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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Acerca de este Curso
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Programa - Qué aprenderás en este curso
Tensor and Datasets
Linear Regression
Linear Regression PyTorch Way
Multiple Input Output Linear Regression
Logistic Regression for Classification
Softmax Rergresstion
Shallow Neural Networks
Reseñas
- 5 stars64,28 %
- 4 stars23,02 %
- 3 stars5,69 %
- 2 stars3,87 %
- 1 star3,11 %
Principales reseñas sobre 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
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.
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.
Good introduction of PyTorch. There are some minor code errors and inconsistencies in the material but generally not difficult to figure it out.
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