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Quality Score

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Overall Score : 86 / 100

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Course Description

The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.The prerequisites for this course are: 1) Basic knowledge of Python.2) Basic linear algebra and probability.Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand:1) Linear regression: mean squared error, analytical solution.2) Logistic regression: model, cross-entropy loss, class probability estimation.3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions.4) The problem of overfitting.5) Regularization for linear models.Do you have technical problems? Write to us: coursera@hse.ru

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Instructor Details

Evgeny Sokolov

Evgeny Sokolov graduated from Moscow State University in 2013 with a computer science degree. Evgeny is a lead data scientist at Yandex.Zen - personal recommendations service created by Yandex, russian search giant. Evgeny is also a senior lecturer and deputy head of Big Data and Information Retrieval department at Higher School of Economics - one of Russia's top universities, where he reads courses on machine learning and helps to introduce data science courses into all B.Sc. programs.

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Reviews

4.3

136 total reviews

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By Daniel on 19-Jul-18

I have completed other ML courses at Coursera, this is one which I will NOT continue. The lectures, the assignments and the grading are all riddled with mistakes. Alone that is not a problem -- however the instructors have failed to make corrections. I am willing to push through material containing errors, however to find an error posted in the forums, a response from the instructors stating "fixing it" ... and then six months later no changes, is too much for me.

By Dmitry on 18-Jan-19

Alexander Panin has ruined this course with his pronunciationP.S. finished the course with honors

By Alexey S on 28-Dec-17

You should only take this class, if you already know 90-95% of what it of supposed to teach.In this case, you might extract something useful from it.Otherwise, it will cause a lot of frustration - the course is terrible from a learning standpoint.

By Sandeep P on 31-Jul-18

I have started taking this course after completing Andrew Ng's Deep learning Specialization. This course is very hands-on and would be a great addition to any one interested in Machine learning. The programming assignments are harder but are rewarding in asserting the skillset.

By Anna N on 7-Mar-18

Lectures provide very small amount of material. There is no sense to describe topics like gradient descent in advance course. It would be much better to take just a few topics and describe them in much more details than to speak 5 min about CNN, 10 mins about RNN, etc.Also big minus is poor English.

By on 15-Oct-18

Just a theory, no practice at all !!!

By Darya L on 14-Dec-18

In general the course is good, it gives you the idea of different neural networks, their usage and a bit of their inner math. The only thing I didn't really like: most programming assighnments contain large precoded parts, which are difficult to understand. For me it would be more useful, if assighnments wouldn't be so difficult, but I had to code myself.

By Radishevski V on 27-Nov-18

The course is good enough, but lecturer Aleksendr Panin speaks too quickly and anyway with a strong accent. Fast does not mean good

By Daniel I on 8-May-18

PROS: Interesting exercisesCONS: Very poorly explained. Poorly prepared exercises. Hard to understand if you don't already know about the matter (then... Why would you need this course?)

By Nikita F on 9-May-18

Most video lectures are useless, lecturers are just reading some text from a paper/screen, some of them have english very far away from perfect. Each programming assignment is more about struggling with bugs rather than learning something about ML. Final project is a torture for students without GPU, you spent 5 hours training the model - your final loss was too low. Can you add a test/assertion for the loss function? So far it was my worst coursera experience.

By Marco on 11-Feb-18

Simply this is not a course as it's not teaching anything, but just presenting things you need to study in order to complete the exercises.I'm on the second week and the slides and videos are simply a nightmare, can't find a better word.The speaker is not able to speak a fluent English and you can't really build a logic sentence even from the subtitles. You get some intuition from the slides and from some comments in the forums.I'm not struggling with the actual topics but to translate them into Tensorflow code, which is not a pre-requisite for the course.I'm wasting a lot of time trying to understand how to do the silliest things, just because there isn't any introduction to it. I have painfully lost hours trying to understand how to do a reshape of a placeholder with not known sizes, but you are supposed to finish the exercises in 1h, while this is barely impossible if you do not know Tensorflow in advance.It looks like that you need to know both machine learning topics and Tensorflow, which means that you don't really need this course then.So I'm not sure what is this course about as it is not really teaching anything, topics are just presented and then you need to do your own research. It looks like a book page table: you know what the course is talking about, but you don't get any real explanation to that.I know that the course is considered "advanced", but it does not help you in solving the exercises at all as you need to learn elsewhere how to do it.The time you spend for the exercises is massive but generally it is not correlated with the difficulty of the topic, but more in the way the exercises are presented.It's a bit of shame as the teachers look very competent, but it really looks as if they haven't put a good effort in this course.

By Oleg O on 1-Dec-18

Useful course, whereas it is not always clear how to complete homeassignments