《Learning TensorFlow》：深度学习入门与实战指南

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《学习TensorFlow：构建深度学习系统的指南》是一本专为数据科学家、工程师、学生和研究人员设计的全面教程，由Tom Hope、Yehezkel S. Resheff和Itay Lieder合著。本书针对的是快速发展的深度学习领域，特别是在计算机视觉、自然语言处理（NLP）、语音识别和通用预测分析方面广泛应用的TensorFlow，它目前是开源软件中的领军者。书中强调了实践导向的学习方法，对初学者友好，同时也深入探讨了高级主题，旨在帮助读者掌握如何构建能够应用于实际生产环境的深度学习系统。通过本书，读者将了解到如何利用TensorFlow的强大功能来解决复杂问题，并利用大量数据训练深度神经网络，实现前所未有的准确度。作者们结合理论与实践，从基础概念入手，逐步引导读者了解TensorFlow的工作原理、架构以及关键组件，如张量（Tensor）、图（Graph）、会话（Session）等。此外，书中的内容还包括但不限于： 1. **TensorFlow安装与配置**：介绍了如何在不同平台上安装和配置TensorFlow，以便于开发环境的搭建。 2. **核心概念**：讲解神经网络、反向传播算法和优化器，以及如何构建和调整深度神经网络模型。 3. **数据预处理**：涵盖了数据清洗、标准化、编码和批处理等步骤，这对于深度学习模型的性能至关重要。 4. **计算机视觉应用**：展示了如何使用TensorFlow进行图像分类、对象检测和图像分割等任务。 5. **自然语言处理**：介绍了如何处理文本数据，如词嵌入、序列建模和情感分析等。 6. **深度学习模型调优与评估**：讨论了模型选择、超参数调整和性能评估的方法，确保模型的效率和效果。 7. **部署与监控**：指导读者如何将训练好的模型部署到生产环境中，并监控其性能。 8. **案例研究**：通过实际项目演示，帮助读者理解和应用所学知识。最后，版权信息部分提到，本书享有2017年的版权，并且可供教育、商业或销售推广用途。出版商O'Reilly Media提供了印刷版和在线版本，读者可以通过联系出版社获取更多信息。《学习TensorFlow》是一本既适合入门者提升技能，也适合进阶者深入探索深度学习技术的专业参考书，是任何致力于在这个领域发展的专业人士的必备读物。

Figure 1-3. Going from images to text with image captioning (illustrative example).

Text summarization

Natural language understanding (NLU) is a key capability for building AI systems.

Tremendous amounts of text are generated every day: web content, social media,

news, emails, internal corporate correspondences, and many more. One of the most

sought-after abilities is to summarize text, taking long documents and generating

succinct and coherent sentences that extract the key information from the original

texts (Figure 1-4). As we will see later in this book, TensorFlow comes with powerful

features for training deep NLU networks, which can also be used for automatic text

summarization.

4 | Chapter 1: Introduction

Figure 1-5. A dataow computation graph. Data in the form of tensors ows through a

graph of computational operations that make up our deep neural networks.

A High-Level Overview

TensorFlow, in the most general terms, is a software framework for numerical com‐

putations based on dataflow graphs. It is designed primarily, however, as an interface

for expressing and implementing machine learning algorithms, chief among them

deep neural networks.

TensorFlow was designed with portability in mind, enabling these computation

graphs to be executed across a wide variety of environments and hardware platforms.

With essentially identical code, the same TensorFlow neural net could, for instance,

be trained in the cloud, distributed over a cluster of many machines or on a single

laptop. It can be deployed for serving predictions on a dedicated server or on mobile

device platforms such as Android or iOS, or Raspberry Pi single-board computers.

TensorFlow is also compatible, of course, with Linux, macOS, and Windows operat‐

ing systems.

The core of TensorFlow is in C++, and it has two primary high-level frontend lan‐

guages and interfaces for expressing and executing the computation graphs. The most

developed frontend is in Python, used by most researchers and data scientists. The

C++ frontend provides quite a low-level API, useful for efficient execution in embed‐

ded systems and other scenarios.

Aside from its portability, another key aspect of TensorFlow is its flexibility, allowing

researchers and data scientists to express models with relative ease. It is sometimes

revealing to think of modern deep learning research and practice as playing with

“LEGO-like” bricks, replacing blocks of the network with others and seeing what hap‐

pens, and at times designing new blocks. As we shall see throughout this book, Ten‐

sorFlow provides helpful tools to use these modular blocks, combined with a flexible

API that enables the writing of new ones. In deep learning, networks are trained with

6 | Chapter 1: Introduction

a feedback process called backpropagation based on gradient descent optimization.

TensorFlow flexibly supports many optimization algorithms, all with automatic dif‐

ferentiation—the user does not need to specify any gradients in advance, since Ten‐

sorFlow derives them automatically based on the computation graph and loss

function provided by the user. To monitor, debug, and visualize the training process,

and to streamline experiments, TensorFlow comes with TensorBoard (Figure 1-6), a

simple visualization tool that runs in the browser, which we will use throughout this

book.

Figure 1-6. TensorFlow’s visualization tool, TensorBoard, for monitoring, debugging, and

analyzing the training process and experiments.

Key enablers of TensorFlow’s flexibility for data scientists and researchers are high-

level abstraction libraries. In state-of-the-art deep neural nets for computer vision or

NLU, writing TensorFlow code can take a toll—it can become a complex, lengthy, and

cumbersome endeavor. Abstraction libraries such as Keras and TF-Slim offer simpli‐

fied high-level access to the “LEGO bricks” in the lower-level library, helping to

streamline the construction of the dataflow graphs, training them, and running infer‐

ence. Another key enabler for data scientists and engineers is the pretrained models

that come with TF-Slim and TensorFlow. These models were trained on massive

amounts of data with great computational resources, which are often hard to come by

and in any case require much effort to acquire and set up. Using Keras or TF-Slim, for

example, with just a few lines of code it is possible to use these advanced models for

inference on incoming data, and also to fine-tune the models to adapt to new data.

The flexibility and portability of TensorFlow help make the flow from research to

production smooth, cutting the time and effort it takes for data scientists to push

their models to deployment in products and for engineers to translate algorithmic

ideas into robust code.

A High-Level Overview | 7

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