利用认知计算与深度学习解决人工智能问题

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"《认知计算：人工智能实战》是一本针对AI和机器学习问题提供完整、实际代码示例的书籍，采用问题解决方案的方式，使深度学习和机器学习对普通开发者变得易于理解和应用。书中通过介绍认知服务API、机器学习平台和库的组合，为读者提供了深入的实践指导。本书首先概述了当前的技术环境，并阐述了机器学习和深度学习在商业场景中的应用。内容涵盖数字助手、计算机视觉、文本分析、语音识别以及机器人流程自动化等领域，为读者提供了一个全面的工具包，以便将这些技术快速且便捷地应用于自己的项目中。重点聚焦于微软认知服务的实现，同时结合TensorFlow和微软认知工具包(CNTK)的实例，以展示深度学习生态系统的多样性。通过阅读本书，你将能够： 1. 使用微软认知服务API构建生产级解决方案。 2. 应用TensorFlow和微软认知工具包(CNTK)进行深度学习。 3. 解决企业在自然语言处理和计算机视觉方面的挑战。 4. 了解机器学习开发周期，从问题定义到大规模部署的全过程。本书由Adnan Masood和Adnan Hashmi撰写，Matt Winkler作序，旨在帮助读者掌握认知计算和人工智能的实际应用，无论是初学者还是有经验的开发者，都能从中受益，提升自己在AI领域的技能和实践能力。" 本书不仅提供了理论知识，还包含了丰富的实战案例，通过具体代码示例，让读者能够在实际操作中学习和理解复杂的算法和技术。对于想要深入了解并应用认知计算和AI技术的开发者来说，这是一本非常实用的参考资料。

xviii

Machine learning does not exist without data. Whether in bulk or in streams, models

are trained, evaluated, and improved through data. Machine learning lets us extract much

greater value out of data of all shapes and sizes. Machine learning can even be used to

enrich data. Consider the most recent code that you wrote dealing with user input. Odds

are, it dealt with numerical values, or relatively short string values. Modern programming

languages are incredibly powerful and efficient when dealing with those data types.

Expand that out a little bit, to images, video, sounds, or large volumes of text. What data

types exist to reason on these, not just manipulate them? Machine learning enriches data

by letting us address a larger variety of data, and by transforming it into things that can be

reasoned over in code. Cognitive APIs, like Microsoft’s Cognitive Services make it easy to

take an image and break it into component parts. Want to know what’s in the image, how

many people are in it, are they happy? All of that is available to you with a simple HTTP

method. The output of that easily goes into our programs to make a decision, such as

adjusting a thermostat based on the number of people in a room.

Devices are becoming smarter, and in many cases, more connected. Machine

learning models, built on data observed from those devices, enable better understanding

of the device and the environment around it. This enables more efficient devices to

be built, future designs to be influenced, but more importantly, can be used to predict

failures or identify anomalies. The “digital exhaust” from these devices is valuable, not

only for training new models, but also for being able to provide a mechanism to evaluate

the impact and outcome of models currently deployed. This stream of exhaust is key to

creating the virtuous cycle of model development, improvement, and improvements in

outcomes.

One of the most inspiring examples of this is Microsoft’s AI for Earth initiative, which

looks to provide grant funding to organizations leveraging AI to advance sustainability.

I’ve had the opportunity to interact with a number of the grantees, and the stories

of how they are transforming the way we consume, conserve, and manage natural

resources remind us of the power of software as a force for good. Every industry is being

transformed, and that is being powered by machine learning.

The other key aspect of “Why now,” is the cloud. The emergence of plentiful,

and powerful, compute capacity in the cloud, along with the hardware and software

innovations in the GPU space has helped to foster a massive wave of innovation, with

much of that coming in the area of deep learning. The fundamentals behind deep

learning are not new, neural networks modeling the functionality of the neurons in the

brain originated in the middle of the last century. Three things needed to come together

foreword

xxi

Preface

The hype around disruptive new technologies travels from the peak of inflated

expectations to the valley of disillusionment until it hits its stride in the plateau of

productivity. This journey is often counterproductive, and AI and machine learning are

not an exception to this rule. The jury is still out on why it is called deep-learning hype

and not backpropaganda, but I digress. It is safe to say that the ubiquitous nature of

machine learning–based applications enables today’s commerce and innovation. As one

discerns what is possible with AI for one’s business, the concrete use cases are no longer

limited by lack of vision and instead become quite evident. This figurative leap of faith

requires a future-oriented growth mindset and thought leadership. We quickly see the

key concerns from C-suite start with Why AI, quickly followed by How and then When!

This book you are holding is written with the motive of exploring the art of the

possible with its readers. This manuscript is by no means perfect; however, it intends

to give you tools and an understanding of the contemporary AI landscape. Intelligent

correlation using AI and machine learning helps companies achieve richer search

experiences; where and how to use this to accomplish real productivity gains requires

you, the domain expert, to connect your business use case with the algorithms and

models. AI data insights and discovery are transforming customer service into a science

where previously there was only art. I implore you to explore novel use cases across the

spectrum, such as bias-free product-recommendation engines, detection of potential

predatory tactics with natural language annotations, contract analysis to augment and

accelerate the discovery process, inventory monitoring and analysis using computer

vision, and digital assistants that can comprehend, summarize, and answer queries on

diverse enterprise corpora; the possibilities are bounded only by your imagination.

Enterprise-grade AI search is helping enterprises cut through the fog of uncertainty,

helping in product development, and extracting real business value by intelligent

process compression.

Let’s embark on the AI-first journey of scholastic gradient descent.

Adnan Masood, PhD

Chief Architect AI & Machine Learning—Microsoft MVP (Artificial Intelligence); visiting

scholar, Department of Computer Engineering, Stanford University.

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