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Back to Introduction to Deep Learning & Neural Networks with Keras

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1,247 ratings

Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
After completing this course, learners will be able to:
• Describe what a neural network is, what a deep learning model is, and the difference between them.
• Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.
• Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.
• Build deep learning models and networks using the Keras library....

MP

Jun 30, 2022

Excellent introduction to the mechanics of Neural Networks in general, and the Keras application specifically. Alec is an outstanding teacher, I always appreciate his knowledge and enthusiasm.

AB

Mar 15, 2020

Interesting course. Forward propagation, gradient descent, backward propagation, the vanishing gradient problem, (+ Regression, Classification, and CNN with Keras) explained clearly.

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By deepthi l

•Oct 9, 2019

Loved the way the instructor has clearly set the expectations of the scope and breadth of coverage of topics in this course. However, if the course was a bit more deeper into intermediate level concepts, it would have given me more confidence to face interviews. Nevertheless, I would love to learn more from this instructor as he kept me motivated (and not bored) all through the course from start to the end.

By Aniket S

•Dec 16, 2019

Queries are not getting resolved in the discussion forum. So, instructors should participate in the discussion forum to resolve such queries.

By ANGEL G C

•Apr 19, 2020

The course is OK in the overall. But it has two main drawbacks from my point of view:

1) The final assignments asks for solving the task making use of routines NOT shown along the course: i.e. it's shown in the labs to solve issues in a way, and in the assignment it has to be done in a different one.

2) It was IMPOSSIBLE for me to get feedback from the Staff at any question. I ended up solving the issues by my own and through try and error, which is fine from a self-study approach, but when it comes to technical issues, it can get quite complicated...

By Lam C V D

•Sep 8, 2019

The teaching is not deep enough to solve the Week 5 assignment, please take note you need to plumb in other Keras courses.

By Amit T

•Sep 28, 2019

Details very well covered. I found it very much interesting and well explained.

By Suhas S

•Sep 19, 2019

It is a good Introduction course on Deep Learning using Keras.

By Jered W

•Dec 16, 2019

The final assignment needs to be more user friendly.

By Bhrigu C

•Nov 1, 2020

The course does not cover using following concepts with keras - Dropouts, maxpooling, CNN, RNN, padding.

They have been covered in the pytorch course using PyTorch, which is entirely different from how we would use with Keras. It makes this course highly incomplete in terms of examples and assessments and I don't think I have learnt much here. There are way better free courses on youtube by regular data scientists (not from IBM) that include detailed concepts and examples on these left-out important content.

By Serge F

•Nov 16, 2020

The title is not right. It is more a general presentation of deep learning then a présentation with KERAS.

By Sadabrata K

•Apr 6, 2020

Lab assignments are really good as they help in building the concepts nicely. IBM has really put together the best instructors. The videos were also very easy to understand. One more thing I would like to add is that previously I have tried learning Deep Learning from other places too but this course is the best.

By Amitayu B

•Mar 16, 2020

Interesting course. Forward propagation, gradient descent, backward propagation, the vanishing gradient problem, (+ Regression, Classification, and CNN with Keras) explained clearly.

By Petr J

•Jan 20, 2020

Quite good course on Nerual Networks. I would only welcome even more practical examples with practice coding but I was happy with this setup.

By Chuck H

•Dec 10, 2019

Really enjoyed the class, felt that the level of challenge was appropriate. Thanks for making this class available!

By Andres H

•Dec 2, 2019

Excellent i understood the main principles of neural networks and the recomendations were very usefull

By GAUTAM K

•Oct 3, 2019

Very intractive and benificial course for me .Thank you coursera and IBM for this course

By Rose C

•Nov 10, 2019

I really enjoyed this course, specially for all the labs and assignments. Thanks much!

By Denis U

•Jan 16, 2020

Very good course to start with Neural Networks & Keral lib. Recommend for beginners.

By Pokala A

•Nov 6, 2019

It,s a very good course to get grip on keras and artificial neural networks.

By Ramiro R Á

•Nov 17, 2019

Nicely packed in a small course. And the examples are pretty good as well

By Laeeq K

•Sep 26, 2019

Super Class course ever I see. Thanks for give such best production.

By Saeed A

•Oct 16, 2019

Nice for start point

By Alex S

•May 11, 2020

Good course for absolute beginners. Would have liked an extra week or two to 'manually build' some of the key neural network concepts from scratch as in the first week.

By Sameer u

•Mar 11, 2020

try to add more case study problems and solve it on lectures so that we can understand how to start (initialize) the coding part when we receive any real world problem.

By Marc A T

•Oct 23, 2022

I really like this course as it has only provided me with the tools that I need to understand neural networks without overhelming myself with abstract mathematics or theories. It is indeed good to be equipped with foundational theories and mathematics, but they do not build much intuition without direct practice. I believe that theories can be learned along the process rather than being stagnant because you cannot grasp them initially. With this course, I have become proactive because this is more concept-based and the mathematics has a good visualization to it. I like the way it is designed and how the final requirement aids you while allowing flexibility.

This course is highly recommended to those who have a background in Python programming, and interested in the basics of neural networks without the exhaustive mathematics.

By Frank S

•Nov 22, 2021

This was a good course and well paced. But I have a comment about the final project. It was not clear to me if the same model was being re-trained and improved upon in 50 trials - or if the idea was to do 50 trials to capture just how good a model it is, using different splits of the data. Since we were asked to find the mean and sigma of 50 trials - I assumed those trials would be independent and each one performed with a fresh model - otherwise it is odd to find a mean and sigma of a system that is converging to a final result. In that case it's only the final mean and sigma of the final trial that matters. So I think some clarity would help there - without giving it all away with regard to how to do the project.

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