Chevron Left
返回到 Deep Neural Networks with PyTorch

學生對 IBM 提供的 Deep Neural Networks with PyTorch 的評價和反饋

45 個評分
3 個審閱


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. Learning Outcomes: After completing this course, learners will be able to: • explain and apply their knowledge of Deep Neural Networks and related machine learning methods • know how to use Python libraries such as PyTorch for Deep Learning applications • build Deep Neural Networks using PyTorch...



1 - Deep Neural Networks with PyTorch 的 7 個評論(共 7 個)

創建者 Shinhoo K

Nov 17, 2019

Awesome! This course gives me the basic workflow for using machine learning technique in my research! The materials in the form of Jupyter lab really help!

創建者 Pavan D

Nov 19, 2019

very intuitive and in depth

創建者 Daniel K

Nov 20, 2019

Amazing, really informative and helps a lot !!! really liked this course and would recommend this to anyone interested in Deep learning!

創建者 Farrukh N A

Dec 09, 2019

Best course on AI

創建者 Vittorino M

Dec 09, 2019

Aprendí muchísimo. Gracias.

創建者 RuoxinLi

Dec 09, 2019

Very Clear explanation and rich labs. The quiz can be more challenging

創建者 Henrik S

Dec 10, 2019

While the subject of this course is interesting, the general quality of the course materials is sub-standard of what I am used to on Coursera. I posted a question on the forum that the staff never bothered to answer. I used to a much better quality from Coursera.