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 stars63,88 %
- 4 stars23,14 %
- 3 stars5,72 %
- 2 stars4,12 %
- 1 star3,11 %
Top-Bewertungen von DEEP NEURAL NETWORKS WITH PYTORCH
this course provides a very good and cohesive introduction to Neural Networks. I learned a lot during my journey and I recommend it for anyone interesting in the field.
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.
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.
Wonderful course!!! Best among all the courses under AI Engineer Certificate by IBM. Deep learning always haunted me with the maths involved but now I get a very good start with this.
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