Introduction to Artificial Intelligence: Deep Neural Networks - Lecture 19

Explore deep neural networks in this 37-minute lecture from Memorial University's Computer Science 3200 course on Artificial Intelligence. Delve into edge detection, max pooling, and the iterative process of deep learning. Examine the computational requirements and discover practical applications through autoencoding examples. Investigate deep reinforcement learning and the concept of learning from replays. Analyze heuristic search techniques and gain insights into what deep neural networks actually perceive. Conclude by exploring methods to manipulate deep neural networks, providing a comprehensive overview of advanced AI concepts.

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Deep Neural Networks
Edge Detector
Max Pooling
Repeating the Process
Computing Required
Autoencoding Example
Deep Reinforcement Learning
Learning from Replays
Heuristic Search
What do DNN see?
Tricking DNN