In this course, Sam Woods delves into the fascinating world of artificial intelligence and machine learning, focusing on the development of Bionic GPTs (Generative Pre-Trained Transformers) and AI agents. The course is designed to provide students with a comprehensive understanding of the latest advancements in AI and its applications.
Course Objectives:
Understand the basics of artificial intelligence and machine learning
Learn how to develop and train Bionic GPTs
Explore the capabilities and limitations of AI agents
Develop practical skills in AI programming and deployment
Course Outline:
Introduction to Artificial Intelligence and Machine Learning
Overview of AI and its applications
Fundamentals of machine learning: supervised and unsupervised learning, neural networks, and deep learning
Introduction to Bionic GPTs and their role in AI
Bionic GPTs – Theory and Implementation
In-depth exploration of Bionic GPTs: architecture, components, and training methods
Hands-on training with Bionic GPTs using popular frameworks such as PyTorch and TensorFlow
Case studies: applications of Bionic GPTs in natural language processing, computer vision, and speech recognition
AI Agents – Design and Development
Introduction to AI agents: types, architectures, and characteristics
Designing and developing AI agents using popular frameworks such as OpenCV and Robot Operating System (ROS)
Case studies: applications of AI agents in robotics, autonomous vehicles, and game playing
Advanced Topics in AI
Deep learning techniques: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks
Transfer learning and fine-tuning pre-trained models
Exploring emerging trends in AI: Explainable AI, Adversarial Attacks, and Fairness in AI
Practical Applications of AI Agents
Case studies: real-world applications of AI agents in industries such as healthcare, finance, and transportation
Hands-on exercises: designing and implementing AI agents for specific tasks
Challenges and limitations of AI agents in real-world scenarios
Future Directions in AI Research
Emerging trends in AI research: multimodal learning, transfer learning, and reinforcement learning
Exploring the potential applications of AI in areas such as space exploration, education, and social media
Future directions for AI research: ethics, bias, and explainability
