These deep neural networks are capable of learning complex patterns and relationships in data, making them well-suited for tasks such as image and speech recognition, natural language processing ...

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 ...

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

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 ...

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.

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 ...

An artificial neural network (ANN) is a type of machine learning model inspired by neurons in the brain. 1,2 The individual components of the ANN receive information in numerical form, process it, and ...

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 ...