Neural Networks and Deep Learning Fundamentals

Explore the fundamentals of neural networks and deep learning through this comprehensive video series that breaks down complex concepts into intuitive explanations. Begin with understanding what neural networks are and how they function, then dive into gradient descent and the learning mechanisms that power these systems. Master the crucial concept of backpropagation through both intuitive explanations and mathematical foundations, learning how neural networks adjust their parameters to improve performance. Discover the architecture and mechanics of transformers, the revolutionary technology behind large language models, including detailed explanations of attention mechanisms and how these models might store and retrieve factual information. Gain insights into modern AI applications including large language models and AI-generated images and videos, with expert guest content providing additional perspectives on cutting-edge developments in artificial intelligence and machine learning.

Syllabus

But what is a neural network? | Deep learning chapter 1
Gradient descent, how neural networks learn | Deep Learning Chapter 2
Backpropagation, intuitively | Deep Learning Chapter 3
Backpropagation calculus | Deep Learning Chapter 4
Large Language Models explained briefly
Transformers, the tech behind LLMs | Deep Learning Chapter 5
Attention in transformers, step-by-step | Deep Learning Chapter 6
How might LLMs store facts | Deep Learning Chapter 7
But how do AI images and videos actually work? | Guest video by Welch Labs