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
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