Keras深度学习实战:从基础到高级应用

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《深度学习与Keras:安东尼奥·古利的实践指南》是一本专注于介绍如何使用Keras、Theano和TensorFlow等深度学习框架实现神经网络的书籍。作者安东尼奥·古利和苏吉特·帕尔通过深入浅出的方式,带领读者从基础的监督学习入手,例如讲解简单线性回归、经典多层感知器以及深度卷积网络,这些技术在图像识别任务中发挥关键作用,如识别手写数字和对手写数字图像进行分类,同时涉及高级的对象识别,比如使用图像注释来定位显著的面部特征。 书中特别关注了循环神经网络(Recurrent Networks),这是专为处理序列数据,如文本、音频和时间序列设计的,这对于自然语言处理、语音识别等领域至关重要。接着,作者将读者带入无监督学习的世界,介绍了自动编码器(Autoencoders)和生成对抗网络(GANs),这两种模型在数据压缩、生成新样本以及图像修复等方面表现出色。 此外,书中的内容还包括了非传统神经网络的应用,如风格转换,这展示了深度学习在艺术和视觉设计领域的创新潜力。值得注意的是,书中强调版权保护,所有内容未经版权所有者Packt Publishing事先书面许可,不得复制、存储或传播。 该书不仅提供理论知识,还通过实例和实战项目帮助读者掌握实际操作技巧,适合那些希望进一步理解和应用深度学习的读者,无论他们是初学者还是经验丰富的专业人士。通过阅读这本书,读者能够系统地学习和理解深度学习的基本原理,并在实践中提升技能。
2017-05-04 上传
图书可以从下面的链接下载 http://download.csdn.net/detail/u013003382/9832573 Deep Learning with Keras by Antonio Gulli English | 26 Apr. 2017 | ASIN: B06Y2YMRDW | 318 Pages | AZW3 | 10.56 MB 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. Table of Contents Neural Networks Foundations Keras Installation and API Deep Learning with ConvNets Generative Adversarial Networks and WaveNet Word Embeddings Recurrent Neural Network — RNN Additional Deep Learning Models AI Game Playing Conclusion