Keras

Independently published, 2021. — 68 p. — ASIN : B0947GKFSZ. This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression, and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition, and much...

2nd Edition. — Packt Publishing, 2020. — 444 p. — ISBN: 978-1-83921-757-9. Cut through the noise and get real results with a step-by-step approach to understanding deep learning with Keras programming You already know that you want to learn Keras, and a smarter way to learn is to learn by doing. The Deep Learning with Keras Workshop focuses on building up your practical skills...

Apress, 2019. — 182 p. — ISBN13: (electronic): 978-1-4842-4240-7. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The...

Packt Publishing, 2018. — 368 p. — ASIN B078N8RDCP. A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders, GANs, VAEs,...

Amazon Digital Services LLC, 2018. — 189 р. This introduction will help you develop a good understanding of deep learning completely from scratch This book covers: Introduction to machine learning and deep learning Math for deep learning explained to the layman How neural networks work: a general overview Activation functions in deep networks Loss functions Weight...

Packt Publishing, 2017. — 318 p. — ISBN: 978-1-78712-842-2. Get to grips with the basics of Keras to implement fast and efficient deep-learning models 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...