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chapter_programming_model/index.md

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+# Programming Model
+
+Machine learning frameworks comprise various components that facilitate
+the efficient development of algorithms, data processing, model
+deployment, performance optimization, and hardware acceleration. When
+designing the application programming interfaces (APIs) for these
+components, a key consideration is striking the right balance between
+framework performance and usability. To achieve optimal performance,
+developers utilize C or C++, as these programming languages enable
+efficient invocation of the APIs provided by the operating system and
+hardware accelerators.
+
+Regarding usability, machine learning framework users, including data
+scientists, biologists, chemists, and physicists, often possess strong
+industrial backgrounds and are skilled in using high-level scripting
+languages like Python, Matlab, R, and Julia. While these languages offer
+remarkable programming usability, they lack deep optimization
+capabilities for underlying hardware or operating systems compared to C
+and C++. Therefore, the core design objective of machine learning
+frameworks encompasses two aspects: providing easy-to-use APIs for
+implementing algorithms using high-level languages like Python, and
+providing low-level APIs centered around C and C++ to assist framework
+developers in implementing numerous high-performance components and
+efficiently executing them on hardware. This chapter describes
+strategies for achieving this design objective.
+
+The chapter aims to achieve the following learning objectives:
+
+1.  Understanding the workflows and programming principles of machine
+    learning frameworks.
+
+2.  Understanding the design of neural network models and layers.
+
+3.  Understanding how machine learning frameworks bridge Python and
+    C/C++ functions.
+
+4.  Understanding the support for functional programming in machine
+    learning frameworks.