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.
AR
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.
FN
Really well explained. For some lectures you might need to refer outside the course, but mostly well understandable for an intermediate level student.
AJ
I had some information about the subject, but the content on the course was very good in terms of filling some voids in my knowledge Thanks for the great content
A
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.
SU
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.
AJ
Great material as an introduction to the topic. Perhaps, a bit more about model testing / validation could have just made the course more complete.
OS
A great introduceintroductory course to deep learningIt teaches deep learning concepts using practical labs and keras which makes the concept very clear.which
BJ
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
PM
Very Clear and Precise knowledge which started from the grass-root level to help newbies come up to the level of understanding deep learning models and algorithms.Thumbs up!!
NW
Was a great course which gave a good understanding of deep learning and the labs were very useful in getting to understand the subject and using Keras from a practical point of view.
AM
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.
AB
Interesting course. Forward propagation, gradient descent, backward propagation, the vanishing gradient problem, (+ Regression, Classification, and CNN with Keras) explained clearly.
Showing: 20 of 404
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...
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.
Queries are not getting resolved in the discussion forum. So, instructors should participate in the discussion forum to resolve such queries.
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.
The title is not right. It is more a general presentation of deep learning then a présentation with KERAS.
The video and Jupyter notebooks were both concise and of excellent quality. However, the versions of dependent libraries are somewhat outdated, which makes it quite challenging to run locally.
The teaching is not deep enough to solve the Week 5 assignment, please take note you need to plumb in other Keras courses.
The final assignment needs to be more user friendly.
Details very well covered. I found it very much interesting and well explained.
It is a good Introduction course on Deep Learning using Keras.
Amazing material but very outdated. I was able to figure things out on my own but those not as good at debugging may run into very difficult to over barriers. That said the course is fantastic. If it gets an update I'll raise the score.
Many of the small doubts are not solved and its hard to understand from lab .
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.
Interesting course. Forward propagation, gradient descent, backward propagation, the vanishing gradient problem, (+ Regression, Classification, and CNN with Keras) explained clearly.
Quite good course on Nerual Networks. I would only welcome even more practical examples with practice coding but I was happy with this setup.
Really enjoyed the class, felt that the level of challenge was appropriate. Thanks for making this class available!
Excellent i understood the main principles of neural networks and the recomendations were very usefull
Very intractive and benificial course for me .Thank you coursera and IBM for this course
I really enjoyed this course, specially for all the labs and assignments. Thanks much!
Very good course to start with Neural Networks & Keral lib. Recommend for beginners.