In this post, we have prepared a curated top list of reading recommendations for beginners and experienced. This hand-picked list of the best Keras books and tutorials can help fill your brain this January and ensure you’re getting smarter. We have also mentioned the brief introduction of each book based on the relevant Amazon or Reddit descriptions.
- 1. Deep Learning with Python (2017)
- 2. Deep Learning with Keras (2017)
- 3. Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python (2017)
- 4. Deep Learning with Applications Using Python (2018)
- 5. Machine Learning with Python Cookbook: Practical Solutions from Preprocessing (2018)
- 6. Deep Learning Cookbook: Practical recipes to get started quickly (2018)
- 7. Deep Learning with Python: A Hands-on Introduction (2017)
- 8. Mastering TensorFlow 1.x: Advanced machine learning(2018)
- 9. Practical Convolutional Neural Network Models: Enhance deep learning skills by building intelligent ConvNet models using Keras (2018)
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1. Deep Learning with Python (2017)
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn’t beat a serious Go player, to defeating a world champion.
Author(s): Francois Chollet
2. Deep Learning with Keras (2017)
This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided.
Author(s): Antonio Gulli, Sujit Pal
3. Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python (2017)
Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process.Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get…
Author(s): Abhishek Nandy, Manisha Biswas
Build deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning.
Author(s): Navin Kumar Manaswi
The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline—everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon…
Author(s): Chris Albon
Recent developments in deep learning have put the field center stage for innovation in software engineering. New algorithms and techniques in academia hold promise for many real world problems, and new machine learning platforms are powerful, but aren’t necessarily easy to get started with.With this hands-on cookbook, you’ll discover that deep learning doesn’t need to be intimidating. Aimed at readers who are new to deep learning, this cookbook enables you to solve problems quickly, using the most appropriate platform…
Author(s): Douwe Osinga
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you …
Author(s): Nikhil Ketkar
Build, scale, and deploy deep neural network models using the star libraries in Python.TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs.This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim…
Author(s): Armando Fandango
9. Practical Convolutional Neural Network Models: Enhance deep learning skills by building intelligent ConvNet models using Keras (2018)
One stop guide to practice ConvNets models from most common to recent advances in artificial intelligence field.Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, speech recognition and more. These advances create unprecedented opportunities and challenges to build and deploy large-scale ConvNet applications. This book aims to take you through the building blocks of CNN’s…
Author(s): Pradeep Pujari
Best Keras Books that Should be on Your Bookshelf
We highly recommend you to buy all paper or e-books in a legal way, for example, on Amazon. But sometimes it might be a need to dig deeper beyond the shiny book cover. Before making a purchase, you can visit resources like Genesis and download some Keras books mentioned below at your own risk. Once again, we do not host any illegal or copyrighted files, but simply give our visitors a choice and hope they will make a wise decision.
Next-Generation Machine Learning with Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More
Author(s): Butch Quinto
ID: 2525213, Publisher: Apress, Year: 2020, Size: 5 Mb, Format: epub
Python Machine Learning: Machine Learning And Deep Learning From Scratch Illustrated With Python, Scikit-Learn, Keras, Theano And Tensorflow
Author(s): Moubachir Madani Fadoul
ID: 2532837, Publisher: , Year: 2020, Size: 2 Mb, Format: mobi
Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
Author(s): Rowel Atienza
ID: 2533898, Publisher: Packt Publishing, Year: 2020, Size: 30 Mb, Format: epub
Please note that this booklist is not absolute. Some books are really hot items according to The Wall Street Journal, others are composed by unknown authors. On top of that, you can always find additional tutorials and courses on Coursera, Udemy or edX, for example. Are there any other relevant resources you could recommend? Drop a comment if you have any feedback on the list.