Deep Learning with TensorFlow and Keras

Deep learning is transforming software, science, business, and artificial intelligence.

But understanding neural networks requires more than knowing how to call a machine learning API.

Deep Learning with TensorFlow and Keras takes you from the foundations of neural networks to practical deep learning systems that can be trained, evaluated, optimized, and deployed.

You will learn how modern deep learning models work—and how to build them yourself using TensorFlow 2 and Keras.

Inside this practical guide, you will explore:

• Neural networks, neurons, activation functions, loss functions, backpropagation, and weight initialization

• Training techniques including Adam, SGD, AdamW, learning-rate schedules, batch normalization, regularization, and callbacks

• Convolutional Neural Networks for image classification, including CIFAR-10 and data augmentation

• Transfer learning and fine-tuning with pretrained models

• Recurrent Neural Networks, LSTM, and GRU architectures for sequential data

• Text classification, embeddings, pretrained GloVe vectors, and end-to-end text pipelines

• Time-series forecasting, multivariate prediction, and anomaly detection

• Advanced Keras techniques including custom layers, model subclassing, custom training loops, and tf.data pipelines

• Generative models including autoencoders, variational autoencoders, and GANs

• Object detection, IoU, pretrained detectors, and bounding-box processing

• Model optimization with quantization, TensorFlow Lite, pruning, and knowledge distillation

• Production deployment using SavedModel, FastAPI, and MLflow

• Mixed-precision training, distributed training, and advanced data pipelines

• Attention mechanisms and Transformer encoder architectures

• Complete end-to-end projects involving plant disease detection, sentiment analysis APIs, LSTM forecasting, and image generation

The book follows a practical learning progression: intuition first, mathematics where it matters, and working Python code throughout.

Rather than treating deep learning as a black box, it helps you understand what happens inside your models, diagnose training problems, make informed architectural decisions, and move from experimentation toward production.

Whether you are building your first neural network or strengthening your practical deep learning skills, this book provides a structured path through the technologies and architectures behind modern AI.