15 Best Free Deep Learning eBooks for Beginners in 2026
Deep learning has transformed the fields of artificial intelligence and machine learning in recent years. As a subset of machine learning, deep learning utilizes artificial neural networks to process data and make intelligent decisions in a way that mimics the human brain. Its applications span computer vision, natural language processing, speech recognition, autonomous driving, and much more.
While the mathematical theory behind deep learning can be complex, getting started with the practical applications is within reach for beginners thanks to the availability of powerful open source frameworks and an abundance of learning resources. Among these resources, free deep learning eBooks are a fantastic way to build foundational knowledge before moving on to online courses and hands-on projects.
To help you navigate the sea of free content, we‘ve compiled this list of the 15 best free deep learning eBooks for beginners in 2024. When making our selections, we considered the following key factors:
Relevance and Coverage: Each book covers important deep learning concepts and techniques that are widely applicable. We made sure to include books that discuss foundational topics like neural networks and popular architectures like convolutional neural networks, recurrent neural networks, and transformers.
Authoritativeness: Many books are written by highly respected deep learning experts and pioneers, such as Ian Goodfellow, Yoshua Bengio, François Chollet. This ensures the content is accurate and insightful.
Accessibility: Every eBook on our list is available to read for free online or download in a format like PDF or EPUB. This makes them easy to access for learners around the world.
Uniqueness: While having books that thoroughly cover the deep learning essentials is important, we also sought to include books with unique perspectives. This includes books that dive deep into niche topics like probabilistic deep learning, or show how to implement deep learning using less commonly covered languages like R.
Diversity of Topics: Collectively, the books on our list span a wide range of deep learning topics and applications. Whether your interests lie in computer vision, natural language processing, generative models, or deep reinforcement learning, there are books to match.
Practicality: We prioritized books that include hands-on coding tutorials and projects so you can practice implementing deep learning models in Python and other languages. Some selections even guide you through the process of deploying models to the cloud, mobile apps, and edge devices.
Without further ado, let‘s dive into our picks for the 15 best free deep learning eBooks for beginners in 2024:
1. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Widely regarded as the "bible" of deep learning, this comprehensive book covers the field from essential theory to practice. The authors are some of the world‘s leading experts, with Goodfellow known for inventing generative adversarial networks (GANs).
While the full book is quite advanced, the early chapters provide an excellent foundation for beginners. Topics covered include:
- Linear algebra and probability theory
- Numerical computation
- Machine learning basics
- Deep feedforward networks
- Regularization for deep learning
- Optimization for training deep models
- Convolutional networks
- Sequence modeling: recurrent and recursive nets
- Practical methodology
- Applications
Why beginners should read it: The first few chapters cover essential mathematical building blocks and provide a thorough introduction to modern deep learning methods. Even if you don‘t work through the entire book, you‘ll gain a solid conceptual understanding of the field.
Where to get it: Read online at deeplearningbook.org
2. Deep Learning with Python by François Chollet
If you‘re eager to start building deep learning models, this book is the perfect place to begin. Written by François Chollet, the creator of the Keras deep learning framework, it‘s a clear and practical guide to implementing models in Python.
You‘ll start with the basics of neural networks and progress to building sophisticated models for computer vision, natural language processing, and generative applications. Hands-on projects include:
- Classifying newswires
- Classifying movie reviews
- Predicting house prices
- Generating text with LSTM
- Turning text into sequences with recurrent neural networks
- Building a question answering system
- Neural style transfer
- Generating images with variational autoencoders
- Introduction to deep reinforcement learning
Why beginners should read it: The combination of clear explanations and hands-on code makes this one of the best resources for gaining practical deep learning skills. The projects cover a variety of applications so you can practice applying techniques to real-world problems.
Where to get it: GitHub repo with Jupyter notebooks
3. Dive into Deep Learning by Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola
Dive into Deep Learning is an interactive book with code, math, and discussions. The authors include professors and engineers from Amazon, Google, and top universities. The book is a boon for beginners as it starts from scratch with a focus on hands-on implementation.
