Table of Contents

• Papers

  • Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
  • GFRNN - supp.pdf)
  • LSTM: A Search Space Odyssey
  • A Critical Review of Recurrent Neural Networks for Sequence Learning
  • Visualizing and Understanding Recurrent Networks
  • Recurrent Neural Network based Language Model
  • Extensions of Recurrent Neural Network Language Model
  • Recurrent Neural Network based Language Modeling in Meeting Recognition
  • Deep Neural Networks for Acoustic Modeling in Speech Recognition
  • Speech Recognition with Deep Recurrent Neural Networks
  • Reinforcement Learning Neural Turing Machines
  • Memory Networks
  • Policy Learning with Continuous Memory States for Partially Observed Robotic Control
  • Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language
  • Neural Turing Machines
  • Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
  • Mastering the Game of Go with Deep Neural Networks and Tree Search
  • Batch Normalization
  • Residual Learning
  • Berkeley AI Research (BAIR) Laboratory
  • MobileNets by Google
  • Cross Audio-Visual Recognition in the Wild Using Deep Learning
  • Dynamic Routing Between Capsules
  • Matrix Capsules With Em Routing
  • Efficient BackProp
  • Generative Adversarial Nets
  • FaceNet: A Unified Embedding for Face Recognition and Clustering
  • Siamese Neural Networks for One-shot Image Recognition
  • Unsupervised Translation of Programming Languages
  • Matching Networks for One Shot Learning
  • VOLO: Vision Outlooker for Visual Recognition
  • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
  • DeepFaceDrawing: Deep Generation of Face Images from Sketches
  • Policy Learning with Continuous Memory States for Partially Observed Robotic Control
  • Memory Networks
  • Reinforcement Learning Neural Turing Machines
  • Recursive Deep Learning for Natural Language Processing and Computer Vision
  • Using Very Deep Autoencoders for Content Based Image Retrieval
  • Learning Deep Architectures for AI
  • Geoff Hinton's reading list (all papers)
  • Supervised Sequence Labelling with Recurrent Neural Networks
  • Training Recurrent Neural Networks
  • Deep Neural Networks for Acoustic Modeling in Speech Recognition
  • Speech Recognition with Deep Recurrent Neural Networks
  • Google - Sequence to Sequence Learning with Neural Networks
  • Efficient BackProp
  • Unsupervised Translation of Programming Languages
  • ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
  • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
  • ImageNet Classification with Deep Convolutional Neural Networks
  • Image-to-Image Translation with Conditional Adversarial Networks
  • GFRNN - supp.pdf)
  • LSTM: A Search Space Odyssey
  • A Critical Review of Recurrent Neural Networks for Sequence Learning
  • Visualizing and Understanding Recurrent Networks
  • Reinforcement Learning Neural Turing Machines
  • Memory Networks
  • Policy Learning with Continuous Memory States for Partially Observed Robotic Control
  • Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
  • Fast R-CNN
  • Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language
  • Generative Adversarial Nets
  • VOLO: Vision Outlooker for Visual Recognition
  • Neural Networks for Named Entity Recognition - ner.zip)
  • Matching Networks for One Shot Learning
  • Recurrent Neural Network based Language Model
  • Extensions of Recurrent Neural Network Language Model
  • Recurrent Neural Network based Language Modeling in Meeting Recognition

