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Deep Neural Networks with PyTorch

IBM via 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.


  • 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


4.4 rating at Coursera based on 1541 ratings

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