Fine-Tuning and Customisation

Fine-Tuning and Customisation

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Understanding Artificial Intelligence Ecosystems and Technologies

Module 1	Introduction	-
Unit 1	Introduction
Module 2	The Evolution of Artificial Intelligence	-
Unit 1	Understanding the Historical Development of AI
Unit 2	The Origins of Artificial Intelligence
Unit 3	Symbolic AI and Expert Systems
Unit 4	The AI Winters and Periods of Stagnation
Unit 5	The Rise of Machine Learning
Unit 6	Deep Learning and the Modern AI Revolution
Unit 7	The Emergence of Generative AI
Unit 8	Current Trends and Future Directions
Unit 9	Module summary
Module 3	The AI Technology Landscape	-
Unit 1	Understanding the Main Categories of AI
Unit 2	Rule-Based Systems
Unit 3	Machine Learning Systems
Unit 4	Deep Learning Systems
Unit 5	Generative AI
Unit 6	Reinforcement Learning
Unit 7	Multi-Agent Systems
Unit 8	Robotics and Embodied AI
Unit 9	Hybrid AI Systems
Unit 10	Module Summary
Module 4	Machine Learning Fundamentals	-
Unit 1	How Machines Learn
Unit 2	Supervised Learning
Unit 3	Unsupervised Learning
Unit 4	Semi-Supervised Learning
Unit 5	Reinforcement Learning
Unit 6	Training, Validation and Testing
Unit 7	Datasets and Data Quality
Unit 8	Model Performance and Evaluation
Unit 9	Module Summary
Module 5	Deep Learning and Neural Networks	-
Unit 1	The Foundations of Modern AI
Unit 2	Artificial Neural Networks
Unit 3	Deep Neural Networks
Unit 4	Computer Vision Systems
Unit 5	Speech Recognition Technologies
Unit 6	Pattern Recognition Capabilities
Unit 7	Strengths and Limitations of Deep Learning
Unit 8	Computational Requirements
Unit 9	Module Summary
Module 6	Generative AI and Large Language Models	-
Unit 1	Understanding the Technology Behind ChatGPT and Similar Systems
Unit 2	What is Generative AI?
Unit 3	Foundation Models
Unit 4	Large Language Models (LLMs)
Unit 5	Transformers and Attention Mechanisms
Unit 6	Prompting and Context Windows
Unit 7	Fine-Tuning and Customisation
Unit 8	Multimodal AI Systems
Unit 9	Current Limitations and Challenges
Unit 10	Module Summary