddings
Text Generation With LSTM Recurrent Neural Networks in Python with Keras
Has Deep Learning been applied to automatic text summarization (successfully)?
Text Clustering: Get quick insights from Unstructured Data
Natural Language Processing with Deep Learning is almost human-level accurate. Worse yet, it gets smart!
State-of-the-art neural coreference resolution for chatbots, Github and Paper
Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models
Efficient Estimation of Word Representations in Vector Space
Distributed Representations of Words and Phrases and their Compositionality
Original code for word2vec paper with comments
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
Deep Learning, NLP, and Representations
Understanding vector representations
Demystifying Word2Vec
TensorFlow Vector Representations of Words
Word2Vec Tutorial - The Skip-Gram Model and Pdf
Word2Vec Tutorial Part 2 - Negative Sampling and Pdf
Word2Vec Resources
Word2Vec (Part 1): NLP With Deep Learning with Tensorflow (Skip-gram)
Word2Vec (Part 2): NLP With Deep Learning with Tensorflow (CBOW)
Word2Vec test dataset
Pretrained Character Embeddings for Deep Learning and Automatic Text Generation
Kaggle Bag of Words Meets Bags of Popcorn
fairseq Facebook AI Research Sequence-to-Sequence Toolkit
GloVe: Global Vectors for Word Representation
GloVe: Global Vectors for Word Representation + Implementation
Learning when to skim and when to read
Swivel: Improving Embeddings by Noticing What's Missing
Swivel in Tensorflow
Item2Vec: Neural Item Embedding for Collaborative Filtering
How to Generate a Good Word Embedding?
Enriching Word Vectors with Subword Information
Pre-trained word vectors for 294 languages (fastText) from Facebook
Aligning vector representations
Aligning the fastText vectors of 78 languages
Offline bilingual word vectors, orthogonal transformations and the inverted softmax
ParlAI: A new software platform for dialog research
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data and Github
SentEval - A python tool for evaluating the quality of sentence embeddings
Unsupervised Sentiment Neuron
Generative and Discriminative Text Classification with Recurrent Neural Networks
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
Speech Recognition with Deep Recurrent Neural Networks
WaveNet: A Generative Model for Raw Audio, Paper
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders Github and Intro
Generative Adversarial Networks - nVidia GPU Tech conference 2017 intro
A list of GAN papers
Deep Learning Research Review Week 1: Generative Adversarial Nets
Deep Learning with cats
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
PixelGAN Autoencoders
Do GANs actually learn the distribution? An empirical study
Keras implementation of the Conditional GAN
Fantastic GANs and where to find them
Автоэнкодеры в Keras
Using Deep Learning to Reconstruct High-Resolution Audio
One-shot Learning with Memory-Augmented Neural Networks + Explanation
Video Active One-shot Learning
Differential neural computer from DeepMind and more advances in backward propagation
Google’s DeepMind AI Now Capable of ‘Deep Neural Reasoning’
This is the code for "How to Learn from Little Data - Intro to Deep Learning #17' by Siraj Raval on YouTube
In-Depth on Udacity’s Self-Driving Car Curriculum
Term 2: In-Depth on Udacity’s Self-Driving Car Curriculum
Term 3: In-Depth on Udacity’s Self-Driving Car Curriculum
The Secrets of Term 3 Revealed!
Udacity Self-Driving Car Project Q&As
Udacity Self-Driving car preparation: Essence of linear algebra, Derivatives of multivariable functions
nVidia End-to-End Deep Learning for Self-Driving Cars
Video Amnon Shashua CVPR 2016 keynote: Autonomous Driving, Computer Vision and Machine Learning
Multi-Scale Context Aggregation by Dilated Convolutions and TF Github
An augmentation based deep neural network approach to learn human driving behavior
An Introduction to LIDAR: The Key Self-Driving Car Sensor
An Introduction to the CAN Bus: How to Programmatically Control a Car
A Comparison of Self-Driving Sensors
Under the Hood of a Self-Driving Taxi
Vehicle Detection and Tracking
How do self driving cars drive? Part 1: Lane keeping assist
Hacking my own car: Lessons learnt after a few months of setbacks.
How do self driving cars drive? Part 1: Lane keeping assist
3 Approaches to Vehicle Detection and Tracking
Visual Object Tracking using Adaptive Correlation Filters
Visualizing lidar data
Udacity Students Past, Present, and Future
Youtube lectures on Kalman Filter
Youtube lectures on Model predictive control
Youtube Controlling Self Driving Cars
Autoware
Robot Operating System (ROS)
Baidu Apollo and Github
Open Source Self Driving Car Initiative
Elcano Project autonomous driving for tricycles and like
SELF RACING CARS
Book The Science of Vehicle Dynamics: Handling, Braking, and Ride of Road and Race Cars
comma.ai Our Road to Self Driving Victory
A panda and a cabana: How to get started car hacking with comma.ai
Stanford Code From Cars That Entered DARPA Grand Challenges
BERKELEY AUTONOMOUS RACE CAR
Self Driving RC Car
OpenCV Python Neural Network Autonomous RC Car
Build Your Own Android-Powered Self Driving R/C Car and Github
Arduino Powered Autonomous Vehicle
Autonomous Control of RC Car Using Arduino
40 Excellent Autonomous Mobile Robots on Wheels That You Can Build at Home
Using reinforcement learning in Python to teach a virtual car to avoid obstacles, Part 2, Part 3, Github
The Race to 2021: The State of Autonomous Vehicles and a "Who's Who" of Industry Drivers
Becoming a Self-Driving Car & Machine Learning Engineer
Quora How do I get a job working on autonomous or self-driving cars?
Quora How can one learn to be a self driving car engineer?
The Open Source Car Control Project
Deep Reinforcement Learning: Pong from Pixels
Minimal and Clean Reinforcement Learning Examples
DEMYSTIFYING DEEP REINFORCEMENT LEARNING
Simple Reinforcement Learning article series
Noisy Networks for Exploration
Trust Region Policy Optimization
Proximal Policy Optimization Algorithms, Github and Intro
Faster Physics in Python from OpenAI
Robots that Learn
Releasing the Dexterity Network (Dex-Net) 2.0 Dataset for Deep Grasping
Drone Uses AI and 11,500 Crashes to Learn How to Fly
AIY Projects: Do-it-yourself AI for Makers and Intro
A Raspberry Pi Hexy — Transcript
All the slides (and more) from the 2017 IA Summit
https://medium.com/intuitionmachine/navigating-the-unsupervised-learning-landscape-951bd5842df9
https://blog.twitter.com/engineering/en_us/topics/insights/2017/using-deep-learning-at-scale-in-twitters-timelines.html