Both libraries provide high-level APIs for building and training neural networks, making it easy for developers to get started. What are some popular deep learning libraries for Python?

By the end of the book, you’ll pack everything into a complete Python deep learning library, creating your own class hierarchy of layers, activation functions, and neural network architectures ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

This video is an overall package to understand Dropout in Neural Network and then implement it in Python from scratch.

Born in the 1950s, the concept of an artificial neural network has progressed considerably. Today, known as “deep learning”, its uses have expanded to many areas, including finance.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically ...

Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We'll talk about how the math of ...

Artificial Neural Networks: Learning by Doing Designed to mimic the brain itself, artificial neural networks use mathematical equations to identify and predict patterns in datasets and images.

Although neural networks have been studied for decades, over the past couple of years there have been many small but significant changes in the default techniques used. For example, ReLU (rectified ...