The book describes:
- What deep learning is and how it works
- Key concepts and techniques used in deep learning
- Building deep learning models using popular Python frameworks
- Math involved in finding weights and discovering patterns
- Methods for training deep learning models
- Using visualization to understand neural networks
- Techniques for improving generalization and optimization
- Implementing models for computer vision and natural language processing
Why beginners should read it: The interactive notebook format and gradual difficulty progression make this an engaging resource for gaining hands-on experience. Extensive examples cover both fundamental concepts and advanced techniques.
Where to get it: Read online at d2l.ai
4. Neural Networks and Deep Learning by Michael Nielsen
This online book is written as an accessible introduction to neural networks and deep learning. Michael Nielsen comes from a physics background and takes an approach grounded in simple explanations and fun experiments to build intuition.
The book covers:
- Using neural networks to recognize handwritten digits
- How the backpropagation algorithm works
- Improving the way neural networks learn
- Deep learning and neural networks in practice
- Future impact of neural networks and deep learning
Why beginners should read it: Nielsen‘s intuitive analogies and thought experiments bring abstract concepts to life. The book focuses more on developing solid foundations than covering a breadth of architectures, making it perfect for beginners. You‘ll also implement fun experiments like teaching neural networks to play video games.
Where to get it: Read online at neuralnetworksanddeeplearning.com
5. Probabilistic Deep Learning by Oliver Dürr, Beate Sick, and Elvis Murina
Deep learning models are often deterministic, meaning they give one prediction without indicating uncertainty. This book shows how to quantify uncertainty by taking a probabilistic approach to deep learning. You‘ll learn to leverage probability theory to make your models more robust and interpretable.
Topics include:
- Bayes for beginners
- The probabilistic deep learning view
- How neural networks learn complex functions
- Probabilistic neural networks with Keras and TensorFlow
- Bayesian deep learning
- Deep Markov models
- Variational autoencoders
- Generative adversarial networks
Why beginners should read it: As an advanced topic, probabilistic deep learning isn‘t essential for beginners. However, those coming from statistics, engineering, or physics backgrounds will appreciate this perspective. The book provides a great foundation for working in areas like autonomous driving where quantifying uncertainty is critical.
Where to get it: Read online at Manning
Other Top eBooks
Here are some quick highlights of other highly recommended free deep learning eBooks for beginners:
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Deep Learning for Coders with fastai and PyTorch – A hands-on guide for building state-of-the-art deep learning models using the PyTorch framework. Focuses on a top-down, code-first approach to gain practical skills before diving deep into theory.
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Practical Deep Learning for Cloud, Mobile & Edge – Go beyond theory and learn how to deploy deep learning models to the cloud, mobile, web, and edge devices. Includes practical tips and best practices for optimizing and scaling production-ready deep learning systems.
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TensorFlow 2.0 Deep Learning Projects – For those who learn best by doing, this book provides a variety of hands-on deep learning projects using the TensorFlow framework. Great for practicing newly acquired skills while building your portfolio.
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Deep Learning Interviews – Prepare for deep learning job interviews with this collection of over 200 questions and answers. A useful supplement to help reinforce your understanding of key concepts and techniques.
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Mathematics for Machine Learning – Build a solid mathematical foundation for deep learning and machine learning. Covers linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and more.
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Deep Learning for Computer Vision – Learn to apply deep learning architectures like CNNs, GANs and autoencoders to solve computer vision tasks like image classification, object detection, face recognition, and image synthesis.
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Deep Learning with R – A practical guide to building deep learning models using the R programming language and Keras. Covers CNNs for computer vision, RNNs and transformers for natural language processing, GANs, and more.
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Deep Learning for NLP and Speech Recognition – Go in-depth with deep learning methods for natural language processing and speech recognition. Covers sequence-to-sequence models, attention mechanisms, transformers, and state-of-the-art language models.
Remember, reading these eBooks is just the beginning of your deep learning journey. To really solidify your skills, it‘s important to practice implementing models and experiment with your own projects.
Complement your theoretical knowledge by:
- Participating in Kaggle competitions
- Exploring open source deep learning projects on GitHub
- Taking online courses that include coding exercises
- Building a portfolio of deep learning projects to showcase your skills
- Joining online communities to learn from and collaborate with other practitioners
We hope this curated list of the best free deep learning eBooks helps you get started on an exciting learning path. Pick a book that matches your interests and dive in – amazing applications are waiting to be built!
