4.4
stars
1,292 ratings

Joseph Santarcangelo

42,859 already enrolled

Offered By

IBM Skills Network

About this Course

136,596 recent views

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

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Intermediate Level

Approx. 31 hours to complete

English

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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

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.44/5 (272 Ratings)

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Developer Skills Network

885,133 Learners

26 Courses

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.

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Syllabus - What you will learn from this course

Content Rating89%(3,613 ratings)

Week

Week 1

5 hours to complete

Tensor and Datasets

5 hours to complete

6 videos (Total 44 min)

Week

Week 2

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)

Week

Week 3

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)

Week

Week 4

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

4.4

284 reviews

5 stars
64.34%
4 stars
22.96%
3 stars
5.72%
2 stars
3.94%
1 star
3.01%

TOP REVIEWS FROM DEEP NEURAL NETWORKS WITH PYTORCH

by MTOct 16, 2022

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

by SSJul 26, 2020

Wonderful course!!! Best among all the courses under AI Engineer Certificate by IBM. Deep learning always haunted me with the maths involved but now I get a very good start with this.

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 GCAug 3, 2020

Great introduction to deep learning with pytorch. It would help if the notebooks in the labs take shorter to run so that the students can experiment with the code and the models.

View all reviews

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Deep Neural Networks with PyTorch

About this Course