Deep Learning with Keras

Implementing deep learning models and neural networks with the power of Python

Antonio Gulli , Sujit Pal 지음

Packt(GCO Science) 2017년 04월 26일 출간

작품소개

책소개

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.

목차

▶TABLE of CONTENTS
1: NEURAL NETWORKS FOUNDATIONS
2: KERAS INSTALLATION AND API
3: DEEP LEARNING WITH CONVNETS
4: GENERATIVE ADVERSARIAL NETWORKS AND WAVENET
5: WORD EMBEDDINGS
6: RECURRENT NEURAL NETWORK ― RNN
7: ADDITIONAL DEEP LEARNING MODELS
8: AI GAME PLAYING
9: CONCLUSION

책 속으로

▶What this book covers
？ Chapter 1, Neural Networks Foundations, teaches the basics of neural networks.
？ Chapter 2, Keras Installation and API, shows how to install Keras on AWS, Microsoft Azure, Google Cloud, and your own machine. In addition to that, we provide an overview of the Keras APIs.
？ Chapter 3, Deep Learning with ConvNets, introduces the concept of convolutional networks. It is a fundamental innovation in deep learning that has been used with success in multiple domains, from text to video to speech, going well beyond the initial image processing domain where it was originally conceived.
？ Chapter 4, Generative Adversarial Networks and WaveNet, introduces generative adversarial networks used to reproduce synthetic data that looks like data generated by humans. And we will present WaveNet, a deep neural network used for reproducing human voice and musical instruments with high quality.
？ Chapter 5, Word Embeddings, discusses word embeddings, a set of deep learning methodologies for detecting relationships between words and grouping together similar words.
？ Chapter 6, Recurrent Neural Networks ？. RNN, covers recurrent neural networks, a class of network optimized for handling sequence data such as text.
？ Chapter 7, Additional Deep Learning Models, gives a brief look into the Keras functional API, regression networks, autoencoders, and so on.
？ Chapter 8, AI Game Playing, teaches you deep reinforcement learning and how it can be used to build deep learning networks with Keras that learn how to play arcade games based on reward feedback.
？ Chapter 9, Conclusion, is a crisp refresher of the topics covered in this book and walks the users through what is new in Keras 2.0.

작가정보

저자(글) Antonio Gulli

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 has managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields ranging from publishing (Elsevier) to consumer internet (Ask.com and Tiscali) and high-tech R&D (Microsoft and Google).

저자(글) Sujit Pal

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.