• Tutorials

  • Theano Tutorial
  • UFLDL Tutorial 1
  • UFLDL Tutorial 2
  • Neural Networks for Matlab
  • Using convolutional neural nets to detect facial keypoints tutorial
  • Torch7 Tutorials
  • VGG Convolutional Neural Networks Practical
  • Deep Learning with Python
  • Grokking Deep Learning
  • Deep Learning for Search
  • Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder
  • Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras
  • Overview and benchmark of traditional and deep learning models in text classification
  • The Illustrated Self-Supervised Learning
  • Visual Paper Summary: ALBERT (A Lite BERT)
  • Semi-Supervised Deep Learning with GANs for Melanoma Detection
  • Named Entity Recognition using Reformers
  • Deep N-Gram Models on Shakespeare’s works
  • Wide Residual Networks
  • Fashion MNIST using Flax
  • Fake News Classification (with streamlit deployment)
  • Regression Analysis for Primary Biliary Cirrhosis
  • Cross Matching Methods for Astronomical Catalogs
  • Named Entity Recognition using BiDirectional LSTMs
  • Image Recognition App using Tflite and Flutter
  • Deep Learning from the Bottom up
  • UFLDL Tutorial 2
  • The Best Machine Learning Tutorials On The Web
  • TensorFlow tutorials
  • More TensorFlow tutorials
  • TensorFlow Python Notebooks
  • Keras and Lasagne Deep Learning Tutorials
  • Classification on raw time series in TensorFlow with a LSTM RNN
  • TensorFlow-World
  • Pytorch Tutorial by Yunjey Choi
  • Hardware for AI: Understanding computer hardware & build your own computer
  • A Deep Learning Tutorial: From Perceptrons to Deep Networks
  • Hardware for AI: Understanding computer hardware & build your own computer
  • Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras
  • Overview and benchmark of traditional and deep learning models in text classification
  • The Illustrated Self-Supervised Learning
  • Visual Paper Summary: ALBERT (A Lite BERT)

• Videos and Lectures

  • How To Create A Mind
  • Deep Learning, Self-Taught Learning and Unsupervised Feature Learning
  • Recent Developments in Deep Learning
  • The Unreasonable Effectiveness of Deep Learning
  • Deep Learning of Representations
  • Principles of Hierarchical Temporal Memory
  • Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab
  • Making Sense of the World with Deep Learning
  • Demystifying Unsupervised Feature Learning
  • Visual Perception with Deep Learning
  • The Next Generation of Neural Networks
  • The wonderful and terrifying implications of computers that can learn
  • Unsupervised Deep Learning - Stanford
  • Natural Language Processing
  • A beginners Guide to Deep Neural Networks
  • Deep Learning: Intelligence from Big Data
  • Introduction to Artificial Neural Networks and Deep Learning
  • NIPS 2016 lecture and workshop videos - NIPS 2016
  • Deep Learning Crash Course - lectures by Leo Isikdogan on YouTube (2018)
  • Deep Learning Crash Course
  • Deep Learning with R in Motion
  • Medical Imaging with Deep Learning Tutorial - ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting tasks.
  • Deepmind x UCL Deeplearning
  • Deepmind x UCL Reinforcement Learning
  • CMU 11-785 Intro to Deep learning Spring 2020 - 785, Intro to Deep Learning by Bhiksha Raj
  • What is Neural Structured Learning by Andrew Ferlitsch
  • Deep Learning Design Patterns by Andrew Ferlitsch
  • Architecture of a Modern CNN: the design pattern approach by Andrew Ferlitsch
  • Metaparameters in a CNN by Andrew Ferlitsch
  • Multi-task CNN: a real-world example by Andrew Ferlitsch
  • A friendly introduction to deep reinforcement learning by Luis Serrano
  • What are GANs and how do they work? by Edward Raff
  • Coding a basic WGAN in PyTorch by Edward Raff
  • Training a Reinforcement Learning Agent by Miguel Morales
  • Understand what is Deep Learning
  • A beginners Guide to Deep Neural Networks
  • Unsupervised Deep Learning - Stanford
  • Medical Imaging with Deep Learning Tutorial - ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting tasks.
  • Deepmind x UCL Reinforcement Learning
  • CMU 11-785 Intro to Deep learning Spring 2020 - 785, Intro to Deep Learning by Bhiksha Raj
  • What is Neural Structured Learning by Andrew Ferlitsch
  • Deep Learning Design Patterns by Andrew Ferlitsch
  • Architecture of a Modern CNN: the design pattern approach by Andrew Ferlitsch
  • Metaparameters in a CNN by Andrew Ferlitsch
  • Multi-task CNN: a real-world example by Andrew Ferlitsch
  • A friendly introduction to deep reinforcement learning by Luis Serrano
  • What are GANs and how do they work? by Edward Raff
  • Coding a basic WGAN in PyTorch by Edward Raff
  • Training a Reinforcement Learning Agent by Miguel Morales
  • Recent Developments in Deep Learning
  • Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab
  • Deep Learning Crash Course - lectures by Leo Isikdogan on YouTube (2018)
  • The wonderful and terrifying implications of computers that can learn
  • Making Sense of the World with Deep Learning