Modern Deep Convolutional Neural Networks with PyTorch

Description

In this course, you will learn :

• Convolutional Neural Networks
• Image Processing
• Advance Deep Learning Techniques
• Regularization, Normalization
• Transfer Learning

Syllabus :

1. Convolutional Neural Networks

• What is image
• Motivation to Convolutions
• Convolution operation
• Parameters of the convolution
• Non-linear function
• Max Pooling and Average Pooling
• Building deep convolutional network

 

2. Regularization and Normalization

• Overfitting. L2 regularization
• DropOut regularization. DropConnect regularization
• DropBlock regularization
• Early Stopping regularization
• Batch Normalization

 

3. Improving the quality

• Data Augmentation
• Existing datasets
• Modern Architectures
• Transfer Learning

 

4. Boat Recognition Project

• Data Loading
• Data Augmentation
• Transfer Learning: ResNet-18

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