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.

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 ...

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?

In this course, we’ll examine the history of neural networks and state-of-the-art approaches to deep learning. Students will learn to design neural network architectures and training procedures via ...

The neural network uses this training data to extract and assign weights to features that are unique to fruits labelled good, such as ideal size, shape, color, consistency of color and so on.

This collection welcomes submissions on explainability techniques for deep learning neural networks, encompassing diverse neural architectures and ensuring broad applicability to different domains.

The deep learning pioneers believe that better neural network architectures will eventually lead to all aspects of human and animal intelligence, including symbol manipulation, reasoning, causal ...

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.