1 de dez de 2020
I learned so many things in this module. I learned that how to do error analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
30 de mar de 2020
It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.
por Kharuk I•
20 de jul de 2020
Instead of clearly and concisely formulating some of the ideas (like F1 formula for the metric), they are discussed as if they were hard to understand. This makes the understanding harder and is rather annoying. The assignments are useful - they make sure you understood the material the material.
por Burag C•
10 de abr de 2018
This was a good intuition course. I learned a lot and loved the content. However, I am afraid the information here needs to be repeated many times to make it a habit (as part of programming exercises). That's why I am giving it a 4-star. I feel like this could have been part of the last course.
por Kevin C•
22 de dez de 2019
Great overall. However, a major thing that is missing is the different between val set and dev set, and about the recent trend to perform K-Fold CV on the training set to get the val set. Maybe still need a separate dev set because of the different distribution if it comes a separate soure?
15 de nov de 2017
Wish there were more projects / assignments to exercise concepts taught. Like in the first 2 courses of the specialization.
Maybe even blending videos with a broader Jupyter notebook would be better. The videos are great, but paired with practical application its much more likely to stick.
por Prashant S•
2 de mar de 2018
-This course have two quizzes and no programming assignment.
-This course gives a very good advice on how we can improve Algorithm performance.
-Best way to split data into Train/dev/test.
-Quizzes statement can be made more precise and clear but stil the scenario in the quiz was good.
por Timo K•
20 de dez de 2017
Very good course, but in contrast to the other courses the practical exercises are missing. I would like to see some transfer learning and (non-)end-to-end learning approaches, where the student has to examine how bad/good end-to-end performs in contrast to a multi-step approaches.
por Samchuk D•
24 de set de 2017
The content of this course is quite unique. Thus it makes it much more interesting and important.
Thank's a lot for this tips!
However it would be nicer if there is some videos practical assignments about tech aspects of implementations of "transfer learning" and "multitask learning"
por Christopher M•
29 de jul de 2020
A very nuanced course, which I see myself coming back to, gaining more insight and appreciation for it over time. Some of the quiz questions where quite difficult to answer as they were open to interpretation. I think Prof. Ng went the extra mile in putting this material together.
por Eslam H•
20 de ago de 2018
I got the same feedback for many of my colleagues that this course is not that important and I should start with course #4 instead, but I am glad I didn't there is a lot of insights and experiences in this course that I think it would take anyone many years to conclude by himself.
por Antonio C•
14 de abr de 2020
That's a great course to learn some practicalities of deep learning/transfer learning and multitask learning, and when to use different strategies for structuring a project. In my opinion, the course could do with a hands-on programming exercise to help consolidate the learnings.
por Milan S•
1 de jun de 2018
Sometimes its become bored who has not any experienced into working on real life ML project because without facing problem you can not understand problem in better way so i recommend course instructure to make this course with little more practical way so that it easy to digest.
por Bakr K•
28 de jun de 2020
The lack of progamming assignments hurts what could have been one of the best courses of the specialization, especially in solidifying the advices and ideas seen here. Nevertheless this course still provides valuable informations, and it's one i'll come back to later for sure.
por Hans E•
18 de fev de 2018
A bit slow going and repetitive (and some simple video editing to remove double sections would improve things). Nevertheless I'm amazed how much I learned or consolidated is just a few evenings of watching these videos. Thanks again! Looking forward to course 4 in this series.
por Srinivas R•
3 de out de 2017
Thorough and practical guidelines to structure and analyze issues with machine learning projects. Distilled learning presented from a lot of project experience. It would be hard to gain such knowledge without having gone through a number of projects. Accelerates your learning.
por Anirudh R•
17 de jun de 2020
It was a very informative course. I learnt about different metrics that are used for measuring the success of deep learning models . I learnt about the different approaches like transfer learning, multi task learning etc. The assignments were very challenging and interesting.
por Rahul D•
20 de abr de 2019
Machine learning simulator assignments were great, wish we could have more of them both in this course as well as in the other courses in the specialization. Additionally, I would have loved programming assignments that reinforced these largely workflow-related concepts.
por Lester A S D C•
25 de jun de 2019
Useful knowledge regarding the efficient practices in the application of machine learning. Mentors doesn't seem as responsive though, compared to the other courses of the specialization. Quizzes were helpful, but needs more justification for some of the correct answers.
por Harshit S•
12 de nov de 2017
The course showed the experiences while dealing with machine learning projects but could have been better if the experience would have been shared through practical exercises rather than objective case study.
It would be better if there were programming exercise as well.
por Jihwan M•
15 de set de 2017
I have a feeling that this third course is not yet fully edited. I see some black screens, and sometimes the clips have Andrew speak faster than usual. Nonetheless, the various tips and appropriate actions to take when doing a machine learning project were very useful.
por akshaya r•
12 de jan de 2020
Good explanation for the initial steps of organizing the ML project and the direction to approach the problem accounted for. The quiz was interesting but as it is the same set of questions for any next attempt, I would not say I have mastered the course completely.
por Jean-Simon B•
8 de mai de 2018
Only 2 weeks, good concepts to know. But videos are not "final release" they are not well edited. Some time Andrew repeat the same sentence 2x but they forgot to cut it.
No programming assignment. Although quiz format is fun and you really learn by doing the quiz.
por Bogdan P•
3 de set de 2017
This was a slightly more theoretical course than the first 3 in the Deep Learning specialization and, even thought I enjoyed it, I think the info would stick better if there would have been a programming assignment too (or some other type fo practical application).
por Kalle H•
20 de nov de 2017
Nice and concrete examples of what to think of and focus on when trying to improve your machine learning projects. Not as engaging tasks to complete as in the previous courses in this specialisation, however a good change of scenary if you have been doing these.
por Boris V•
21 de jan de 2018
Great material, but it's not quite easy to understand it from scratch, if you didn't have such problems yourself (i.e if you have no experience in deep NN training). I've stored this material and going to revisit it after I gain more experience in training NNs.
por Fredrik K•
6 de out de 2017
Great course, however the quiz of week 2 had some ambigious phrasings and I think at least one example (the one with the data synthesis of foggy images) is contradictive of what was taught in the video lessons. Other than that, really good content and teaching!