The best deep learning books provide readers with the latest information on the topic. They break down the complex subject into easy-to-digest learning materials, making it more understandable. The best books to learn deep learning can also serve different education goals and readers. Keep reading to find out more about the best books for deep learning.
How Can I Choose the Best Books for Deep Learning Engineers?
You can choose the best book for deep learning engineers by considering your current level of knowledge. An experienced engineer can find books that are relevant and appropriate for their depth of insight. For beginners, books on machine learning can serve as a primer, providing background information before diving into deep learning concepts and practical approaches.
Tips for Choosing the Best Deep Learning Books
- Consider your learning goals. You should curate your deep learning journey based on your knowledge, skill level, and goals. If you’re starting from scratch, it’s best to pick up a book that provides a basic introduction and explores a broad range of topics.
- Budget. Set a budget that works for you. Check and compare prices from different bookstores to see where you can get a good deal. If you’re on a strict budget, you can try finding an online version through websites like Amazon.
- Consider the applicability. Find a book that matches your current expertise. If you’re a software engineer that wants to look into deep learning, choose a book that focuses on deep learning for a specific programming language.
- Research the book. Think about how deep learning can apply to your personal projects and endeavors. Books that provide practical examples and address real-world problems that you might encounter in your career are ideal.
- Ask people in your field for recommendations. Do your peers and colleagues have personal favorites? Ask around for recommendations that can help you understand deep learning fundamentals and other relevant concepts.
The 10 Best Deep Learning Books: An Overview
|Deep Learning||MIT Press||Generative adversarial networks (GANs), sequence modeling, numerical computation|
|Deep Learning for Coders with fastai and PyTorch||O’Reilly Media, Inc.||Deep learning models, deep network architectures, tabular modeling|
|Deep Learning with Python||Manning Publications||Generative deep learning, tensor operations, variational autoencoders (VAE)|
|Grokking Deep Learning||Manning Publications||Fundamental concepts, deep learning models, federated learning|
|Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow||O’Reilly Media, Inc.||Neural nets, convolutional neural networks, attention models|
|The Hundred-Page Machine Learning Book||Self-published||Deep learning techniques, machine learning basics, decision trees|
|Introduction to Machine Learning with Python: A Guide for Data Scientists||O’Reilly Media, Inc.||Linear models, kernelized support vector machines|
|Machine Intelligence: Demystifying Machine Learning, Neural Networks and Deep Learning||Notion Press||Machine and deep learning algorithms, neural networks, mathematical concepts|
|Machine Learning Yearning||Self-published||Machine learning projects, deep learning network, artificial intelligence systems|
|Reinforcement Learning: An Introduction||Bradford Book||Temporal-difference learning, function approximation, trajectory sampling|
The 10 Best Deep Learning Books: A Closer Look
This selection of the 10 best deep learning books covers a wide range of expertise and complexity. Whether you’re learning from scratch or already an expert, this list will help you find the perfect book for your needs. These books showcase different deep learning applications and provide an excellent balance between theory and real-world applications.
1. Deep Learning
- Author: Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Best for: Students with experience in machine learning who want in-depth coverage
The Deep Learning textbook is an essential read that covers theoretical concepts such as structured probabilistic models, representation learning, and deep generative models. It’s a great pick for university students looking to deepen their knowledge of this complex subject.
2. Deep Learning for Coders with fastai and PyTorch
- Author: Jeremy Howard and Sylvain Gugger
- Best for: PhD holders and big tech companies
This comprehensive book provides a deep dive into several topics, like training models, advanced techniques, and key concepts in deep learning theory. You’ll learn how to build natural language processing train models using fastai and PyTorch. It also delves into the ethics of deep learning algorithms.
3. Deep Learning with Python
- Author: Francois Chollet
- Best for: Beginner and intermediate software programmers
The book is built around a set of practical code examples that programmers can use in their projects. It also tackles high-level concepts in deep learning that are essential for anyone trying to understand the subject. It uses the example of building neural networks in a software library called Keras to expand conceptual understanding.
4. Grokking Deep Learning
- Author: Andrew W. Trask
- Best for: Beginners who want to begin building their own deep learning systems
Andrew Trask teaches readers how to build neural networks from scratch. The examples in this book use Python and its software library, NumPy. This book aims to build deep learning neural networks, identify images, and teach object detection and visualization.
