TensorFlow 1.x Deep Learning Cookbook-PacktPublishing(2017).pdf下载

weixin_39821260 2020-06-17 11:30:23

In this book, you will learn how to efficiently use TensorFlow, Google's open
source framework for deep learning. You will implement different deep learning
networks such as Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative
Advers
相关下载链接：//download.csdn.net/download/windstand/10232407?utm_source=bbsseo

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TensorFlow-1x-Deep-Learning-Cookbook-code.zip TensorFlow 1.x Deep Learning Cookbook-pdf.zip pdf的代码部分乱码的，所以，把作者的代码也放在一起下载。CSDN现在必须要分下载真low，若因在他出下载的pdf中代码乱码，直接去https://github.com/PacktPublishing/TensorFlow-1x-Deep-Learning-Cookbook 下载代码，不需要下载本资源，省分~~

In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. You will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By the end of this book, you will be an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more.

In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. You will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By the end of this book, you will be an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more.

https://github.com/PacktPublishing/Practical-Machine-Learning-Cookbook.git Practical Machine Learning Cookbook by Atul Tripathi English | 14 Apr. 2017 | ASIN: B01JLBMC9K | 570 Pages | AZW3 | 38.79 MB Key Features Implement a wide range of algorithms and techniques for tackling complex data Improve predictions and recommendations to have better levels of accuracy Optimize performance of your machine-learning systems Book Description Machine learning has become the new black. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you'll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one. What You Will Learn Get equipped with a deeper understanding of how to apply machine-learning techniques Implement each of the advanced machine-learning techniques Solve real-life problems that are encountered in order to make your applications produce improved results Gain hands-on experience in problem solving for your machine-learning systems Understand the methods of collecting data, preparing data for usage, tra

红色石头的个人网站：www.redstonewill.com 今天给大家推荐一本适合新手入门机器学习和 TensorFlow 的最佳教程：《TensorFlow Machine Learning Cookbook》，中文译为《TensorFlow 机器学习方案手册》。书籍介绍 TensorFlow 是一个用于机器学习的开源软件库。本书将教你如何使用 TensorFlow 进行复杂的数据计...

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