Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks.

This repository contain hands-on, practical knowledge of how to use neural networks and deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning in 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 ... Students must be competent in Python and NumPy – or be able to ...

It provides a flexible platform for building and training deep learning models. It’s known for its scalability and performance. Keras is a high-level neural networks API written in Python.

Recurrent neural networks (RNNs) are a type of deep learning model that can capture ... In this article, you will learn how to use RNNs with Python for some common NLP tasks, such as sentiment ...

One of the main challenges of neural networks and deep learning is the need for large amounts of data and computational resources. Neural networks learn from data by adjusting their parameters to ...

Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the ...

In my previous article, we explored how artificial intelligence and machine learning help Company Z, our online retail example, to predict customer behaviour, segment audiences and optimise ...