Deep Neural Networks with PyTorch (Coursera)

Followed by Feedforward 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 PyTorch for Deep Learning applications

• build Deep Neural Networks using PyTorch

Course 4 of 6 in the IBM AI Engineering Professional Certificate.

Syllabus

WEEK 1

Tensor and Datasets

WEEK 2

Linear Regression

Linear Regression PyTorch Way

WEEK 3

Multiple Input Output Linear Regression

Logistic Regression for Classification

WEEK 4

Softmax Rergresstion

Shallow Neural Networks

WEEK 5

Deep Networks

WEEK 6

Convolutional Neural Network

WEEK 7

Peer Review