Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures.

Key features:

• Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals.

• Explains and provides examples of deploying TensorFlow and Keras in several projects.

• Explains the fundamentals of Artificial Neural Networks (ANNs).

• Presents several examples and applications of ANNs.

• Learning the most popular DL algorithms features.

• Explains and provides examples for the DL algorithms that are presented in this book.

• Analyzes the DL network’s parameter and hyperparameters.

• Reviews state-of-the-art DL examples.

• Necessary and main steps for DL modeling.

• Implements a Virtual Assistant Robot (VAR) using DL methods.

• Necessary and fundamental information to choose a proper DL algorithm.

• Gives instructions to learn how to optimize your DL model IN PRACTICE.

Description:

This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step.