Browsing All posts tagged under »deep learning«

Convolutional Networks

December 30, 2016


Continuing further with Deep Learning, here I will briefly describe what I learned on convolutional network (CNN). If you understand the basics of a simple 2-layer network (fully connected) and can implement it yourself from scratch you are all set to understand the mighty daddy (ie. CNN). Again it is important to understand that CNN, […]

Deep Learning Overview

July 25, 2016


View my Deep Learning Overview : [Google Slides] Deep Learning Research Projects: [Google Slides] Beware, these things get out of date very quick. This presentation is from Oct 2016. The outline of the talk: Toy Neural Network Loss Function Stochastic Gradient Descent Forward-pass (Neural Function Evaluation) Backward-pass (Gradient of Neural function wrt to params) Recent […]

Toy Neural Network

June 11, 2016


In my last post on neural network [HERE], I talked on how one can think of neural network as universal approximators. In this post I am trying to help understand a toy neural network implementation. In particular one can have a clearer and intuitive understanding of what a forward_pass is and what back_propagation means. Most […]

Neural Network as Universal Approximators : Intuitive Explaination

May 31, 2016


Came across this wonderful explanation of why the neural network with hidden layer are universal approximators. Although not very helpful for practical purpose gives an intuitive feel of why neural network give reasonable results. The basic idea is to analyze a sigmoid function as you change w and b . In particular effect on as one varies w and […]