深度學(xué)習(xí) Deep Learning with Python 2017 經(jīng)典的深度學(xué)習(xí)教程。強(qiáng)烈推薦下載學(xué)習(xí)。最新的深度學(xué)習(xí)教程。

這本書向您介紹了深度學(xué)習(xí)的橋梁框架如keras實(shí)踐的學(xué)術(shù)現(xiàn)狀和工業(yè)國(guó)家之間的差距。

本書簡(jiǎn)要介紹了深層學(xué)習(xí)的數(shù)學(xué)前提和基本原理，使本書成為希望深入學(xué)習(xí)的軟件開發(fā)人員的良好起點(diǎn)。還包括了一個(gè)簡(jiǎn)短的深入學(xué)習(xí)架構(gòu)的調(diào)查。

使用Python進(jìn)行深入學(xué)習(xí)還將介紹自動(dòng)微分和GPU計(jì)算的關(guān)鍵概念，而在深入進(jìn)行大規(guī)模實(shí)驗(yàn)時(shí)，這并不是深入學(xué)習(xí)的核心概念。

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 to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.
This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.
Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.