Artificial Neural Networks with TensorFlow 2 - Practical ANN Projects

In this review of Artificial Neural Networks with TensorFlow 2, the bottom line is clear: this book is for developers and students who want hands-on, project-based exposure to a wide range of ANN architectures. The author walks readers from foundational sequential models to advanced networks such as CNNs, RNNs, LSTMs and DCGANs, making it easy to apply TensorFlow 2 concepts to real problems. The single biggest reason to buy is the chapter-per-project format that covers architecture, data, preprocessing, training and evaluation in one place.

Key Features

• Project-based chapters: Each chapter presents a full project that explains the network architecture and the practical steps to reproduce results, which helps readers learn by doing.
• Range of ANN types: Coverage spans simple sequential networks to convolutional, recurrent and generative adversarial networks so readers gain broad exposure to modern models.
• Data and preprocessing details: The book describes datasets used and preprocessing approaches, making experiments easier to reproduce and adapt.
• Training and optimization guidance: Each project includes model training, testing and performance optimization notes to improve practical model results.
• Theory plus practice: The author combines architectural theory with implementation steps so readers understand why a network is built a certain way.

Who It's For

This book is best suited for intermediate programmers, machine learning students and practitioners who already have some Python and basic ML knowledge and want to apply TensorFlow 2 to concrete tasks. It is especially useful for learners who benefit from scenario-based projects that tie theory directly to code and evaluation.

Those seeking an introductory primer with extensive statistics, or a short quick-reference pocket guide, should look elsewhere; the book is organized around full projects and assumes time for coding and experimentation rather than a rapid overview.

Pros & Cons

Pros

• Clear, project-focused chapters that make reproducing experiments straightforward.
• Wide coverage of ANN architectures gives readers practical experience across CNN, RNN, LSTM and DCGAN models.
• Each project includes data preprocessing, training, testing and optimization guidance for applied learning.

Cons

• Not a quick reference; projects require time and prior programming familiarity to follow effectively.

Specifications

Title	Artificial Neural Networks with TensorFlow 2
Author	Poornachandra Sarang
Focus	Project-based ANN architectures and implementations
Framework	TensorFlow 2
Topics covered	Sequential, CNN, RNN, LSTM, DCGAN and other ANN types
Includes	Dataset descriptions, preprocessing, training, testing and optimization

Our Verdict

Artificial Neural Networks with TensorFlow 2 is a solid, practical resource for intermediate learners who want to implement and experiment with a wide variety of ANN architectures. The chapter-per-project approach delivers good value for readers who plan to code through the examples and apply the techniques to their own datasets.

Frequently Asked Questions

Does this book use TensorFlow 2 for examples?
Yes, all projects and implementations are presented using TensorFlow 2 as the primary framework.

Are the projects suitable for beginners?
The projects are best for readers with basic Python and ML knowledge; absolute beginners may find the pace brisk.

What types of networks are covered?
The book covers sequential networks, CNNs, RNNs, LSTMs, DCGANs and other common ANN architectures with full project breakdowns.