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Learner Reviews & Feedback for Deep Neural Networks with PyTorch by IBM

4.7
19 ratings
2 reviews

About the Course

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. 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...

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1 - 3 of 3 Reviews for Deep Neural Networks with PyTorch

By Shinhoo K

Nov 17, 2019

Awesome! This course gives me the basic workflow for using machine learning technique in my research! The materials in the form of Jupyter lab really help!

By Pavan D

Nov 19, 2019

very intuitive and in depth

By Daniel K

Nov 20, 2019

Amazing, really informative and helps a lot !!! really liked this course and would recommend this to anyone interested in Deep learning!