Machine and Deep Learning Using MATLAB: Algorithms and Tools for Scientists and Engineers

In-depth resource covering machine and deep learning methods using MATLAB tools and algorithms, providing insights and automating decision-making processes

Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code.

The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues.

Readers will also find information on:

• Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning)
• Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response)
• Image acquisition and analysis in the form of applying one of neural networks, and training model accuracy, model loss, and RMSE for training a given model
• Retraining and creation for image labeling, object identification, regression classification, and text recognition

Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.

《使用 MATLAB 进行机器和深度学习》是一本深入介绍使用 MATLAB 工具和算法进行机器和深度学习方法的资源，提供洞察力并自动化决策过程。

本书向初入职场的专业人士介绍了 MATLAB 的强大功能，通过解释相关的 MATLAB 工具或应用程序以及其在给定方法或一组方法中的使用方式，来探索机器和深度学习应用。书中详细解释了这些工具的属性，包括输入和输出参数，通过附带的文本或表格指出了其限制或适用性，并展示了完整的运行示例，包括所需的 MATLAB 命令提示代码。

本书还通过图表形式与给定的 MATLAB 代码并行展示结果，并且后续可以将 MATLAB 编写的代码作为模板，尝试解决新的案例或数据集。每章还提供了自学的实例，并附有解决方案和编码示例的相关网站。突出的注释引起用户对关键点或问题的注意。

读者还将找到以下信息：
- 通过应用计算算法来预测数值数据模式（聚类或无监督学习）的数值数据获取和分析
- 预测变量和响应变量之间的关系（有监督学习），分为分类（离散响应）和回归（连续响应）
- 应用神经网络之一进行图像获取和分析，并训练模型准确性、模型损失和训练给定模型的 RMSE
- 图像标记、对象识别、回归分类和文本识别的重新训练和创建

《使用 MATLAB 进行机器和深度学习》是一本对于在工程和科学领域有一定熟悉度的专业人士、高级学生和研究人员来说非常有用且内容全面的资源，希望掌握该软件及其众多应用的读者可以从中受益。

Kamal I. M. Al-Malah received his PhD degree from Oregon State University in 1993. He served as a Professor of Chemical Engineering in Jordan and Gulf countries, as well as Former Chairman of the Chemical Engineering Department at the University of Hail in Saudi Arabia. Professor Al-Malah is an expert in both Aspen Plus^(R) and MATLAB^(R) applications. He has created a bundle of Windows-based software for engineering applications.

Kamal I. M. Al-Malah於1993年從俄勒岡州立大學獲得博士學位。他曾在約旦和海灣國家擔任化學工程教授，並擔任沙特阿拉伯哈伊爾大學化學工程系的前主任。Al-Malah教授是Aspen Plus®和MATLAB®應用的專家。他開發了一套基於Windows的工程應用軟件。