Back to Introduction to Deep Learning

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1,806 ratings

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421 reviews

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...

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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By Fatvvs F

•Jul 27, 2019

I think it's the best intro to DL, especially thank you a lot for mathematical explanation of marix/tensor derivatives. Also implementation NN with numpy and gradient descend improvement are my fave tasks.

By dinesh k

•Sep 20, 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

By Rahul K

•Mar 1, 2019

Really Great course. I would recommend everyone to take this course but after having some "basic knowledge" of Machine Learning, Deep Learning, CNN, RNN and programming in python.

By Erik G

•Apr 13, 2019

This course gives a great overview of what can be done with DNNs. Topics are well chosen, clearly presented, and a good level of difficulty.

By NIKHIL M

•May 22, 2021

nice presentation of concepts, just need of upgradation of scripts to tensorflow 2. Brilliant teachers

By Veronika C

•Dec 15, 2020

Отличный курс. Знать бы еще как с него выйти, чтоб продолжить что-то другое проходить.

By Oleg O

•Dec 1, 2018

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

By AJIT R

•Feb 28, 2019

This thing is AMAZING. This thing iS no "INTRODUCTION" - IT IS "ADVANCED".

By Andres V

•Nov 22, 2018

nice really hard course.

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 Rusty B

•Apr 3, 2021

In 2021 this course might come across as a bit dated - it's using tensorflow 1 and a fairly out-of-date version of even that. This is something of a probem especially if you want to set up your computer for the course to use a GPU, since that would require a very out of date version of CUDA etc.

The actual material of the course is great, and my theory is that even though the tensorflow version is out of date, updating to tensorsflow 2 yourself is not that big of an ask once you understand the math and methodology presented.

By Juho H

•Jun 25, 2020

Very challenging assignments, and unfortunately using the old version of Tensorflow. On the other hand, you really get an understanding on many things other courses skip (like the different optimizer algorithms), and the labs are very interesting. But you really need to have already fairly much experience in machine learning before tackling this one!

By Abhishek S

•Jun 12, 2018

It's good overall.But if you are a beginner ,this course might be very advanced for you.

By Федоров Ф

•Nov 18, 2018

i think that the explanations and examples in the notebooks was not always sufficient

By Igor B

•Feb 8, 2019

The course indeed gives an introduction to deep learning, but the practical part is discouraging since the "deep learning" part of practical assignments is usually given rather than asked to develop individually.

By Anupkumar M

•Oct 11, 2018

Too much mathematical

Some of the instructors were tough to follow

By Miten S

•Aug 24, 2020

You should update it to TF2

By ABHAS B

•Apr 28, 2020

The content is good, but seems they forgot to upgrade the code material to TF2. It's a pain to work when you are working to learn TF2 outside of this code, come back to have to use TF1. Such a waste of time.

By Danilo L

•Oct 28, 2020

I cant waste my time fixing bugs of non updated code, the videos content are great but it is not worth spending time with this kind of assignments.

By Jeremy C

•Feb 4, 2020

You either need to understand Deep Learning, in which case the explanations are very bad; or you already know Deep Learning a bit; in which case the course doesn't bring anything.

Generally, the instructors are hard to understand, it goes from 0 to 100 in a second.

They also speak with a strong accent which doesn't help the understanding.

If you want to complete the Specialization, maybe follow it and accept to lose your time and money.

Otherwise, skip this and focus on better courses

By S B A K 1

•May 17, 2021

i want to quit this shittiy course

By Ivan M

•Oct 14, 2019

Frankly speaking, I want to set 3 or 2 stars because of:

1) Many non-documented issues which I have writing code for assignments.

2) Material have been made in many foreign languages, but Russian is forgotten and it dissapointed me (hello from Moscow!)

3) Some task descriptions are not actual.

4) Coursera notebooks version is not equals github version (I checked it in GAN).

But hard to learn easy in battle, right? I learn more when try to pass Numpy NN, GAN, LSTM and etc in spite of material, English (I realise that it is very difficult task to fit a lot of material in 5/10/15 minutes. Alexander Panin tried to do it=)). It was very hard, but I did it. I realised that the authors of the course have many important projects and haven't enough time for the courses. So, I want to wish them success in their job and continue work on this course. I believe that they can do better. That's why I set 5 starts. Good luck!

By Siddhartha D

•Mar 31, 2018

Course contents are good. Assignments are hard but you get to understand the intricacies of the workings of different types of neural networks and its really fun to do. There are few cases where the assignments require a GPU to work efficiently. I think Coursera should sign up with GPU cloud vendors for deep learning courses. Although peer grading process can be really helpful, I absolutely dislike it. In one instance, my assignment was marked as not runnable by one of the peers while other peers have marked it as runnable and had awarded scores for the other sections of the assignment. But, I had to resubmit the assignment in order to get the full score and it took a while for it to get reviewed as not many peers are available. In some cases, one might have to switch the session. Overall, I highly recommend the course as there's quite a lot to learn from it. Thanks.

By Ayush T

•Jul 26, 2018

I think this is the best course on Deep Learning on Coursera. It has flavours of various domains of Deep Learning like CNN, RNN, AutoEncoders and GAN's. The pace of this course is moderate which makes it easy to follow if you know a couple of thing about Deep Learning in advance. Definitely, this course is not for those people who are at a very initial stage or with no knowledge of learning Machine Learning. The assignments which are part of the assignment are really well chosen. This course makes you explore the subject on your own. But still the domain of Deep Learning is huge so there is still a lot to learn.

By khirod

•Apr 16, 2020

It was definitely challenging but at the end it really boosted my confidence as I completed all the lectures and their respective quizzes and assignments. The discussion forums could be more active and helpful I felt.

Finally I want to thank the instructors for designing this course and for all the lectures. There were few videos though which eluded my senses and I had to refer to some online videos to understand the concepts. But if someone has worked previously in similar space, I am sure they won't face any difficulty.

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