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

4.7

stars

1,986 ratings

About the Course

This course introduces deep learning and neural networks with the Keras library. In this course, you’ll be equipped with foundational knowledge and practical skills to build and evaluate deep learning models. You’ll begin this course by gaining foundational knowledge of neural networks, including forward and backpropagation, gradient descent, and activation functions. You will explore the challenges of deep network training, such as the vanishing gradient problem, and learn how to overcome them using techniques like careful activation function selection. The hands-on labs in this course allow you to build regression and classification models, dive into advanced architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and autoencoders, and utilize pretrained models for enhanced performance. The course culminates in a final project where you’ll apply what you’ve learned to create a model that classifies images and generates captions. By the end of the course, you’ll be able to design, implement, and evaluate a variety of deep learning models and be prepared to take your next steps in the field of machine learning....

Top reviews

Mar 27, 2025

Really well explained. For some lectures you might need to refer outside the course, but mostly well understandable for an intermediate level student.

Jul 10, 2024

The course is quite complex for a person who does not have knowledge of algebra, statistics and calculus, the final project was good because it was challenging.

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276 - 300 of 381 Reviews for Introduction to Deep Learning & Neural Networks with Keras

By Ridha O

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Feb 11, 2022

good one

By Simha C

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Aug 26, 2024

Awesome

By SURYA B

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May 7, 2025

useful

By José M

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Mar 27, 2023

Good!!

By Dastur K

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Jul 12, 2025

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 Jatin 0

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Aug 31, 2025

good

By Rakshita

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Aug 30, 2025

good

By Rasim A

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Apr 29, 2025

Good

By prakhar

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Apr 25, 2025

good

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 21, 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 Khánh N

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May 24, 2025

oke

By harkirat s s

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Sep 7, 2025

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.