Deep learning is a powerful tool of artificial intelligence that’s changing many things. It is essential to have a good knowledge of Deep Learning, if you are aiming to make a career in AI. To make your life easy, we have made a list of some common Deep Learning ebooks, that you must read. This list has 12 free ebooks to help you learn about deep learning. They explain what it is, how it’s used, and exciting new things being done with it. Each book covers different parts of deep learning, like how it works and how it’s used in things like seeing pictures, understanding language, and more.

Key Factors

Based on a number of important criteria, these 12 free deep learning eBooks were narrowed down:

• Relevance and Coverage: From basic concepts to real-world applications in a range of fields, including computer vision and natural language processing, every book addresses a substantial portion of deep learning.
• Authoritativeness: The content in these publications is guaranteed to be accurate and credible because many of the authors are well-known and highly skilled in the field of deep learning, including Yoshua Bengio, Ian Goodfellow, and Michael Nielsen.
• Accessibility: Everyone who wants to learn more about deep learning can simply access the chosen eBooks because they are all freely available online.
• Uniqueness: Some publications include novel insights, such as concentrating on specialist methods like GANs and probabilistic modeling or applying particular programming languages, like R, for deep learning.
• Diversity of Topics: The list includes books that cover a broad spectrum of topics within deep learning, ensuring there’s something for beginners seeking an introduction to advanced practitioners looking for specialized insights.
• Practicality: Some books focus on practical implementations, providing hands-on examples and coding exercises, which is valuable for those looking to apply deep learning in real-world scenarios.

By taking these things into account, the list seeks to offer a comprehensive collection of free deep learning eBooks that meet a variety of interests and learning goals in the subject.

12 Best Free Deep Learning eBooks

Lets us dive into the description of each book.

1. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

• Description: This comprehensive book serves as a foundational guide to deep learning, covering a wide array of topics from basic principles to advanced techniques. It is widely regarded as an authoritative resource in the field.
• Who should read: Ideal for beginners seeking a thorough understanding of deep learning concepts and also valuable for experienced practitioners looking to deepen their knowledge.
• Availability: Free online version available at Deep Learning Book

2. “Deep Learning for Computer Vision” by Rajalingappaa Shanmugamani

• Description: This book focuses on deep learning techniques specifically for computer vision tasks such as image classification and object detection. It offers insights into advanced computer vision applications.
• Who should read: Recommended for those interested in applying deep learning to computer vision tasks, from students to researchers.
• Availability: Free PDF download at Packt Free eBook

3. “Introduction to Deep Learning” by MIT Press

• Description: An introductory book that covers the basics of deep learning with examples and exercises. It is designed as a beginner-friendly resource.
• Who should read: Beginners who want a structured introduction to deep learning concepts.
• Availability: Free PDF download at MIT Press

4. “Deep Learning with Python” by Francois Chollet

• Description: Written by the creator of Keras, this book focuses on practical deep learning using the Python programming language. It emphasizes hands-on coding examples.
• Who should read: Python developers interested in applying deep learning techniques using Keras.
• Availability: Free online version at Manning

5. “Deep Learning for Natural Language Processing” by Palash Goyal, Sumit Pandey

• Description: Explores the application of deep learning techniques to natural language processing tasks. It covers topics like sentiment analysis, language modeling, and more.
• Who should read: Suitable for those interested in understanding how deep learning is used in processing and understanding human language.
• Availability: Free online version

6. “Building Machine Learning Powered Applications” by Emmanuel Ameisen

• Description: While not solely focused on deep learning, this book teaches how to integrate deep learning models into practical applications effectively. It covers aspects of machine learning engineering.
• Who should read: Developers and data scientists interested in deploying machine learning, including deep learning models, in real-world applications.
• Availability: Free online version at O’Reilly

7. “Python Deep Learning” by Ivan Vasilev, Daniel Slater, Gianmario Spacagna

• Description: This book covers deep learning concepts using Python and popular libraries like TensorFlow. It includes practical examples and code snippets.
• Who should read: Python developers looking to delve into deep learning with TensorFlow.
• Availability: Free online version at O’Reilly

8. “Deep Learning with R” by François Chollet, J.J. Allaire

• Description: This book focuses on using the R programming language for deep learning tasks. It provides insights into using R with TensorFlow and Keras.
• Who should read: R users interested in applying deep learning techniques using R.
• Availability: Free online version at Manning

9. “Machine Learning Yearning” by Andrew Ng

• Description: While not strictly a deep learning book, it offers valuable insights into designing and deploying machine learning systems effectively. It covers practical aspects of machine learning engineering.
• Who should read: Those interested in understanding the process of building and deploying machine learning systems.
• Availability: Free online version at deeplearning.ai

10. “Deep Learning for Coders with fastai and PyTorch” by Sylvain Gugger, Jeremy Howard

• Description: Focuses on practical deep learning using the fastai library and PyTorch. It emphasizes a coding-centric approach with real-world examples.
• Who should read: Coders and developers interested in hands-on deep learning with PyTorch and fastai.
• Availability: Free online version at fast.ai

11. “Probabilistic Deep Learning with Python” by Oliver Dürr, Michael Lindner, Yves-Laurent Kom Samo

• Description: Explores the intersection of deep learning and probabilistic modeling, providing insights into uncertainty in deep learning. It covers topics like Bayesian neural networks.
• Who should read: Those interested in understanding uncertainty and probabilistic aspects of deep learning.
• Availability: Free online version at O’Reilly

12. “R Deep Learning Essentials” by Mark Hodnett

• Description: Focuses on deep learning using the R programming language, covering various deep learning architectures and techniques in R.
• Who should read: R users interested in deep learning, especially those looking to implement deep learning models in R.
• Availability: Free online version at Packt Free eBook

End Note

Knowledge is both potent and available in the field of deep learning. For novices and experts alike, the carefully chosen collection of 12 free eBooks offers a starting point and a comprehensive exploration. These resources are suitable for a wide range of learning objectives, be it learning the fundamentals, delving into specific topics like as generative adversarial networks (GANs), or investigating real-world coding applications. These eBooks serve as pillars of knowledge as the field develops, enabling both experts and enthusiasts to take advantage of deep learning’s potential for creativity and discovery.

You can also read our article on best deep learning books here.