-- Ordered collection of my Deep Learning reading material
- A Deep Dive into Recurrent Neural Networks
- On Bayesian Classifier
- Deep Learning Summit
- Hacker's guide to Neural Networks
- Shawn Tan's Blog
based on LISA Lab Reading List
- Siganos And Stergiou : Neural Networks
- Backpropagation : The Original Article
- Hinton's Connectionist Symbol Processing
- LeCun's Theoretical Framework for Backpropagation
- Geman's Neural Network and the Bias/Variance Dilemma
- Micheal I Jordan's Neural Network
- Richard Zemel's Lecture Notes on Machine Learning and Data Mining
- Hintons's Lecture Notes on Neural Networks Course
- Andrew Moore's Statistical Data Mining Slides
- Probability for Data Miners
- Probability Density Functions
- Gaussians
- Maximum Likelihood Estimation
- Cross Validation
- Regression and Classification with Neural Networks
- AI Introduction
- LeCun's Efficient Backpropagation
- LeCun and Bengio : Pattern Recognition and Neural Networks
- Dietterich's Machine Learning for Sequential Data
- Neal's Learning Stochastic Feed Forward Networks
- Hinton's Wake-Sleep Algorithm for Unsupervised Neural Network
- LeCun and Bengio : Gradient-Based Learning Applied to Document Recognition
- Introduction
- Linear Algebra
- Probability
- Convex Optimization
- SGD
- From Frequency to Meaning : VSM
- Simple Word Vector representations
- Advanced word vector representations
- Neural Networks and backpropagation
- A few useful things to know about in Machine Learning
- A Unifying Review of Linear Gaussian Models