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Intro

Images are Numbers

Tasks in Computer Vision

High Level Feature Detection

Manual Feature Extraction

Learning Feature Representations

Fully Connected Neural Network

Using Spatial Structure

Applying Filters to Extract Features

Feature Extraction with Convolution

Filters to Detect X Features

The Convolution Operation

Producing Feature Maps

Convolutional Layers: Local Connectivity

Introducing Non-Linearity

Pooling

CNNs for Classification: Feature Learning

CNNs for Classification: Class Probabilities

CNNs: Training with Backpropagation

ImageNet Dataset

ImageNet Challenge: Classification Task

An Architecture for Many Applications

Beyond Classification

Semantic Segmentation: FCNs

Driving Scene Segmentation

Image Captioning using RNNS

Impact: Face Detection

Impact: Self-Driving Cars

Impact: Healthcare

Deep Learning for Computer Vision: Summary

Description:

Explore convolutional neural networks in this comprehensive lecture from MIT's Introduction to Deep Learning course. Dive into deep computer vision, covering topics from basic image representation to advanced CNN architectures. Learn about manual and learned feature extraction, convolution operations, pooling, and the application of CNNs for classification tasks. Discover how CNNs are trained using backpropagation and their performance on the ImageNet dataset. Examine cutting-edge applications in semantic segmentation, image captioning, and real-world impacts in face detection, self-driving cars, and healthcare. Gain a solid foundation in deep learning techniques for computer vision through this in-depth presentation by lecturer Ava Soleimany.

Convolutional Neural Networks

Alexander Amini

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#Computer Science #Artificial Intelligence #Neural Networks #Convolutional Neural Networks (CNN) #Deep Learning #Computer Vision #Machine Learning #Feature Extraction #Autonomous Vehicles #Self-Driving Cars #Semantic Segmentation