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Convolutional Neural Networks - Full Stack Deep Learning - Spring 2021

The Full Stack via YouTube

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Overview

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This lecture explains convolutional filters and key operations used in ConvNets, including strides, padding, dilation, and pooling. It concludes with an examination of the LeNet architecture.

Syllabus

- Introduction
- Convolutional Filters
- Filter Stacks and ConvNets
- Strides and Padding
- Filter Math
- Convolution Implementation Notes
- Increasing the Receptive Field with Dilated Convolutions
- Decreasing the Tensor Size with Pooling and 1x1-Convolutions
- LeNet Architecture

Taught by

The Full Stack

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