Course description

The following content is introduced to let developers understand the ins and outs of the AI system at the beginning, form a preliminary understanding of the systematization and hierarchy of the AI system, and lay a preliminary foundation for the subsequent development of specific AI system content.

"History and Development of AI": Understand the history, current situation and development of deep learning through artificial intelligence, especially the impact of the emergence of large models on artificial intelligence.

• "Driving Force of AI Development": introduces the driving force of deep learning development: that is, the development of algorithms, frameworks, architectures and computing power, and understands that in addition to upper-layer applications and algorithms, the hardware resources of the underlying abstraction and management of the system are also important components of AI systems.

• "AI System Full-Stack Architecture": The system is often in the middle layer to manage tasks at the top and abstract and manage the hardware at the bottom. AI systems are no exception, and by demonstrating the full-stack composition and ecosystem architecture of AI systems, developers can form a systematic knowledge framework and lay the foundation for future AI system learning.

• Principles and Examples of AI Systems: Review the classical theories of computer system design and guide the learning of subsequent AI systems, and understand the theories, principles and assumptions that affect the design of AI systems. Through simple examples, developers are inspired to understand the AI systemic problems behind the algorithm, and to understand the role of the AI system on the upper-layer algorithm and the underlying hardware.

What will i learn?