Mastering TensorFlow - Deep Learning with Practical Examples & Code

Preface

Artificial Intelligence (AI) has redefined how machines learn, think, and make decisions. At the heart of this revolution lies deep learning—and one of its most widely adopted frameworks is TensorFlow.

Developed by Google Brain, TensorFlow empowers developers, researchers, and data scientists to build intelligent systems capable of perceiving, understanding, and interacting with the world in human-like ways. From recommendation systems and self-driving cars to speech recognition and medical diagnostics, TensorFlow provides the computational foundation behind some of today’s most advanced applications.

This book—“Mastering TensorFlow”—is designed for professionals who want to go beyond basic tutorials and truly master TensorFlow end-to-end. Through hands-on examples, real-world case studies, and fully functional Python code, we’ll explore how to architect, train, and deploy machine learning and deep learning models with confidence.
You’ll not only learn how TensorFlow works but also understand the why behind it — from computational graphs and automatic differentiation to distributed training, transfer learning, and deployment on cloud or mobile.

Who This Book Is For
Developers transitioning into machine learning and AI.
Data scientists seeking scalable, production-ready model training.
Researchers exploring neural architectures and TensorFlow internals.
Anyone looking to master TensorFlow’s real-world applications.

What You Will Learn
TensorFlow fundamentals, architecture, and execution model.
Data pipelines using tf.data, TFRecord, and preprocessing layers.
Building and training deep neural networks with Keras and tf.function.
Convolutional, Recurrent, and Transformer-based models.
Model optimization, callbacks, regularization, and checkpoints.
Transfer learning and fine-tuning with pre-trained networks.
Distributed training and mixed-precision computation.
Model deployment — TensorFlow Serving, TF Lite, and TensorFlow.js.
Real-world case studies — image classification, NLP, and time series forecasting.
Tips for debugging, performance tuning, and scaling TensorFlow models.