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Intro

Vision: Evolutionary Origins

The Visual Cortex

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

Filters to Detect X Features

The Convolution Operation

Producing Feature Maps

Feature Extraction with Convolution

Convolutional Layers: Local Connectivity

Introducing Non-Linearity

Pooling

CNNs for Classification: Feature Learning

CNNs: Training with Backpropagation

ImageNet Dataset

ImageNet Challenge: Classification Task

An Architecture for Many Applications

Beyond Classification

Semantic Segmentation: FCNS

Driving Scene Segmentation

Object Detection with R-CNN

Image Captioning using RNNS

Class Activation Maps (CAM)

Data, Data, Data

Deep Learning for Computer Vision: Impact

Impact: Face Recognition

Impact: Self-Driving Cars

Impact: Medicine nature

Deep Learning for Computer Vision: Review

Description:

Explore a comprehensive lecture on deep learning for computer vision in this 35-minute video from MIT's Introduction to Deep Learning course. Dive into the fundamentals of convolutional neural networks, including their architecture, training process, and applications. Learn about evolutionary origins of vision, the visual cortex, image representation, and various computer vision tasks. Discover how CNNs extract features using convolution operations, pooling layers, and non-linearities. Examine advanced topics such as semantic segmentation, object detection, and image captioning. Gain insights into the impact of deep learning in face recognition, self-driving cars, and medicine. Understand the importance of data in computer vision applications and review key concepts covered in the lecture.

Convolutional Neural Networks

Alexander Amini

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#Computer Science #Artificial Intelligence #Neural Networks #Convolutional Neural Networks (CNN) #Deep Learning #Computer Vision #Object Detection #Feature Detection #Backpropagation #Semantic Segmentation

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