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Intro
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To discover from images what is present in the world, where things are, what actions are taking place, to predict and anticipate events in the world
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The rise and impact of computer vision
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Impact: Self-Driving Cars
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Impact: Medicine, Biology, Healthcare
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Images are Numbers
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Tasks in Computer Vision
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Manual Feature Extraction
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Learning Feature Representations Can we learn a hierarchy of features directly from the data instead of hand engineering
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Fully Connected Neural Network
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Using Spatial Structure
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Feature Extraction with Convolution
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Filters to Detect X Features
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The Convolution Operation
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Producing Feature Maps
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Convolutional Layers: Local Connectivity
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Introducing Non-Linearity
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Pooling
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Putting it all together
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An Architecture for Many Applications
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Classification: Breast Cancer Screening
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Semantic Segmentation: Fully Convolutional Networks
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Continuous Control: Navigation from Vision
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End-to-End Framework for Autonomous Navigation
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Deep Learning for Computer Vision: Summary
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore convolutional neural networks for computer vision in this comprehensive lecture from MIT's Introduction to Deep Learning course. Delve into the rise and impact of computer vision, including applications in self-driving cars and healthcare. Learn about image representation, manual feature extraction, and the transition to learning feature representations directly from data. Understand the architecture of convolutional neural networks, including convolutional layers, non-linearity, and pooling. Discover how these networks can be applied to various tasks such as classification, semantic segmentation, and continuous control for autonomous navigation. Gain insights into the power of deep learning for computer vision and its potential to revolutionize multiple industries.

Convolutional Neural Networks

Alexander Amini
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#Computer Science #Artificial Intelligence #Neural Networks #Convolutional Neural Networks (CNN) #Deep Learning #Computer Vision #Image Recognition #Machine Learning #Feature Extraction
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Intro