Explore the foundations of deep learning in this comprehensive lecture from MIT's Introduction to Deep Learning course. Delve into key concepts like perceptrons, neural networks, activation functions, loss functions, and gradient descent. Learn about crucial techniques such as backpropagation, setting learning rates, batched gradient descent, and regularization methods like dropout and early stopping. Gain insights into why deep learning is transforming various fields and how to apply neural networks effectively. Follow along with practical examples and a clear, structured approach to understanding the core principles of deep learning.
Overview
Syllabus
- Introduction
- Course information
- Why deep learning?
- The perceptron
- Activation functions
- Perceptron example
- From perceptrons to neural networks
- Applying neural networks
- Loss functions
- Training and gradient descent
- Backpropagation
- Setting the learning rate
- Batched gradient descent
- Regularization: dropout and early stopping
- Summary
Taught by
https://www.youtube.com/@AAmini/videos
