With countless options to design neural networks, an effective architecture search algorithm would be game-changing. Here we look at the state of the art.
Here we see how to speed up NumPy array processing using Cython. By explicitly declaring the "ndarray" data type, your array processing can be 1250x faster.
Python might be one of today's most popular languages, but it's definitely not the most efficient. See how Cython can easily boost your Python scripts.
This is the second tutorial in the series which discusses extending the implementation for allowing the GD algorithm to work with any number of inputs in the input layer.
In this tutorial, which is the Part 1 of the series, we are going to make a worm start by implementing the GD for just a specific ANN architecture in which there is an input layer with 1 input and an output layer with 1 output.
In this post, we take a look at a problem that plagues training of neural networks, pathological curvature.
An in-depth explanation of Gradient Descent, and how to avoid the problems of local minima and saddle points.
