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Keras深度学习入门与实践 by Posts & Telecom Press,Antonio Gulli,Sujit Pal Pdf
Keras快速上手;基于Keras的Python实践;Keras深度学习实践应用;提升AI编程能力 Key Features 全书展示了基于Keras框架、以Python编码的20多种有效的神经网络。 Book Description作为一款轻量级、模块化的开源深度学习框架,Keras以容易上手、利于快速原型实现、能够与TensorFlow和Theano等后端计算平台很好兼容等优点,深受众多开发人员和研究人员的喜爱。 本书结合大量实例,简明扼要地介绍了目前热门的神经网络技术和深度学习技术。从经典的多层感知机到用于图像处理的深度卷积网络,从处理序列化数据的循环网络到伪造仿真数据的生成对抗网络,从词嵌入到AI游戏应用中的强化学习,本书引领读者一层一层揭开深度学习的面纱,并在逐渐清晰的理论框架下,提供多个Python编码实例,方便读者动手实践。 通过阅读本书,读者不仅能学会使用Keras快捷构建各个类型的深度网络,还可以按需自定义网络层和后端功能,从而提升自己的AI编程能力,在成为深度学习专家的路上更进一步。What you will learn 在大型神经网络上使用反向传播算法逐步优化函数 微调神经网络以改进结果质量 使用深度学习进行图像和音频处理 在特定的案例中使用递归神经张量网络(RNTN)以取得比标准词嵌入更好的效果 识别循环神经网络(RNN)适于解决的问题 探索自动编码机的实现过程 使用强化学习增强深层神经网络 Who this book is for 深度学习爱好者,深度学习方向的研究人员和工程技术人员
Advanced Deep Learning with Keras by Rowel Atienza Pdf
Understanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existence Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learning A wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANs Book DescriptionRecent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.What you will learn Cutting-edge techniques in human-like AI performance Implement advanced deep learning models using Keras The building blocks for advanced techniques - MLPs, CNNs, and RNNs Deep neural networks – ResNet and DenseNet Autoencoders and Variational Autoencoders (VAEs) Generative Adversarial Networks (GANs) and creative AI techniques Disentangled Representation GANs, and Cross-Domain GANs Deep reinforcement learning methods and implementation Produce industry-standard applications using OpenAI Gym Deep Q-Learning and Policy Gradient Methods Who this book is for Some fluency with Python is assumed. As an advanced book, you'll be familiar with some machine learning approaches, and some practical experience with DL will be helpful. Knowledge of Keras or TensorFlow 1.x is not required but would be helpful.
Advanced Deep Learning with TensorFlow 2 and Keras by Rowel Atienza Pdf
Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learnUse mutual information maximization techniques to perform unsupervised learningUse segmentation to identify the pixel-wise class of each object in an imageIdentify both the bounding box and class of objects in an image using object detectionLearn the building blocks for advanced techniques - MLPss, CNN, and RNNsUnderstand deep neural networks - including ResNet and DenseNetUnderstand and build autoregressive models – autoencoders, VAEs, and GANsDiscover and implement deep reinforcement learning methodsWho this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.
Deep Learning Projects Using TensorFlow 2 by Vinita Silaparasetty Pdf
Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts. The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application. What You'll Learn Grasp the basic process of neural networks through projects, such as creating music Restore and colorize black and white images with deep learning processes Who This Book Is For Beginners new to TensorFlow and Python.
金融中的人工智能 by Posts & Telecom Press,Jeffrey Ng,Subhash Shah Pdf
一本书轻松读懂金融科技的核心内涵 Key Features 一本书轻松读懂金融科技的核心内涵; 众多业界人士推荐,内容通俗易懂; 立足AI视角,解读金融业务新形态; 书中囊括丰富的算法讲解和代码示例; 更有一系列高效的金融科技解决方案。 Book Description近年来,人工智能在各个领域被广泛应用,但对于很多金融从业人员来说,人工智能仍然给人一种高深莫测的感觉。本书旨在从新技术(如人工智能)的视角给出金融业务的新兴解决方案。 本书内容通俗易懂,不仅揭示了人工智能在金融业中的重要性,还结合机器学习算法和示例给出了一系列的金融科技解决方案,涉及时间序列分析、强化学习、预测分析、自动化投资组合管理、情绪分析、自然语言处理等知识点。此外,本书还结合现实工作总结了相关的注意事项。 本书适合传统金融行业的从业者以及新兴金融科技领域的实践者阅读。读者可从本书深入浅出的知识点和案例中了解到人工智能的魅力,为更好地运用人工智能技术赋能金融业务做好准备。What you will learn 轻松读懂金融科技的核心内涵; 人工智能在金融业中的重要性 一系列的金融科技解决方案 Who this book is for 本书适合传统金融行业的从业者以及新兴金融科技领域的实践者阅读。读者可从本书深入浅出的知识点和案例中了解到人工智能的魅力,为更好地运用人工智能技术赋能金融业务做好准备。
