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.

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

Homepage:

https://samueljwoods.com/

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