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

4.5
звезд
Оценки: 1,818
Рецензии: 426

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: coursera@hse.ru...

Лучшие рецензии

DK
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

TP
8 авг. 2020 г.

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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101–125 из 424 отзывов о курсе Introduction to Deep Learning

автор: saket g

17 июня 2018 г.

Challenging and motivating, it is not self sufficient but its ok to see some resources on Internet.Always excited to study this.Thanks to all teachers...!!

автор: Zhanpeng H

5 янв. 2018 г.

This is the best course that I have taken so far about deep learning on Coursera. It contains nice explanations about different types of neural networks.

автор: Hussein N

3 нояб. 2019 г.

I really enjoyed this course and how practical it is. It was super exciting to make the a practical application with transfer learning only after 4 weeks

автор: ashesh g m

8 мая 2019 г.

Its much more informative than the title suggests. A good course to take for someone who already knows basics/theoretical knowledge of machine learning.

автор: Pun C S

18 окт. 2019 г.

Quite In depth introduction on Deep learning. But you need to have a solid background on python and machine learning in order to catch up the materials

автор: Cristhian J P S

28 июля 2019 г.

It's really helpfull and I've learn differents architectures of deep learnning. I'm going to continue with other course to practices these courses.

автор: Гридасов И И

7 февр. 2019 г.

The best course that I've ever seen. It gives wide and deep understanding of whatever in deep learning. I strongly recommended this course to you.

автор: LOKESH J

21 мая 2020 г.

Excellent teachers but at time the pronounciation wasn't clear. Could be augmented by documentation. May be it is already there but didn't see it

автор: Ryan O

9 сент. 2020 г.

This is likely the best deep learning material I've interacted with.

THE OFFLINE MODE SHOULD BE MANDATORY. DON'T TRY TO DO THIS ON THE PLATFORM

автор: Harish K B

22 июня 2020 г.

Great Course blended with real time problem solving. Loved the time with Coursera Notebook even-though its a bit hard. Thanks to all trainers.

автор: Debabrata A K S

19 февр. 2020 г.

It was tough and challenging but achievable. Great contents and learning materials. Instructor are good, videos are well paced too. Thanks

автор: Eugene I

29 авг. 2018 г.

Thank you guys for replacing some lectures. As for me, at present, the course is one of the best courses in this specialization.

автор: Amit K

25 мая 2020 г.

This course teaches you introduction to deep learning which other courses consider as advanced deep learning. Very Very Useful.

автор: Ahmed N

23 апр. 2018 г.

It is great and rich contents i studied machine learning a lot and this one is very useful and beneficial to me thanks a lot.

автор: Mateusz D

2 окт. 2020 г.

Very good Course. Might be hard at some point, but with a little bit of research and dedication, you can get it done easily.

автор: Eric

5 февр. 2019 г.

An advanced class for overview for deep learning. A very wide range of the usages will let you think what you have learnt.

автор: SAHIR S

8 мар. 2018 г.

An really good introduction to Deep Learning. I think that this course is for students already familar with eep Learning

автор: stephane d

18 мая 2021 г.

Great course but I highly recommend taking basic Machine Learning courses before starting this advanced specialization!

автор: Mulang' O

15 янв. 2019 г.

The course compels you to work on the solutions and hence expose you to hand-on that are very vital for understanding

автор: Raman

25 июня 2018 г.

Great course, very deep understanding of deep learning, things I had no idea of and things I always needed are there.

автор: Adithya J

7 сент. 2021 г.

Really great course to get to learn and gat a good grasp on the basics of Deep learning!! Well taught and explained.

автор: Ishwar N

29 июня 2020 г.

Excellent course with lot of Maths required for deep learning and also covering advanced topics. Highly recommended.

автор: Tay J

23 сент. 2021 г.

It was pretty good for me. I find almost all information useful. The only thing I didn't like was using Tensorflow.

автор: Abdurrezzak E

7 дек. 2020 г.

An amazing introduction to the theory and practice at the same time. Thanks to great lecturers of HSE and Coursera!

автор: Francesco R

31 авг. 2020 г.

Stellar course with super-interesting lectures and great exercises! I have especially enjoyed the Honor material :)