Deep Learning with Python and PyTorch

Description

In this course, you will learn:

• Apply what you've learned about Deep Neural Networks and other machine learning techniques.
• PyTorch can be used to create and train deep neural networks.
• Create pipelines for deep learning.

Syllabus:

1. Classification

• Softmax Regression
• Softmax in PyTorch Regression
• Training Softmax in PyTorch Regression
   

2. Neural Networks

• Introduction to Networks
• Network Shape Depth vs Width
• Back Propagation
• Activation functions
   

3. Deep Networks

• Dropout
• Initialization
• Batch normalization
• Other optimization methods
   

4. Computer Vision Networks

• Convolution
• Max Polling
• Convolutional Networks
• Pre-trained Networks
   

5. Computer Vision Networks

• Convolution
• Max Pooling
• Convolutional Networks
• Training your model with a GPU
• Pre-trained Networks
   

6. Dimensionality reduction and autoencoders

• Principle component analysis
• Linear autoencoders
• Autoencoders
• Transfer learning
• Deep Autoencoders

7. Independent Project

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