The Deep Learning with Keras Workshop: Learn how to define and train neural network models with just a few lines of code

by Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat

Discover how to leverage Keras, the powerful and easy-to-use open source Python library for developing and evaluating deep learning models New experiences can be intimidating, but not this one! This beginner's guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks. What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework. The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you'll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you'll explore recurrent neural networks and learn how to train them to predict values in sequential data. By the end of this book, you'll have developed the skills you need to confidently train your own neural network models. If you know the basics of data science and machine learning and want to get started with advanced machine learning technologies like artificial neural networks and deep learning, then this is the book for you. To grasp the concepts explained in this deep learning book more effectively, prior experience in Python programming and some familiarity with statistics and logistic regression are a must.

573 pages, Kindle Edition

Published July 29, 2020

Reviews

[Marcos Malumbres · Rating 5 out of 5]

Very good introduction to the scikit-learn, tensorflow and keras frameworl, including basics of machine learning with sklearn, basic neural networks, techniques for model selection and improving efficiency. The book includes special chapters for Convolutional Neural Networks and image analysis and a brief introduction to Recurrent Neural Networks.

Overall, it is an excellent introduction to all these topics, with a basic explanation of the concepts and implementation without getting deep into the mathematics. The exercises are quite good, a bit repetitive in some cases but all ok! I found (pages refer to the 2nd edition) one mistake in adding a parameter to a function (page 187), one .model.fit_generator() deprecated command (page 239), an internal hidden layer with input_shape (page 245), a typo (datasetset) in page 272, and an unexpected problem with the dimensions of one array (page 296). Whereas I easily solved the first ones, I was unable to fix the last problem. In any case, very few errors for a 430-page book full of codes.

Highly recommended for intro into Keras!!!