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
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- 5 stars64.09%
- 4 stars23.08%
- 3 stars5.75%
- 2 stars4.02%
- 1 star3.04%
TOP REVIEWS FROM DEEP NEURAL NETWORKS WITH PYTORCH
It was a very informative and interesting lecture. I learn a lot about the details when using PyTorch to build and train a deep neural network. I am so thankful.
Good pacing, great examples and the assignments are doable within the time allocated for them. Combines both technical information and applied code.
The material is good. I found the assignments a bit too easy. A bit more challenge would be welcome. I found the artificial voice with the lectures to be distracting. The AI isn't quite good enough.
By this course I can understand the basic concept for building neural network or deep lerning model using PyTorch. Very Good course to beginner.
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