TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
复制标题
用于深度学习的 TensorFlow：从线性回归到强化学习
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
R. Zadeh
中科院分区：
文献类型：
--
作者：
Bharath Ramsundar;R. Zadeh
了解如何使用TensorFlow解决具有挑战性的机器学习问题，TensorFlow是Google用于深度学习的革命性新软件库。如果你有一些基本的线性代数和微积分的背景，这本实用的书通过向你展示如何设计能够检测图像中的对象，理解文本，分析视频和预测潜在药物的特性的系统来介绍机器学习的基础知识。TensorFlow for Deep Learning通过实际示例教授概念，并帮助您从头开始构建深度学习基础知识。它非常适合具有软件系统设计经验的实践开发人员，对于熟悉脚本但不一定熟悉设计学习算法的科学家和其他专业人员也很有用。学习TensorFlow基础知识，包括如何执行基本计算构建简单的学习系统以了解其数学基础深入研究数千种应用中使用的完全连接的深度网络使用超参数优化将原型转化为高质量模型使用卷积神经网络处理图像使用递归神经网络处理自然语言数据集使用强化学习解决诸如井字游戏等游戏toe使用包括GPU和张量处理单元在内的硬件训练深度网络
Learn how to solve challenging machine learning problems with TensorFlow, Googles revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. Its ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units