Deep Learning Resources

Resources

Lessons

• Lesson 1 Notes Tim Lee (Tim’s GitHub repo)
• Lesson 2: Case Study - A world class image classifier for dogs and cats (err…, anything) Apil Tamang
• Lesson 2 Notes Tim Lee
• Lesson 3 Notes Tim Lee
• Lesson 4 Notes Tim Lee

Blog Sites by Author

• Anand Saha
• Apil Tamang

Blogs Written by (or recommended by) fastai Fellows

Resnet

• Decoding the ResNet architecture Anand Saha
• Yet Another ResNet Tutorial (or not) Apil Tamang
• An Overview of ResNet and its Variants Vincent Fung

Stuctured Deep Learning

• Structured Deep Learning Kerem Turgutlu (Masters’ student at USF)

NLP

• Fine-tuned Language Models for Text Classification by Jeremy Howard and Sebastian Ruder

PyTorch

• Transfer Learning using PyTorch — Part 2 Vishnu Subramanian (April 2017)
• A practitioner’s guide to PyTorch by Radek

Learning Rate

• Improving the way we work with learning rate Vitaly Bushaev
• Visualizing Learning rate vs Batch size (Neural Nets basics using Fast.ai tools) Miguel (Nov 2017)
• Estimating an Optimal Learning Rate For a Deep Neural Network Pavel Surmenok
• Cyclical Learning Rate Technique Anand Saha
• Transfer Learning using differential learning rates Manikanta Yadunanda

CNN

• Convolutional Neural Network in 5 minutes Sanyam Bhutani
• CS231n Convolutional Neural Networks for Visual Recognition

Kaggle

• FastAI Kaggle Starter Kit Tim Lee

Jupyter Notebook

• Debugging Jupyter notebooks

and More

• Do smoother areas of the error surface lead to better generalization? (An experiment inspired by the first lecture of the fast.ai MOOC) Radek
• Contributing to fast.ai Wayde Gilliam
• Getting Computers To See Better Than Humans Arjun Rajkumar
• Fun with small image data-sets Nikhil B
• Fun with small image data-sets (Part 2) Nikhil B
• Structured Deep Learning Kerem Turgutlu
• Exploring Stochastic Gradient Descent with Restarts (SGDR) Mark Hoffman
• How do We Train Neural Networks? Vitaly Bushev

Reference Blogs

• Understanding LSTMs Christopher Olah
• Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano Denny Britz

Research Publications

• A systematic study of the class imbalance problem
  in convolutional neural networks
• What’s your ML Test Score? A rubric for ML
  production systems (NIPS 2016)
• ADAM: A Method for Stochastic Optimization (ICLR 2015)
• A disciplined approach to neural network hyper-parameters: Part 1 – learning rate, batch size, momentum, and weight decay Leslie Smith, March 2018
• Cyclical Learning Rates for Training Neural Networks (WACV 2017) Leslie Smith
• Fixing Weight Decay Regularization in Adam Ilya Loshchilov, Frank Hutter (Submitted on 14 Nov 2017)
• Learning Distributed Representations of Concepts Geoffrey Hinton, 1986
• Using the Output Embedding to Improve Language Models

Key Research Papers

• A disciplined approach to neural network hyper-parameters: Part 1 – learning rate, batch size, momentum, and weight decay, Leslie N. Smith, 2018
• Deep Residual Learning for Image Recognition Kaiming He, ILSVRC 2015 classification task winner
• Visualizing and Understanding Convolutional Networks Zeiler & Fergus, 2013

Videos

• The wonderful and terrifying implications of computers that can learn (Ted Talk by Jeremy Howard 2014)
• A Visual and Intuitive Understanding of Deep Learning Otavio Good of Google, AI Conf SF Sep 2017
• Ian Goodfellow - Numerical Computation for Deep Learning - AI With The Best Oct 14-15, 2017
• Ali Rahimi’s talk at NIPS(NIPS 2017 Test-of-time award presentation)

来源：CSDN

作者：DrogoZhang

链接：https://blog.csdn.net/weixin_40400177/article/details/103606457