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.
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- 5 stars63,86 %
- 4 stars23,19 %
- 3 stars5,71 %
- 2 stars4,11 %
- 1 star3,10 %
Principales reseñas sobre DEEP NEURAL NETWORKS WITH PYTORCH
The explanation is simple and understandable. They explained deep neural networks so beautifully with PyTorch. Thank you very much for this course IBM.
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!
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.
Amazing course for a beginner in Deep Learning & Pytorch.
I gave 4 stars as I expected it to be more pytorch heavy.
Overall, a really good crafted course.
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