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
IBM Skills NetworkAbout 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 Skills Network
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
- 5 stars64.15%
- 4 stars23.05%
- 3 stars5.74%
- 2 stars4.01%
- 1 star3.03%
TOP REVIEWS FROM DEEP NEURAL NETWORKS WITH PYTORCH
by BPJan 18, 2021
Good pacing, great examples and the assignments are doable within the time allocated for them. Combines both technical information and applied code.
by MEMar 29, 2020
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.
by JPMay 9, 2020
Good introduction of PyTorch. There are some minor code errors and inconsistencies in the material but generally not difficult to figure it out.
by ANMar 6, 2020
It was a very informative and interesting lecture. I learn a lot about the details when using PyTorch to build and train a deep neural network. I am so thankful.
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