This book becomes useful the moment you need to bring math, data, and code together instead of treating them as three separate topics.
The chapter moves from basic algorithms and data analysis into first hands-on experiments in TensorFlow, Keras, and PyTorch, where the point is not the library names but the habit of running models and comparing results.
For interviews and architecture conversations, it works as a gentle entry point that ties machine learning fundamentals to how engineers actually learn to work with data, models, and experiments.
Practical value of this chapter
Deep learning base
The chapter quickly turns math, classical machine learning algorithms, and first deep learning ideas into one coherent map.
Tooling practice
TensorFlow, Keras, and PyTorch make it easy to see how the same path from idea to experiment looks across different toolchains.
Bridge to modern AI
Once this base is in place, it becomes much easier to move into large language models and AI engineering without treating modern systems as just a list of new names.
Interview material
The book helps you explain not only the terminology, but also how an engineer moves from theory to first experiments and working solutions.
Source
Telegram: Book Cube
A detailed review with chapter-by-chapter notes and a practical reading perspective.
Глубокое обучение и анализ данных. Практическое руководство
Authors: Дмитрий Малов
Publisher: БХВ-Петербург, 2023
Length: 270 pages
A concise and practical introduction to deep learning and data analysis: fundamentals, classical machine learning algorithms, applied tasks, and hands-on work with TensorFlow, Keras, and PyTorch.
What this book covers
This is a short and approachable introduction to deep learning and data analysis: mathematical foundations and classical machine learning algorithms first, then applied tasks and hands-on work in three popular frameworks.
The book is especially useful if you want to move quickly from classical ML ideas to neural networks and first practical experiments without getting lost in too much theory.
Structure: 8 chapters
Machine learning foundations
Linear algebra, probability basics, and core machine learning task framing: classification, regression, anomaly detection, translation, and synthesis. Includes a short introduction to Python, OOP, and the development process.
Core machine learning algorithms
Data preprocessing, dimensionality reduction, linear and logistic regression, decision trees, support vector machines, naive Bayes, k-means, kNN, random forest, and gradient boosting.
Deep learning foundations
Backpropagation, perceptron, Markov chains, Boltzmann machines, Hopfield networks, convolutional and recurrent networks, transformers, autoencoders, and GANs.
Data science foundations
CRISP-DM methodology, key roles in an ML team, trends such as deepfakes, AutoML, and MLOps, plus an overview of TensorFlow, PyTorch, and Keras.
Deep learning tasks
Hands-on examples: data augmentation, computer vision with OpenCV, symbol recognition, text processing, audio processing, and video processing.
TensorFlow
Applying chapter 5 tasks using TensorFlow.
Keras
Rebuilding practical tasks through the higher-level Keras API.
PyTorch
Implementing the same class of tasks in the PyTorch ecosystem.
What is most useful
- A compact and readable intro that quickly maps the path from math basics to applied tasks.
- The companion repository works well as a starting point for hands-on experimentation.
- It serves as a solid bridge between introductory theory and first practical projects.
What to keep in mind
- Coverage is broad, so many topics remain high-level rather than deep.
- Chapter 2 is dense, and complete beginners may struggle with the pace and context.
- To build real practical confidence, you will likely need additional sources on math and machine learning.
Practice
GitHub repository
Code and charts from the book that make it easy to replay the examples and build your own variations.
How to get maximum value from it
The best way to read it is to move quickly through the foundational chapters and practice in parallel: topics like backpropagation are much easier to retain once you see them in code.
Use chapters 1-4 to align vocabulary and fundamentals.
While working through chapters 5-8, run the repository code and modify parameters, data, and architectures by hand.
After finishing, move on to resources about real AI systems, large language models, and modern AI engineering.
Related chapters
- Why engineers should know ML and AI - Provides the AI/ML section map and shows where this book fits in the broader learning path.
- Precision and Recall Basics - Reinforces core classification-quality metrics needed for practical tasks from the book.
- AI Engineering (short summary) - Extends the path from foundational ML practice to engineering AI systems where quality, cost, and operations matter.
- Hands-On Large Language Models (short summary) - Bridges from classical deep learning to large language models, vector representations, and RAG architectures.
- Prompt Engineering for LLMs (short summary) - Extends the applied track with prompt and context design practices for LLM applications.