Chevron Left
Introduction to Deep Learning & Neural Networks with Keras に戻る

IBM による Introduction to Deep Learning & Neural Networks with Keras の受講者のレビューおよびフィードバック



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....




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



Good course. It is a very direct approach. It is a basic introduction to keras. Doing the labs is recommended, and also previous knowledge about machine learning is encouraged.


Introduction to Deep Learning & Neural Networks with Keras: 151 - 175 / 213 レビュー

by Sima Q


V​ery Good!

by Muhammad J B


Just great!



Nice course

by Aditya M P


Good Course

by Abul B



by Sambit S


very good

by Dr C S Y



by Souvik M



by Saman S



by Ridha O


good one

by said f



by Krishna H



by Rafael G


Very good course which gives a good introduction to the field. Don't get intimidated by the math you will see and make sure you understand the workflow. Once you do that you will basically repeat it in which one of the neural network types presented at the course. In a negative not, I missed the intructor elaboring how to identity problems that could be approached by applying DL. But I complemented studies on other documents in the internet and that's ok.

by Michael M


It was a pretty good brief, rapid intro. I frankly was expecting more content on options and explanations, but it covered the very essential basics. The final exercise did ask for students to use tools not gone over in class (a bit of scikit-learn). Since I've used scikit-learn before, this wasn't hard for me, but it may be for a newcomer, and actually isn't needed to meet the goals of the assignment, so I'm not sure why it was there.

by Xiaoer H


The course contents are not in-depth enough. The server for Jupyter notebook running is way too slow. Besides, the peer review homework is not that good, because some people didn't read through the questions carefully enough, and they misunderstood the questions themselves and could not give fair enough grades to peers. If the final assignment can be made to auto-grading one, it would be much better (we can set the same random seed)

by lonnie


I​ have experience of Deep Learning, so I am able to walk through this Lession quickly. The main focus is on Capstone Project, and I have learned something on it. To be honest, this lession is very elementary. I suggest to introduce more Deep Learning models and approachs in this lesson.

by Sander v d O


This is a great course. The lectures are boiled down to the essence of neural networks using Keras. I give four stars instead of five stars, because the IBM labs environment that the course uses was quite slow and buggy, so I ended up doing the exercises in Google Colab.

by Adriano S


The Course is basic but interesting. I missed an exercise on backpropagation with the same explanatory level that it had for forward propagation. The last activity needs to be reviewed because it is confusing.

by Aaqib W S


A good course. Could be better if it was explained how to select the optimal number of layers and nodes. This was not covered and explained anywhere. Overall it was good.

by Rohit S


I took this course for understanding the TensorFlow properly. Now I am in the situation to understand all the frameworks. Thanks a lot for providing me this free course

by Benhur O


Good practical examples for ANN. It could be improved the theoretical part and compare better the architecture of the networks with the algorithms and code for Keras

by Mohamed A A


A good introductory course, well suited for beginners looking for general information about neural networks and deep learning, with good practice exercises.

by Lete N


Good intro to the subject. The instructor could have done examples using other neural networks like RNN and autoencoders. It was a fantastic intro

by Rashmin D


It is a good insight for someone to know and understand Deep learning. And exams and projects make sure students learn and practice new concepts.

by Utkarsh


In-depth concept-analysis is required. Good for people who know the theory and want to learn and revise its implementation in Python.