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In this course, you will build a useful understanding of the components of a convolutional neural network (CNN) like convolutions and pooling layers etc. In this course, Alex Smola and Tong He show how to implement some computer vision techniques using GluonCV, a computer vision toolkit.
Intended Audience
This course is intended for:
- Developers who are looking to implement common computer vision models
Course Objectives
In this course, you will learn how to:
- Summarize various convolutional neural network components like convolutions, padding, and channels
- Translate the components to code when creating a neural network like LeNet
- Import your data into a Gluon Data Loader for training and transformation
Prerequisites
We recommend that attendees of this course have the following prerequisites:
- A basic understanding of artificial neural networks
- A basic understanding of linear Algebra topics like matrices, matrix multiplication, and dot products
Delivery Method
This course is delivered through:
- Digital training
Duration
- 2 hours
Course Outline
This course covers the following concepts:
- Convolutions
- Padding and stride
- Channels
- Pooling
- LeNet
- Activation functions
- DropOut
- Batch normalization
- Blocks
- The curse of the last layer
- Residual networks
- Data processing
