Offered By: IBM
Deep Learning with Python and PyTorch
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.
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Deep Learning
At a Glance
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.
What you'll learn
- Apply knowledge of Deep Neural Networks and related machine learning methods
- Build and Train Deep Neural Networks using PyTorch
- Build Deep learning pipelines
Syllabus
Module 1 - Classification
- Softmax Regression
- Softmax in PyTorch Regression
- Training Softmax in PyTorch Regression
Module 2 - Neural Networks
- Introduction to Networks
- Network Shape Depth vs Width
- Back Propagation
- Activation functions
Module 3 - Deep Networks
- Dropout
- Initialization
- Batch normalization
- Other optimization methods
Module 4 - Computer Vision Networks
- Convolution
- Max Polling
- Convolutional Networks
- Pre-trained Networks
Module 5 - Computer Vision Networks
- Convolution
- Max Pooling
- Convolutional Networks
- Training your model with a GPU
- Pre-trained Networks
Module 6 Dimensionality reduction and autoencoders
- Principle component analysis
- Linear autoencoders
- Autoencoders
- Transfer learning
- Deep Autoencoders
Module 7 -Independent Project
Estimated Effort
6 Wks 2/4 Hrs
Level
Beginner
Skills You Will Learn
Artificial Intelligence, Autoencoders, Machine Learning, Python (Programming Language), PyTorch
Language
English
Course Code
DL0110EN