Deep Neural Networks with PyTorch (Coursera)

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch’s tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression.

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.

Course 4 of 6 in the IBM AI Engineerin.

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

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