Deep Learning with Python and PyTorch
Learn on
BeginnerCourse
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.
Language
- English
Topic
- Deep Learning
Skills You Will Learn
- PyTorch, Artificial Intelligence, Python (Programming Language), Autoencoders, Machine Learning
Offered By
- IBM
Estimated Effort
- 6 wks 2/4 hrs
Platform
- edX
Last Update
- January 14, 2025
About this Course
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
Language
- English
Topic
- Deep Learning
Skills You Will Learn
- PyTorch, Artificial Intelligence, Python (Programming Language), Autoencoders, Machine Learning
Offered By
- IBM
Estimated Effort
- 6 wks 2/4 hrs
Platform
- edX
Last Update
- January 14, 2025
Instructors
Joseph Santarcangelo
Senior Data Scientist at IBM
Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
Read more