The 4 best Deep Learning Books

Deep Learning (Adaptive Computation and Machine Learning series)

  • It touches almost all topics related to Deep Learning
  • Equally helpful from students to instructors
  • Explains basic to advanced concepts of Machine Learning

Deep learning is a type of machine learning that makes computers capable to understand the world. Computers learn in terms of a hierarchy of concepts. There is no need to specify the knowledge for the computer by the human. It is because the computer gathers knowledge from experience.

The hierarchy of concepts facilitates the computer to understand different complicated concepts by breaking them out in simpler ones. A graph of these hierarchies can be many layers deep. This book is a great addition in the world of deep learning to learn a broad range of concepts.

According to Elon Musk, “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” This book is a comprehensive guide that covers relevant concepts in linear algebra, numeric computation, and machine learning. Moreover, it elaborates on the conceptual background of deep learning, information theory, and probability theory.

In addition to this, this book also deals with various deep learning techniques utilized by practitioners in the industry. The techniques include optimization algorithms, regularization, sequence modeling, and feedforward networks.

Deep Learning also covers research perspectives including theoretical topics, auto-encoders, and structured probabilistic. Besides these, this incredible resource explains linear factor models, representation learning, and Monte Carlo methods. This list further includes approximate inference, the partition function, and deep generative models.

Deep Learning is exceptionally useful for undergraduate and graduate students who want to use deep learning in their projects. This book is also handy for researchers, software engineers, and instructors.

The Hundred-Page Machine Learning Book

  • Cover all core concepts of Machine Learning just in 100 pages
  • Equally handy for newcomers and old-timers
  • Save your time by clearing your concepts in a few words

This is another masterpiece written on machine learning. The most interesting thing about this book is to deliver all concepts just in a hundred pages. It was a very useful but very hard task, to sum up, all of the machine learning to 100 pages.

Machine learning is a very broader topic. One might think that the author would have skipped a lot to reduce this book. It is true that short book skips math equations but this book contains math equations. It is amazing how the author explains the core concepts of machine learning just in a few words. You might be thinking that the book will be for advanced learners. Not at all. The Hundred-Page Machine Learning Book is very useful for newcomers in this field.

According to the Head of Data Science at Amazon, Karolis Urbonas: “A great introduction to machine learning from a world-class practitioner”.  Head of R&D at Lucidworks, Chao Han, says: “I wish such a book existed when I was a statistics graduate student trying to learn about machine learning.”

VP of Artificial Intelligence at LinkedIn, Deepak Agarwal says: “A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.” Moreover, it also includes convolutional networks and practical methodology.

Deep Learning with Python

  • Fundamentals of machine learning
  • Deep learning for text, sequences, and computer vision
  • Generative deep learning concepts

Machine learning is very popular and gets advanced in recent years. It is touching to human accuracy today. It was a time when machines couldn’t beat even a serious GO player. But now, it has defeated a world champion. Deep learning is backing this progress.

Deep Learning with Python uses Python language and Keras library to introduce the field of deep learning. This book clears the concepts of deep learning by intuitive explanation with practical examples. The book is very useful for both beginners and advanced learners. All you need to start reading this book is intermediate understanding with Python. There is no need to have previous experience in machine learning or Keras library.

Deep Learning with Python explains challenging concepts with practices. You will explore all core concepts of deep learning such as generative models, computer vision and natural language processing. You will learn different deep learning principles, image-classification models, and neural networks. Moreover, it also includes convolutional networks and practical methodology.

In addition to this, you will learn deep learning for text and sequences, image generation, and neural style transfer. This book starts with the intro of deep learning. It passes through the mathematical building blocks of neural networks. Furthermore, it covers advanced deep learning concepts with generative deep learning.

Deep Learning (MIT Press Essential Knowledge series)

  • Explains the history, current state, and future of deep learning
  • Cover basic to advanced algorithms
  • Cover all fundamental concepts of deep learning

Deep learning is an artificial intelligence technology that enhances the functionality of computers. This technology enables speech recognition in mobile phones, AI games, machine translation, and even driverless cars, etc. When we use different products of Google, Microsoft, Apple, Baidu, and Facebook, we are most often interacting with deep-learning systems. It surveys such applications as speech recognition, online recommendation, videogames, natural language process, and bioinformatics.

This Deep Learning -MIT Press Essential Knowledge series- offers a concise and comprehensive intro to this technology. This technology is the heart of the artificial intelligence revolution. This book contains almost 300 pages and covers all core concepts related to deep learning. The book explains some of the basic concepts, its history, and the current state of the technology. You will learn two fundamental algorithms in this book that are: backpropagation and gradient descent.

This book covers different essential deep learning architectures including recurrent neural networks. This list further includes auto-encoder, generative adversarial networks, and capsule networks as well. This book explains the future of deep learning, possible development, major trends, and significant challenges. The book is very useful for students, engineers, beginners, and advanced learners.

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