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.
About this Course
- 5 stars64.29%
- 4 stars23.02%
- 3 stars5.71%
- 2 stars3.94%
- 1 star3.01%
TOP REVIEWS FROM DEEP NEURAL NETWORKS WITH PYTORCH
In-depth course, goes in much more detail than the usual introductory courses, also emphasizes on practical hands on rather than theoretical knowledge
Excellent Course. I love the way the course was presented. There were a lot of practical and visual examples explaining each module. It is highly recommended!
SO far, this has been the best designed and most informative of the four courses that I have taken so far in the IBM AI Engineering Certification.
Great introduction to deep learning with pytorch. It would help if the notebooks in the labs take shorter to run so that the students can experiment with the code and the models.
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