Book Description
While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment, or predict early failures in rotating machinery? This practical book teaches you the skills necessary to put AI and machine learning to work at your company.
Applied Machine Learning and AI for Engineersprovides examples and illustratio蒂芙尼ns from the AI and ML course Prosise teaches at companies and research institutions worldwide. There’s no fluff and no scary equations—just a fast start for engineers and software developers, complete with hands-on examples.
This book helps you:
- Learn what machine learning and deep learning are and what they can accomplish
- Understand how popular learning algorithms work and when to apply them
- Build machine learning models in Python with Scikit-Learn, and neural networks with Keras and TensorFlow
- Train and score regression models and binary and multiclass classification models
- Build facial recognition models and object detection models
- Build language models that respond to natural-language queries and translate text to other languages
- Use Cognitive Services to infuse AI into the apps that you write
中文:
书名:工程师应用机器学习和人工智能: 解决算法无法解决的业务问题
虽然许多人工智能入门指南都是变相的微积分书,但这本书大多避开了数学。相反,作者Jeff Prosise帮助工程师和软件开发人员建立对AI的直观理解,以解决业务问题。是否需要创建一个系统来检测雨林中非法砍伐的声音,分析文本以获取情感,或预测旋转机械的早期故障?这本实用的书教你必要的技能,让人工智能和机器学习在你的公司工作。
应用机器学习和人工智能工程师提供了人工智能和ML课程Prosise在全球公司和研究机构教授的例子和插图。没有绒毛,也没有可怕的方程式-对于工程师和软件开发人员来说,这是一个快速的开始,并附有动手示例。
这本书帮助你:
- 了解什么是机器学习和深度学习,以及它们可以完成什么
- 了解流行学习算法的工作原理以及何时应用它们
- 用Scikit-Learn构建Python机器学习模型,用Keras和TensorFlow构建神经网络
- 训练和分数回归模型与二分类和多类分类模型
- 建立人脸识别模型和物体检测模型
- 构建响应自然语言查询并将文本翻译成其他语言的语言模型
- 使用认知服务将AI注入到您编写的应用程序中
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