Internet of Things: Evolved Workplace

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Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Reinforcement Learning: Reward-based Learning

Explores artificial neural networks, reward information in the brain, animal conditioning, deep reinforcement learning, and a quiz on rewards.

Project-Based Learning in Communications and Robotics

Introduces a project-based course in communications and robotics, emphasizing practical projects and independent learning to prepare students for real-world challenges.

Manipulating Objects with Robots: Vision-Language Integration

Discusses how robots manipulate objects using natural language instructions and integrates vision-language models for enhanced performance.

Statistical Physics in Machine Learning: Understanding Deep Learning

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Deep Networks versus Shallow Networks: Artificial Neural Networks and Deep Learning

Compares deep networks with shallow networks in artificial neural networks and deep learning, exploring reasons for their performance differences.

Prompting and Alignment

Explores prompting, alignment, and the capabilities of large language models for natural language processing tasks.