http://neuralnetworksanddeeplearning.com/chap1.html Up to now, we've been discussing neural networks where the output from one layer is used as input to the next layer. Such networks are called feedforward neural networks. This means there are no loo…

The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. In each hemisphere of our brain, humans have a prima…

In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisition…

When a golf player is first learning to play golf, they usually spend most of their time developing a basic swing. Only gradually do they develop other shots, learning to chip, draw and fade the ball, building on and modifying their basic swing. In a…

R2RT   Written Memories: Understanding, Deriving and Extending the LSTM Tue 26 July 2016 When I was first introduced to Long Short-Term Memory networks (LSTMs), it was hard to look past their complexity. I didn’t understand why they were designed the…

Introduction to Deep Neural Networks Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw…

A Statistical View of Deep Learning (V): Generalisation and Regularisation We now routinely build complex, highly-parameterised models in an effort to address the complexities of modern data sets. We design our models so that they have enough 'capaci…

Machine Learning Crash Course  |  Google Developers https://developers.google.com/machine-learning/crash-course/ Google's fast-paced, practical introduction to machine learning ML Concepts Introduction to Machine Learning As you'll discover, machine…

CS231n Winter 2016: Lecture 5: Neural Networks Part 2 CS231n Winter 2016: Lecture 6: Neural Networks Part 3 by Andrej Karpathy 本章节主要讲解激活函数,参数初始化以及周边的知识体系. Ref: <深度学习>第八章 - 深度模型中的优化 Overview 1. One time setup activation functions, preprocessing, weig…

ICLR 2014 International Conference on Learning Representations Apr 14 - 16, 2014, Banff, Canada Workshop Track Submitted Papers Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence Mathias Berglund, Ta…