Deep Learning in Practice provides a comprehensive guide to developing and optimizing deep learning models for projects. It covers Python, NumPy, TensorFlow, Artificial Neural Networks (ANNs), popular DL algorithms, parameter and hyperparameter analysis, state-of-the-art examples, and a Virtual Assistant Robot (VAR) implementation. It is suitable for undergraduate and graduate students, practitioners, and as a reference for learning deep learning fundamentals and implementing models.

Format: Hardback
Length: 198 pages
Publication date: 01 December 2021
Publisher: Taylor & Francis Ltd

Deep Learning in Practice

Deep Learning in Practice is a comprehensive guide that empowers you to develop and optimize models using Deep Learning (DL) methods and architectures. Whether you're a beginner or an experienced practitioner, this book will help you master the fundamentals of Python, NumPy, and TensorFlow, and deploy TensorFlow and Keras in various projects.

It delves into the realm of Artificial Neural Networks (ANNs), presenting numerous examples and applications. You'll learn about the most popular DL algorithms, their features, and how to implement them effectively. The book also analyzes the parameters and hyperparameters of DL networks, providing insights into state-of-the-art examples.

To ensure your success, the book covers essential steps for DL modeling, including implementing a Virtual Assistant Robot (VAR) using DL methods. It provides crucial information for choosing the right DL algorithm and offers step-by-step instructions to optimize your DL model.

This book is designed for undergraduate and graduate students, as well as practitioners in industry and academia. It serves as a valuable reference for learning deep learning fundamentals and implementing a deep learning model for any project, ensuring a seamless and efficient journey toward AI excellence.

Weight: 472g
Dimension: 160 x 241 x 21 (mm)
ISBN-13: 9780367458621