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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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 stars64,31 %
- 4 stars23 %
- 3 stars5,69 %
- 2 stars3,87 %
- 1 star3,11 %
Principales reseñas sobre DEEP NEURAL NETWORKS WITH PYTORCH
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.
Excellent course, works its way through basics to fully fledged machine learning models at a good pace. A few of the examples used in the lab code throw errors, these should be rectified
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!
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.
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