Python Deep Learning

Introduction

The book «Python Deep Learning» by Ivan Vasilev, published in 2019, is a practical guide to deep learning in Python. This edition is aimed at developers who are already familiar with the basics of machine learning and want to delve into the world of neural networks, convolutional architectures, recurrent models, and generative algorithms. The book combines theoretical explanations with a large number of code examples, making it useful both for self-study and as a reference.

What the book is about

The book covers a wide range of topics, from the basics of neural networks to modern approaches such as Generative Adversarial Networks (GANs) and reinforcement learning. The author consistently guides the reader from simple linear models to complex multi-layer architectures. Special attention is paid to practical implementation: all examples are written in Python using popular libraries (TensorFlow, Keras, PyTorch).

Structure of the edition

The book consists of 12 chapters, each dedicated to a specific aspect of deep learning:

• Chapters 1-2: Introduction to deep learning, mathematical foundations (linear algebra, differentiation, gradient descent).
• Chapters 3-4: Fully connected neural networks, activation functions, regularization, optimization.
• Chapters 5-6: Convolutional Neural Networks (CNN) for image processing, architectures LeNet, AlexNet, VGG, ResNet.
• Chapters 7-8: Recurrent Neural Networks (RNN), LSTM, GRU, sequence and text processing.
• Chapter 9: Autoencoders and their application for data compression and denoising.
• Chapter 10: Generative Adversarial Networks (GANs) — from simple DCGAN to StyleGAN.
• Chapter 11: Reinforcement learning (Q-learning, DQN).
• Chapter 12: Deploying models to production (Flask, Docker, TensorFlow Serving).

Who this book is for

The book is intended for Python developers who already have a basic understanding of machine learning (linear regression, decision trees) and want to move on to neural networks. It will be useful for:

• Data Scientists looking to expand their toolkit.
• Engineers implementing models in real-world projects.
• Students in technical fields studying deep learning.
• Programmers interested in computer vision, NLP, or generative models.

What the reader will learn

After reading the book, the reader will be able to:

• Understand the mathematical foundations of neural networks (gradient descent, backpropagation).
• Implement fully connected, convolutional, and recurrent networks in Keras and PyTorch.
• Train models for image classification, text analysis, and time series.
• Use pre-trained architectures (ResNet, BERT) for transfer learning.
• Create generative models (GANs) and autoencoders.
• Apply reinforcement learning to control tasks.
• Deploy trained models as web services.

Strengths

The book stands out with the following advantages:

• Practical focus: every theoretical block is accompanied by code that can be run immediately in Jupyter Notebook.
• Modernity (as of 2019): covers architectures and libraries that were current at the time of publication.
• Clear language: complex concepts are explained without excessive mathematical rigor, but with sufficient depth.
• Broad coverage: from basics to deployment — the book covers the full model lifecycle.
• Numerous examples: over 50 code examples with comments.

Who it is recommended for