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.
Об этом курсе
- 5 stars64,31 %
- 4 stars23,04 %
- 3 stars5,66 %
- 2 stars3,95 %
- 1 star3,02 %
Лучшие отзывы о курсе DEEP NEURAL NETWORKS WITH PYTORCH
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
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.
Good pacing, great examples and the assignments are doable within the time allocated for them. Combines both technical information and applied code.
While there are some minor technical issues loading out of date libraries, the material and subjects are incredibly useful. This course is very difficult and welcome
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на сертификацию?
Остались вопросы? Посетите Центр поддержки учащихся.