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.
About this Course
Syllabus - What you will learn from this course
Tensor and Datasets
Linear Regression PyTorch Way
Multiple Input Output Linear Regression
Logistic Regression for Classification
Shallow Neural Networks
- 5 stars64.30%
- 4 stars23.03%
- 3 stars5.72%
- 2 stars3.89%
- 1 star3.05%
TOP REVIEWS FROM DEEP NEURAL NETWORKS WITH PYTORCH
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.
Very intensive course. Could do more training labs. But this is definitely a very dense course. Extremely helpful to get started on ML/Deep Learning.
Not only did I gain the basic knowledge of deep learning, but also learned Pytorch. It is a good course, however, there is still a lot more to go in the area of Deep learning,
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Certificate?
More questions? Visit the Learner Help Center.