Chevron Left
Back to Introduction to Deep Learning

Learner Reviews & Feedback for Introduction to Deep Learning by HSE University

4.6
stars
1,718 ratings
400 reviews

About the Course

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...

Top reviews

DK
Sep 19, 2019

one of the excellent courses in deep learning. As stated its advanced and enjoyed a lot in solving the assignments. looking forward for more such courses especially in Natural language processing

TP
Aug 8, 2020

A very good course and it is truly insightful. This course deals with more on the concepts therefore I have a better understanding of what is really happening when I build deep learning models.

Filter by:

326 - 350 of 398 Reviews for Introduction to Deep Learning

By Massimo T

May 17, 2020

Good teaching, the exercise preparation could be more accurate.

By Mauro D S

Jul 28, 2018

Good intro to deep learning (RNN's well explained! Good job.)

By Saraansh T

Jul 12, 2020

A job well done. Quite responsive community and support.

By Seongeun S

Jul 27, 2018

Great course to get a first view of deep learning !

By SABYASACHI B

May 24, 2019

some lectures can be given at a slower pace

By Eric V

May 26, 2018

Great course with challenging assignments.

By Ting Y

Apr 13, 2018

Comprehensive intro of deep learning

By Siddharth P

Jan 25, 2020

Tensorflow 2 would have been great

By hhwaiting

Aug 23, 2018

课程有一定难度,需要有比较好的先验知识,但课程作业设置非常锻炼人

By Olayinka E O

Dec 9, 2020

Lots of Interesting exercises.

By Tadas Š

Oct 15, 2019

Quite good - not too basic.

By Abhishek S

May 18, 2020

Challenging and helpful !

By Chi E

Jan 12, 2019

I love the material!!

By Heider D L

Aug 3, 2020

Really hard lol

By Vinícius L

Jul 15, 2020

Very good.

By Robert K

May 21, 2018

I've dived into this course only AFTER completing Andrew Ng's specialization "deep learning". In that sense this was a nice "revision" with additional set of exercises. Some of the topics introduced were nice exercise in ultimately "testing" your knowledge from other sources. Having said this, you really need previous exposure to machine learning, and I'd also say - deep learning.

But it doesn't give much beyond this point. Lecturers vary in terms of knowledge, or rather the ability to clearly present it. Coursera serves might not be enough for most exercises, and it pushes you to set-up your own machine (if you have a proper one) or configure one on the cloud. With many services it is rather easy now.

Overall, I recommend it as a review, an introduction AFTER some exposure. Some additional material might be new to you, but no necessarily if you followed other courses. I am more eager to look into further courses in the specialization.

By Angela W

Jul 7, 2020

The topics are great, but many of the speakers have heavy accents and the written transcript is often nonsensical. All in all, I did not enjoy watching the lecture videos and found the course pretty tedious. What is very cool is that the programming assignments can be done on google colab (with instructions) where you can use GPUs for free.

In summary, I feel that I did learn a lot and the idea of the course is great, but faced with the prospect of having to watch more of these videos to complete the specialization, I cancelled my subscription after finishing this course instead.

By Ramin A

Jan 21, 2019

Overall I enjoyed the course, but it lacks structure. Some materials are assumed to be well known by the learner and surprisingly some easier ones are not. I like to see the math, but it needs more materials to support it. Most instructor's have very heavy accent and tend to speak too quickly, I find myself rewinding multiple times just to figure out what was being said. Homework's are not too difficult, and are enjoyable. Except for the last one where you need to wait for a peer review. I think this can be a flagship course with more efforts.

By Hermon A

Aug 12, 2019

The explanation of TensorFlow is not enough and the programming homeworks have already a lot of already written (because, i would be very difficult to programming the all of the homework by ourselves in this stage of learning). I think it is better programming homeworks with examples more easy, but with more programming by ourselves.

At least, I think it is already well enough for the final evaluation, the automatic correction and then, the correction by peer only delay the evaluation.

By RJ C

Jun 26, 2018

I could not understand what the lecturer in the second week was saying. Overall good content but awful presentation. Exercises are ridiculous, my code is working fine, but since I do not use the same function as teachers and I do not get the same result to 0.00001, I cannot pass the class. Definitely will not be renewing this class. Think twice before signing up..I am sure the guys that made the class are really smart, and the content is high quality, but overall I am disappointed.

By Juan C E

Feb 27, 2018

The quality of some of the video session is not good, especially for RNN's. Very general, badly explained and little practical information for the practical assignments. Yor have to "learn" the material, not just look for additional information, from other sources.

The pratical assignments are note always well designed, and some are full of flaws. After many many hours of dealing with some of them, you get the impression that you've passed the assignment but not learned much.

By Carlos V

Oct 6, 2018

The Course is good, probably should be called introduction to advance deep learning, the complexity of the assignments make you put lots of efforts around them, that is rewarding at the end, make sure you have plenty of time to dedicate to this Course, one thing the Course could improve on is to try to minimize the switch between libraries and the low-level coding with high-level coding between TF and Keras sometimes it creates confusion.

By Zhaoqing X

Jul 20, 2018

Well, I think it's a good course for introducing us to Deep Learning and it has better(tougher) assignments than Andrew's. It also covers more knowledge than Andrew's. But the quality of the course is not that good. The Russian accent is not important because my native language is not English as well, but the assignments are frustrating. The mentors cannot answer the questions that widely appears in the course.

By Raffael S

May 22, 2020

Sadly, this course leaves out a rigorous introduction to all the math behind Deep Learning. Also, it would have been nice to give an assignment to implement a conv net from scratch even if it is just a small one. Additionally the coding tasks felt like you only had to fill in coding related problems, like splitting text and setting up one-hot encodings. All the neural network fun was already prefilled.

By Dmitry Z

May 11, 2020

I'd prefer a way more detailed explanation of different architectures and alghoritms + a more detailed explanation of Keras and TF. Programming assignments are more like quizzes where you don't write the code yourself, but rather fill in gaps, because during the course you don't study how to create a fully functional system from scratch.