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.
Offered By
IBMAbout this Course
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Intermediate Level
Approx. 31 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 31 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Offered by
IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Syllabus - What you will learn from this course
5 hours to complete
Tensor and Datasets
5 hours to complete
6 videos (Total 44 min)
2 hours to complete
Linear Regression
2 hours to complete
7 videos (Total 35 min)
3 hours to complete
Linear Regression PyTorch Way
3 hours to complete
5 videos (Total 21 min)
2 hours to complete
Multiple Input Output Linear Regression
2 hours to complete
4 videos (Total 18 min)
2 hours to complete
Logistic Regression for Classification
2 hours to complete
4 videos (Total 31 min)
2 hours to complete
Softmax Rergresstion
2 hours to complete
3 videos (Total 18 min)
3 hours to complete
Shallow Neural Networks
3 hours to complete
6 videos (Total 33 min)
Reviews
TOP REVIEWS FROM DEEP NEURAL NETWORKS WITH PYTORCH
by RDSep 29, 2021
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.
by SYApr 29, 2020
An extremely good course for anyone starting to build deep learning models. I am very satisfied at the end of this course as i was able to code models easily using pytorch. Definitely recomended!!
by RAMay 15, 2020
This is not a bad course at all. One feedback, however, is making the quizzes longer, and adding difficult questions especially concept-based one in the quiz will be more rewarding and valuable.
by KDJul 20, 2020
The right level of detail so that you can dive in. I wish there had been a week to cover RNNs as well though, in particular the best way to handle variable length sequences for RNNs :)
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