Regular expressions are a powerful tool for pattern matching. But how do they work? Let's dive into the world of finite state machines.

Convolutional Neural Networks (CNNs) are a class of deep neural networks used for analyzing grid-like data. They are also known as ConvNets. CNNs have proven to be highly effective in areas such as image recognition and classification. In this article, we will breifly explore how CNNs work and how they can be visualized to gain insights into their inner workings.

As software development continues to sprint forward, industry professionals are constantly adapting to new trends and technologies. These shifts promise to carry us into a future where development is faster, smarter, and more accessible. This non-exhaustive list delves into some of the latest trends that are charting new courses in the software development realm.

Factorized convolutions are a method to reduce the computational cost of convolutions, especially in deep neural networks. By decomposing or "factorizing" a standard convolution into multiple simpler convolutions, we can often achieve a reduction in the number of parameters and computations, while maintaining similar (or sometimes even better) performance.

Go was built for the modern world. So let's talk about encoding and decoding the most popular data-interchange format.

Learn how to authenticate users using JSON web tokens and the Django REST Framework.