# Stanford Deep Learning

One of the best Neural Networks/ Deep Learning tutorials can be found here. It is written by Andrew Ng, or as I think of him the best Machine Learning Sensei.

I have been doing the tutorial recently and will be giving hints as I go. So far I am upto the Convolutional Neural Network part (as of Sep 21).

The code is available in my github repository: https://github.com/sachinruk/Standford_DL

## Important Hints

1. When doing the 1st Deep Neural Network exercise (supervised_dnn_cost.m) remember that error component $\delta^{(l)}$ is calculated for each individual example seperately. When calculating the gradient for $W$ we use $\delta^{(l+1)} {a^{(l)}}^T$. The transpose is important.

This blog post will be edited in the coming days. #