5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Author: Aurélien Géron
- Best for: Deep learning practitioners who want guidelines to follow
The book offers a hands-on approach to the fundamentals of deep learning. The reader will learn how to implement tasks such as simple linear progression, sequence models, and reinforcement learning. It builds off the Python framework used in Scikit-Learn, Keras, and TensorFlow.
6. The Hundred-Page Machine Learning Book
- Author: Andriy Burkov
- Best for: Beginners who want to learn the fundamentals of deep learning
Andriy Burkov created an accessible introduction for beginners by discussing the building blocks of algorithms. This short primer focuses on vector machines, decision trees, and regression. It also helps readers with machine learning formulation through scientific notation and proper terminology.
7. Introduction to Machine Learning with Python: A Guide for Data Scientists
- Author: Andreas C. Müller and Sarah Guido
- Best for: Beginners who learn best through a hands-on guide
If your learning process benefits from real-world applications, you’ll enjoy this comprehensive introduction to the caveats of machine learning with Python. You’ll learn how to build machine-learning applications by using Scikit-Learn, a Python framework. This book also tackles the practical aspects of using machine learning algorithms.
8. Machine Intelligence: Demystifying Machine Learning, Neural Networks and Deep Learning
- Author: Suresh Samudrala
- Best for: Professionals in the technology and business industries
This book covers the basics of machine learning clearly and concisely. It’s a comprehensive guide to core concepts such as algorithms and neural networks. Samudrala provides real-world examples via illustrations and data tables and discusses key topics in the field.
9. Machine Learning Yearning
- Author: Andrew Ng
- Best for: Software engineers who have a background in machine learning
This cleverly-named book teaches readers to make artificial intelligence systems work properly and effectively. It provides guidelines for troubleshooting and diagnosing errors in systems. It also helps readers apply various types of learning methods, like end-to-end, transfer, and multi-task learning.
10. Reinforcement Learning: An Introduction
- Author: Richard S. Sutton and Andrew G. Barto
- Best for: Learners who want an in-depth study on reinforcement learning
Sutton and Barto make the nitty-gritty of reinforcement learning their primary focus in this book. This learning method is a computational approach that maximizes interaction with complex environments. You’ll get clear and concise explanations of key ideas about algorithms.
Alternative Ways to Learn Deep Learning
The best alternative way to learn deep learning is to attend a coding bootcamp. A coding bootcamp is a short-term educational program that provides students with practical skills to help them become job-ready individuals. They’re a cost-effective alternative for people who want to break into the tech industry.
Deep learning online courses are another option for learning about this complex subject. These courses can help jumpstart your educational journey, teaching you the core principles you’ll need to eventually become a deep learning professional. These interactive learning experiences are also great opportunities to develop deep learning projects for your portfolio.
Is Learning Deep Learning Worth It?
Yes, learning deep learning is worth it. Jobs in this field have positive projections for opportunities and job growth. According to the Bureau of Labor Statistics (BLS), the job outlook for computer and information research scientists is 22 percent, a much faster rate of growth than the national average. Over the next decade, BLS is projecting an average of 3,200 new jobs each year.
According to a 2022 survey from Statista, the deep learning software market generates about $350 million in revenue. This sector is only going to grow as technology becomes more integrated into other industries. It’s projected that market revenue will reach $930 million by 2025. While the topic is complicated, pursuing deep learning can lead to a fruitful career.
Best Deep Learning Books FAQ
Deep learning is a complex field of machine learning. Both deep learning and machine learning fall under the umbrella of artificial intelligence (AI). Deep learning allows machines and computers to process information via concepts and experience. This in turn allows computers to learn intricate layers of content without needing a human operator.
Yes, a coding bootcamp can help you land a deep learning job. Coding bootcamps like Simplilearn and The Tech Academy can provide the in-depth lessons and practical knowledge needed to become job-ready for your desired career in AI.
Yes, deep learning jobs pay well. According to ZipRecruiter, deep learning engineers have an average salary of $176,549. The top 10 percent of these professionals can potentially earn $241,000, while the bottom five percent have an average compensation of $87,000.
Yes, deep learning jobs involve high levels of logical thinking and profound analytical skills. Understanding machine learning takes time because it involves lifelike intelligent systems.
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