Master Neural Networks: Key Concepts from CS50's AI with Python 🚀

00:00:00 - Introduction
00:00:15 - Neural Networks
00:05:41 - Activation Functions
00:07:47 - Neural Network Structure
00:16:02 - Gradient Descent
00:30:00 - Multilayer Neural Networks
00:32:58 - Backpropagation
00:36:27 - Overfitting
00:38:52 - TensorFlow
00:53:01 - Computer Vision
00:58:09 - Image Convolution
01:08:18 - Convolutional Neural Networks
01:27:03 - Recurrent Neural Networks

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

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LICENSE

CC BY-NC-SA 4.0
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
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David J. Malan
https://cs.harvard.edu/malan
malan@harvard.edu