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Learner Reviews & Feedback for Introduction to Deep Learning & Neural Networks with Keras by IBM

4.7

stars

1,773 ratings

About the Course

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....

Top reviews

Jun 30, 2020

Such a wonderful and high tech course in the world and it is provided by ibm and coursera.Thank you ibm and coursera for such a opportunity.I'm glad and proud to be a part of this organization.

Jul 1, 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.

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251 - 275 of 343 Reviews for Introduction to Deep Learning & Neural Networks with Keras

By Victor M C

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Jul 26, 2024

buen curso

By Lateefat O B

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Apr 7, 2024

wonderful

By Abul B

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Mar 10, 2022

Excellent

By Sambit S

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Sep 1, 2021

very good

By Dr C S Y

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Aug 22, 2021

Excellent

By Souvik M

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Apr 21, 2020

Excellent

By Saman S

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Sep 25, 2019

wonderful

By Mohamed

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Feb 11, 2025

its good

By Ridha O

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Feb 11, 2022

good one

By SIMHA C J

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Aug 26, 2024

Awesome

By José M

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Mar 27, 2023

Good!!

By parisa z

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Nov 9, 2022

great

By Francisco M L L

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Aug 8, 2022

great

By said f

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Mar 29, 2020

super

By Nurzat D

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Apr 19, 2025

Good

By Nithya P V 2

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Mar 28, 2025

dfdf

By Ahmed E

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Aug 15, 2024

good

By Sardor B

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May 22, 2024

good

By Astitva S

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Mar 18, 2024

good

By 01fe21bec413

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Mar 16, 2024

Good

By mezmur w

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Mar 6, 2024

best

By afra a a

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Dec 22, 2023

good

By Muhammad M T

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Mar 22, 2023

good

By Krishna H

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Apr 29, 2020

good

By Gorana B

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Jul 22, 2024

It is short and comprehensive introduction. It could have had a dedicated module on evaluation of the models, with visualizations of target vs predictions and losses. From evaluation of peer-graded assignments I get the impression this is not well understood (ways to do it, meaning of values vs training and epochs). On the other hand peer graded assignment should be more challenging than what is shown throughout the course. So maybe it is enough what was shown throughput the course, as current assignment is a bit more challenging. Otherwise students end up copy pasting materials (which I have seen too often). My problem is more on the concept of evaluation of the assignment and points to be given. Scale is too coarse. And submission request should be less loose - jupyter notebook or python files, not html or pdf files. And some system that is automatically checking for similarities among student's assignments prior to submission would be good to have.