4.4
stars
1,313 ratings

Joseph Santarcangelo

45,159 already enrolled

Offered By

IBM Skills Network

About this Course

140,013 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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Start instantly and learn at your own schedule.

Intermediate Level

Approx. 30 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. 30 hours to complete

English

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

Try Coursera for Business

Instructor

Instructor rating

4.45/5 (281 Ratings)

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Developer Skills Network

927,625 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%(3,748 ratings)

Week1

Week 1

5 hours to complete

Tensor and Datasets

5 hours to complete

6 videos (Total 44 min)

Week2

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)

Week3

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)

Week4

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

290 reviews

5 stars
64.29%
4 stars
23.04%
3 stars
5.68%
2 stars
3.86%
1 star
3.10%

TOP REVIEWS FROM DEEP NEURAL NETWORKS WITH PYTORCH

by SWAug 4, 2022

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.

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 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.

by COMar 21, 2023

Excellent course, I have never seen so much quality learning material in short span of time, and somehow IBM managed to make it easy to understand.

View all reviews

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Deep Neural Networks with PyTorch

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