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

Learn how to create a simple neural network, and a more accurate convolutional neural network, with the PyTorch deep learning library PyTorch is a Python-based tensor computing library with high ...

Create a fully connected feedforward neural network from the ground up with Python — unlock the power of deep learning!

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?

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.

Neural Networks and Deep Learning have lent enormous success to researchers in tasks such as image recognition, speech recognition, and finding deeper relations in data sets.

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 ...