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Manning – Deep Learning with Python, Third Edition 2025

Updated August 10, 2026 26 MB
Manning – Deep Learning with Python, Third Edition 2025

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Description

Written by the creator of the famous Keras library, Deep Learning with Python, Third Edition is considered one of the world’s most authoritative sources for entering the world of artificial intelligence. In the third edition, the author rewrites extensive sections, teaching the complex concepts of neural networks in simple, code-oriented language, and guiding the reader from basic topics to the most advanced techniques of the day.

Focusing on modern concepts such as generative AI, large language models (LLMs), and new architectures, Scholle aims to provide developers and data scientists with the tools they need to build intelligent models using practical examples in Keras 3, PyTorch, and JAX frameworks.

Book Features

  • Comprehensive training in deep learning concepts from basic principles to advanced generative models.
  • Full coverage of new Keras 3 features and integration with PyTorch and JAX.
  • Practical training in building GPT-like models and working with diffusion models for image generation.
  • A detailed review of machine vision, natural language processing, and time series forecasting.
  • Providing intuitive explanations and simplified mathematics for a deeper understanding of neural layers.
  • Includes practical projects and coded examples for experiential learning.

Book specifications

Headlines

Deep Learning with Python

Pictures

Deep Learning with Python

User Guide

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Download file – 26 MB

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File size

26 MB

What is included

  • Comprehensive training in deep learning concepts from basic principles to advanced generative models.
  • Full coverage of new Keras 3 features and integration with PyTorch and JAX.
  • Practical training in building GPT-like models and working with diffusion models for image generation.
  • A detailed review of machine vision, natural language processing, and time series forecasting.
  • Providing intuitive explanations and simplified mathematics for a deeper understanding of neural layers.
  • Includes practical projects and coded examples for experiential learning.