finelybook
Deep Learning with Keras
by Antonio Gulli and Sujit Pal
pages 页数: 318 pages
Publisher Finelybook 出版社: Packt Publishing (28 April 2017)
Language 语言: English
ISBN-10 书号: 1787128423
ISBN-13 书号: 9781787128422
B06Y2YMRDW
Key Features
Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games
See how various deep-learning models and practical use-cases can be implemented using Keras
A practical,hands-on guide with real-world examples to give you a strong foundation in Keras
Book Description
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. Next you will be introduced to Recurrent Networks,which are optimized for processing sequence data such as text,audio or time series. Following that,you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer.
Finally,you will look at Reinforcement Learning and its application to AI game playing,another popular direction of research and application of neural networks.
What you will learn
Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm
Fine-tune a neural network to improve the quality of results
Use deep learning for image and audio processing
Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases
Identify problems for which Recurrent Neural Network (RNN) solutions are suitable
Explore the process required to implement Autoencoders
Evolve a deep neural network using reinforcement learning
About the Author
Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent,innovation,and execution. He is an expert in search engines,online services,machine learning,information retrieval,analytics,and cloud computing. So far,he has been lucky enough to gain professional experience in four different countries in Europe and managed people in six different countries in Europe and America. Antonio served as CEO,GM,CTO,VP,director,and site lead in multiple fields spanning from publishing (Elsevier) to consumer internet (Ask.com and Tiscali) and high-tech R&D (Microsoft and Google).
Sujit Pal is a technology research director at Elsevier Labs,working on building intelligent systems around research content and metadata. His primary interests are information retrieval,ontologies,natural language processing,machine learning,and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this,he worked in the consumer healthcare industry,where he helped build ontology-backed semantic search,contextual advertising,and EMR data processing platforms. He writes about technology on his blog at Salmon Run.
Contents
Chapter 1. Neural Networks Foundations
Chapter 2. Keras Installation And Api
Chapter 3. Deep Learning With Convnets
Chapter 4. Generative Adversarial Networks And Wavenet
Chapter 5. Word Embeddings
Chapter 6. Recurrent Neural Network — Rnn
Chapter 7. Additional Deep Learning Models
Chapter 8. Ai Game Playing
Chapter 9. Conclusion
主要特征
在Keras中实现各种深度学习算法，并了解游戏中可以使用的深度学习
了解如何使用Keras实现各种深入学习模式和实际用例
一个实用的实践指南，具有现实世界的例子，为您在Keras提供坚实的基础
图书说明
本书首先介绍监督学习算法，如简单线性回归，经典多层感知器和更复杂的深卷积网络。您还将探索图像处理，识别手写数字图像，将图像分类为不同类别，以及使用相关图像注释进行高级对象识别。还提供了用于识别面部检测的突出点的示例。接下来，您将被介绍给经常性网络，经过优化处理序列数据，如文本，音频或时间序列。之后，您将了解无人值守学习算法，如自动编码器和非常受欢迎的生成对抗网络（GAN）。您还将探索神经网络的非传统用途作为风格转移。
最后，您将看看加固学习及其应用于AI游戏，另一个流行的神经网络研究和应用方向。
你会学到什么
使用反向传播算法在大型神经网络上优化分步功能
微调神经网络以提高结果的质量
使用深度学习进行图像和音频处理
在特殊情况下，使用递归神经张量网络（RNTN）优于标准字嵌入
确定经常性神经网络（RNN）解决方案适用的问题
探索实现自动编码器所需的过程
使用强化学习进行深层神经网络
关于作者
安东尼奥·古利是一位软件行政和商业领袖，对建立和管理全球技术人才，创新和执行的热情。他是搜索引擎，在线服务，机器学习，信息检索，分析和云计算的专家。到目前为止，他已经很幸运地获得了欧洲四个国家的专业经验，并在欧美六个国家管理了人。 Antonio担任首席执行官，总经理，首席技术官，副总裁，主管和多个领域的领先厂商，从出版（Elsevier）到消费者互联网（Ask.com和Tiscali）和高科技研发（微软和Google）。
Sujit Pal是Elsevier实验室的技术研究总监，致力于围绕研究内容和元数据构建智能系统。他的主要兴趣是信息检索，本体论，自然语言处理，机器学习和分布式处理。他目前正在使用深度学习模型来研究图像分类和相似性。在此之前，他曾在消费者保健行业工作，他帮助构建本体支持的语义搜索，语境广告和EMR数据处理平台。他在他的博客“鲑鱼跑”上写道技术。
目录
神经网络基础
第2章Keras安装和Api
第3章深度学习与Convnets
第四章生成对抗网络和波数
第5章Word嵌入
第六章经常性神经网络 - Rnn
第七章其他深度学习模型
第八章爱游戏
第九章结论
