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Отзывы учащихся о курсе Introduction to Deep Learning от партнера НИУ ВШЭ

Оценки: 1,698
Рецензии: 395

О курсе

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:

Лучшие рецензии

19 сент. 2019 г.

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

28 мая 2020 г.

The hardest, yet most satisfying course I've ever taken in deep learning, by the end of the course I was doing stuff that was borderline sci-fi and that was just "introduction" to deep learning

Фильтр по:

226–250 из 393 отзывов о курсе Introduction to Deep Learning

автор: abdul s

6 нояб. 2018 г.

Very Good Course

автор: Amulya R B

5 нояб. 2017 г.

Awesome course!

автор: Aleksandr G

20 авг. 2019 г.

Very advanced!

автор: Akshit V

13 июля 2019 г.

Great Course!

автор: edward j

28 февр. 2018 г.

Great course!

автор: Ajayi E A

4 июля 2020 г.


автор: Alfonso M

31 янв. 2019 г.

Good course.

автор: Krishna H

10 июня 2020 г.


автор: Alex

1 мар. 2018 г.

Nice work.

автор: Xiao M

18 дек. 2017 г.

Very gooda

автор: Sbabti M z

27 окт. 2020 г.


автор: Kollipaka s

22 мая 2020 г.

very good

автор: M A B

25 февр. 2019 г.


автор: 胡哲维

23 дек. 2018 г.


автор: franco p

29 сент. 2019 г.


автор: Parag H S

13 авг. 2019 г.



16 июля 2020 г.


автор: Имангулов А Б

16 июля 2019 г.


автор: heechan s

10 сент. 2019 г.


автор: Sasikumar G

19 июля 2018 г.


автор: Колодин Е И

18 авг. 2019 г.


автор: Arsenie a

5 апр. 2018 г.


автор: Aparna S

6 янв. 2020 г.

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.


автор: Taylor D

12 янв. 2021 г.

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.

автор: Bikhyat A

26 июля 2020 г.

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.