Deep learning is an artificial intelligence (AI) technique that simulates how humans can gain knowledge. In deep learning, the word “deep” represents the concept of multiple layers in a single/multiple network. A linear perceptron cannot be a universal classifier, but it can be a network with a non-polynomial activation function that includes one hidden layer of indefinite width. These are the top 10 deep learning books that will help you learn about deep learning (DL).
By François Chollet
This book, written by François Chollet, is a founder of Keras and a Google AI researcher. Deep Learning with Python is a book that introduces the area of deep learning using Python and the Keras framework. It increases your knowledge and awareness through practical examples with explanations. The reader will explore challenging concepts and practice with applications in computer vision, natural language processing (NLP), and generative models. You’ll have the knowledge and hands-on abilities to utilize deep learning in your projects after understanding this book.
It includes deep learning principles, image classification models, text and image generation, and deep learning for sequences.
By Ian Goodfellow, Yoshua Bengio, Aaron Courville
This book conveys that DL is a process that allows computers to learn from their mistakes and decipher the world as a hierarchy of concepts. There is no need for an operator to expressly specify all of the knowledge that the computer requires because the computer learns through experience. This book covers a wide spectrum of deep learning topics. A graph of these networks would be many layers deep, allowing the machine to understand difficult and complex concepts by building them out of simpler ones.
Undergraduate and graduate students pursuing jobs in industry or research, as well as software developers interested in incorporating deep learning into their products or platforms, can benefit from deep learning.
By Charu C. Aggarwal
This book covers both traditional and current deep learning models. The theory and methods of deep learning are the key focus. The theory and algorithms of neural networks are particularly crucial for grasping key notions so that one may comprehend key design concepts of neural architectures in a variety of applications.
Special examples of neural networks include support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems.
These methods are compared to more contemporary feature engineering techniques such as word2vec. Deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are among the advanced subjects covered. Graduate students, researchers, and practitioners will benefit from this work.
There are a variety of exercises accessible, as well as a solution manual, to aid with classroom teaching.
4. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms 1st Edition, Kindle Edition
By Nikhil Buduma, Nicholas Locascio
This book summarizes the general perspective of deep learning and neural network concepts. Deep learning has become a very active area of research with the revitalization of neural networks in the 2000s, paving the way for modern machine learning.
The author gives examples and straightforward explanations to guide you through the major principles of this hard topic in this practical book. Tech giants like Google, Microsoft, and Facebook are all aggressively building in-house deep-learning teams. Deep learning, on the other hand, remains a complex and challenging concept for the rest of us to understand.
This book will get you started if you are comfortable with Python and have a strong grip on mathematics, as well as a fundamental grasp of machine learning.
By Antonio Gulli, Amita Kapoor, Sujit Pal
This book teaches neural networks and deep learning algorithms with the help of TensorFlow (TF) and Keras. The reader will learn how to create deep learning applications using the most powerful, popular, and scalable machine learning stack on the market.
TensorFlow is the toolkit of choice for professional applications, and Keras provides a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 has complete Keras integration, making complex but deep machine learning easier and more convenient than ever before.
This book teaches you the theory that is necessary to construct and practice machine learning systems with Keras, TensorFlow 2, and AutoML.
By Andrew Trask
Grokking Deep Learning teaches you how to create deep learning neural networks from the ground up. The author teaches you the science behind this machine language, so you can understand every element of training neural networks. You’ll train your neural networks to perceive and comprehend pictures, translate text into multiple languages, and be completely equipped to go on to master deep learning frameworks using just Python and its math-supporting library, NumPy.
It features the science behind deep learning, concepts regarding federated learning, and tips and tricks regarding deep learning language. It is a very good book who have intermediate skills in programming and mathematics.
By John D. Kelleher
In this book of the MIT Press Essential Knowledge series, computer scientist John Kelleher provides an easy, brief, but thorough introduction to the basic technology at the core of the AI revolution. Deep learning is a type of artificial intelligence that allows computer vision, voice recognition in mobile phones, machine translation, AI games, autonomous vehicles, and other uses. Google, Microsoft, Facebook, Apple, and Baidu tech products are the perfect examples of deep learning systems.
The author gives a thorough (and understandable) overview of the two main deep learning algorithms: gradient descent and optimization algorithms. Finally, the author focuses on the future of deep learning, examining important trends, potential advancements, and significant difficulties.
By Jon Krohn
Three world-class educators and practitioners provide a visually rich, intuitive, and approachable high-level introduction to deep learning principles and applications in Deep Learning Illustrated. It minimizes much of the complexity of creating deep learning models, making the topic more enjoyable to study and accessible to a far larger audience. It is packed with bright, full-color graphics.
The authors highlight several of today’s most widely used and innovative deep learning libraries, including TensorFlow and its high-level API, Keras; PyTorch. This book also focuses on the recently released high-level TensorFlow API called Coach. It provides the platform for the reader to build Deep Reinforcement Learning algorithms, to help readers accomplish more in less time.
By Jeremy Howard, Sylvain Gugger
This book is written by the inventors of FASTAI, Jeremy Howard and Sylvain Gugger, who teach you how to train a model on a variety of tasks using Fastai and PyTorch. You’ll also explore further and deeper into deep learning theory to achieve a complete knowledge of algorithms at work.
Fastai is the first library to give a uniform interface to the most popular deep-learning applications. Deep learning is frequently seen as the sole realm of math PhDs and large tech corporations. However, as this hands-on approach illustrates, Python programmers with limited math experience, tiny quantities of data, and minimum code may produce amazing results in deep learning.
By Aurélien Géron
With numerous recent breakthroughs, this book essentially informs the reader how deep learning has transformed the whole field of machine learning. Even programmers with less understanding of this technology may now use simple, fast tools to develop data-driven algorithms.
The author uses basic examples, little theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow—to assist the reader in gaining an intuitive understanding of the principles and tools for building intelligent systems.
The curious reader may learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need to get started is a little programming knowledge.
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