Introduction The vectorized operations have been discussed in the last post Maths in a Neural Network: Vectorization. This post will focus on implementing the equations with numpy. Equations proved in the previous postsÂ [1] [2]: Note that this network takes one sample input at a time, I'll discuss batch prediction/training later. Feed-forward: $latex A^{(l)}= f^{(l)}(Z^{(l)})&s=2$ $latex … Continue reading Code a Neural Network with Numpy

# Category: Machine Learning

# Maths in a Neural Network: Vectorization

Introduction The element-wise operations have been discussed in the last postÂ Maths in a Neural Network: Element-wise. This post will focus on how to represent all the equations found in the previous post in vectors. 1 Feed-forward Let's consider a 2-3-2 network. 1.1 Element-wise operations $latex a^{(l)}_p = f^{(l)}(z^{(l)}_p)\quad (1)&s=2$ $latex z^{(l)}_p = \sum_q w^{(l)}_{(qp)} a^{(l-1)}_q … Continue reading Maths in a Neural Network: Vectorization

# Maths in a Neural Network: Element-wise

In this post, I try to discuss the element-wise operations of the entire Neural Network algorithm first before doing any vectorization. Because I believe it is easier to focus on one thing at a time.