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.
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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
