Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python

This book provides a comprehensive guide to implementing neural networks and deep learning using TensorFlow 2.0 and Keras. It focuses on fundamental concepts and practical aspects, with code examples and a companion online book. It is suitable for readers with intermediate knowledge of machine learning and programming.

Format: Paperback / softback
Length: 380 pages
Publication date: 29 March 2022
Publisher: APress

This comprehensive guide will help you master the art of implementing neural networks and deep learning using TensorFlow 2.0 and Keras. Whether you're a researcher, data scientist, or engineer, this book will provide you with the knowledge and skills you need to build powerful and efficient machine learning systems.

Understanding Neural Networks:

Neural networks are a powerful tool for solving complex problems in a wide range of fields, including computer vision, natural language processing, and machine learning. In this section, you'll learn the fundamental concepts of how neural networks work, including:

• Activation Functions: Neural networks use activation functions to transform input data into output signals. Different activation functions have different properties, such as sigmoid, tanh, and ReLU, which can affect the performance of the network.

• Layers: Neural networks are composed of multiple layers, each of which performs a specific function. The input layer receives the input data, the hidden layer processes the data, and the output layer generates the output.

• Weight Initialization: Weight initialization is a critical step in training neural networks. The initial weights of the network can significantly impact its performance. You'll learn how to initialize weights using techniques such as random initialization, Xavier initialization, and Glorot Uniform initialization.

• Regularization: Regularization is a technique used to prevent overfitting and improve the generalization of neural networks. You'll learn about techniques such as dropout, weight decay, and L2 regularization, which can help reduce the complexity of the network and improve its performance.

Implementing Neural Networks:

In this section, you'll learn how to implement neural networks using TensorFlow 2.0 and Keras. You'll cover topics such as:

• Building Neural Networks: You'll learn how to build neural networks using TensorFlow's high-level API, including building layers, training the network, and evaluating its performance.

• Training Neural Networks: You'll learn how to train neural networks using TensorFlow's optimizers, such as Adam, SGD, and RMSprop. You'll also learn about techniques such as batch normalization, dropout, and weight decay, which can improve the performance of the network.

• Evaluating Neural Networks: You'll learn how to evaluate the performance of neural networks using TensorFlow's evaluation metrics, such as accuracy, precision, recall, and F1 score. You'll also learn about techniques such as cross-validation and early stopping, which can help optimize the training process.

• Autoencoders and Generative Adversarial Networks: You'll learn about autoencoders and generative adversarial networks, two powerful techniques for unsupervised learning. You'll learn how to build and train autoencoders and generative adversarial networks using TensorFlow and Keras.

A Companion Online Book:

In addition to the book, you'll have access to a companion online book with the complete code for all examples discussed in the book. The online book also includes additional material related to TensorFlow and Keras, such as tutorials, tips, and best practices.

Who This Book Is For:

This book is for readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming. If you're new to machine learning, you may want to start with a more introductory book before diving into this one. However, if you have a solid foundation in machine learning and are ready to take your skills to the next level, this book is for you.

Conclusion:

Implementing neural networks and deep learning can be a challenging task, but with the right level of detail and clarity provided in this book, you'll be able to master the art of building powerful and efficient machine learning systems. Whether you're a researcher, data scientist, or engineer, this book will provide you with the knowledge and skills you need to succeed in your field.

Weight: 768g
Dimension: 253 x 176 x 31 (mm)
ISBN-13: 9781484280195