CODE A NEURAL NETWORK IN PLAIN NUMPY Part 2: Planar data classification with one hidden layer

In the last post we have seen neural network with only two layers that is "Input layer" and "Output layer", which is like a logistic regression algorithm. However in this post we are going to code a Neural network with one more layer that is "hidden layer". You will learn how to: Implement a 2-class … Continue reading CODE A NEURAL NETWORK IN PLAIN NUMPY Part 2: Planar data classification with one hidden layer

Code a Neural Network in plain NumPy: Part 1 (with no Hidden Layer)

You will learn to: Build the general architecture of a learning algorithm, including: Initializing parameters Calculating the cost function and its gradient Using an optimization algorithm (gradient descent) Gather all three functions above into a main model function, in the right order. Overview of the Problem set Problem Statement: You are given a dataset containing: … Continue reading Code a Neural Network in plain NumPy: Part 1 (with no Hidden Layer)

Understanding how deep learning works:

We know that machine learning is about mapping inputs (such as images) to targets (such as the label “cat”), which is done by observing many examples of input and targets. We also know that deep neural networks do this input-to-target mapping via a deep sequence of simple data transformations (layers) and that these data transformations … Continue reading Understanding how deep learning works: