Coursera
Free Online Course (Audit)
English
Paid Certificate Available
7 weeks long, 31 hours worth of material
selfpaced
Overview
Class Central Tips
The course will teach you how to develop deep learning models usingPytorch. The course will start with Pytorch'stensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed byFeedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
Learning Outcomes:
After completing this course, learners will be able to:
•explain and apply their knowledge of Deep Neural Networks and related machine learning methods
•know how to use Python libraries such as PyTorchfor Deep Learning applications
•build Deep Neural Networks using PyTorch
Learning Outcomes:
After completing this course, learners will be able to:
•explain and apply their knowledge of Deep Neural Networks and related machine learning methods
•know how to use Python libraries such as PyTorchfor Deep Learning applications
•build Deep Neural Networks using PyTorch
Syllabus
- Tensor and Datasets
- Linear Regression
- Linear Regression PyTorch Way
- Multiple Input Output Linear Regression
- Logistic Regression for Classification
- Softmax Rergresstion
- Shallow Neural Networks
- Deep Networks
- Convolutional Neural Network
- Peer Review
Taught by
Joseph Santarcangelo
