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Learner Reviews & Feedback for Introduction to Deep Learning by National Research University Higher School of Economics

4.6
stars
1,693 ratings
394 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.

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226 - 250 of 392 Reviews for Introduction to Deep Learning

By Amulya R B

Nov 5, 2017

Awesome course!

By Aleksandr G

Aug 20, 2019

Very advanced!

By Akshit V

Jul 13, 2019

Great Course!

By edward j

Feb 28, 2018

Great course!

By Ajayi E A

Jul 4, 2020

Satisfactory

By Alfonso M

Jan 31, 2019

Good course.

By Krishna H

Jun 10, 2020

Exemplary!

By Alex

Mar 1, 2018

Nice work.

By Xiao M

Dec 18, 2017

Very gooda

By Sbabti M z

Oct 27, 2020

exxellent

By Kollipaka s

May 22, 2020

very good

By M A B

Feb 25, 2019

Excellent

By 胡哲维

Dec 23, 2018

excellent

By franco p

Sep 29, 2019

Amazing!

By Parag H S

Aug 13, 2019

Amazing

By MAINDARGI Y R

Jul 16, 2020

Great

By Имангулов А Б

Jul 16, 2019

hard!

By heechan s

Sep 10, 2019

Good

By Sasikumar G

Jul 19, 2018

Good

By Колодин Е И

Aug 18, 2019

top

By Arsenie a

Apr 5, 2018

B

By Aparna S

Jan 6, 2020

The material that it is trying to cover is very good. The programming assignments are intuitive with fill in the blanks kind of approach. Finishing them and the quizzes was a breeze.

But if you are new to tensorflow and Keras and a picky like me in wanting to know exactly what is going on and how, this course is wanting details.

It does have few other minor hitches -

-It has missing links to resources (you can dig them out though)

-mistakes in slides (that they embarrassingly correct inside)

-If you care about math, it might be disappointing when you see formulae with ill-defined variables and assumptions about notations that are not discussed. If you have a background, and do simple web search you will find it out in no time though.

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By Taylor D

Jan 12, 2021

I learned a lot in this course through the implementation of the assignments. The lectures cover math and theory behind Deep Learning but it wasn't enough for me to come out of the course fully knowing the material. More study is required for the math. The assignments were for the most part enjoyable and helpful. It was exciting to see what Deep Learning could do with a few choice datasets. Just to prepare future students, as of Jan 2021, the implementations are in TensorFlow 1. So you won't be submitting the most up-to-date implementations for the course but it would be good practice to re-write the programs in TF2.0 for your own sake. Overall I enjoyed the class and am ready to apply this to my job.

By Bikhyat A

Jul 26, 2020

The course is really awesome, especially the lecturer Andrei Zimovnonv's lectures are really good. His flow, the concepts he provide, all are lucid. However, Alexander Panin's lectures are, I think quit difficult to understand. Most of the times, he suddenly delivers so fast that you can't even hear what he actually said. I think, he should work on that. And honestly, I still have lot's of confusion in the portions he covered i.e. embedding, auto-encoders, adversial networks etc. One more thing what I'd like to add is, the instructions provided in the assignment notebooks are sometime very hard to understand making me feel they're confusing and incomplete.

By Arend Z

Feb 9, 2018

Very helpful to get a good basic understanding of the different types of neural networks and their application. After finishing the course, I do not yet feel confident enough to build my own neural network applications. Maybe this can be solved by having more programming assignments at 'beginner' level, before 'stepping up' the complexity.

The provided 'example' codes - that work after successful completion - serve as a good starting point to build your own neural networks.