附件裡邊包含三本書，都是 .pdf 文檔。

Deep Learning with Python A Hands-on Introduction
Authors: Ketkar, Nihkil

[size=13.125px]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.
What You Will Learn

• Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe
• Gain the fundamentals of deep learning with mathematical prerequisites
• Discover the practical considerations of large scale experiments
• Take deep learning models to production
  

[size=13.125px]Who This Book Is For
[size=13.125px]Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

Introduction to Deep Learning Using R A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R Authors: Beysolow II, Taweh

[size=13.125px]Understand deep learning, the nuances of its different models, and where these models can be applied.

[size=13.125px]The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R[size=13.125px] provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.

[size=13.125px]What You'll Learn

• Understand the intuition and mathematics that power deep learning models
• Utilize various algorithms using the R programming language and its packages
• Use best practices for experimental design and variable selection
• Practice the methodology to approach and effectively solve problems as a data scientist
• Evaluate the effectiveness of algorithmic solutions and enhance their predictive power
  

[size=13.125px]Who This Book Is For
[size=13.125px]Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.

MATLAB Deep Learning With Machine Learning, Neural Networks and Artificial Intelligence
Authors: Kim, SeongPil

[size=13.125px]Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book.  
With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.
What You'll Learn

• Use MATLAB for deep learning
• Discover neural networks and multi-layer neural networks
• Work with convolution and pooling layers
• Build a MNIST example with these layers
  

[size=13.125px]Who This Book Is For

[size=13.125px]Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.