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Comentários e feedback de alunos de Introduction to Deep Learning da instituição Universidade HSE

4.6
estrelas
1,713 classificações
399 avaliações

Sobre o curso

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...

Melhores avaliações

DK
19 de Set de 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

AM
28 de Mai de 2020

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

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251 — 275 de 398 Avaliações para o Introduction to Deep Learning

por Aparna S

6 de Jan de 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.

-

por Taylor D

12 de Jan de 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.

por Bikhyat A

26 de Jul de 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.

por Arend Z

9 de Fev de 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.

por Anselmo F

22 de Mar de 2020

Very interesting course, the notebooks are very useful and all the concepts are very well motivated and explained. I just found some bugs in the course and had some problems with the explanations of week 4 and I believe week 5 lacked the explanation of some basic concepts, but all of these gaps could be filled with a research of additional material. Anyway, I recommend this even for beginners, all you need to know are derivatives and some Python basics.

por Abhinav S

22 de Abr de 2018

It is not an easy course, but the course projects are very nice. I really liked the RNN and CNN parts of this course very well explained and had some rigour to it.

My only complaint about the course is that it is not self contained. You will have to read up a lot more and refer to other sources on the internet to get a firm grasp of what is being taught and then go ahead to tackle the exercises.

por Jay U

26 de Jun de 2018

+ Instructors go into considerable theoretical depth and are very knowledgeable. + Great assignments, but can be pretty challenging+ You will learning a lot by taking this course.-Some instructors are much better than others- Instructors rely too much on slide reading. Lectures lack interactivity other than an occasional pop question.- Discussion groups are not active. Many posts go unanswered

por Zhen Y

31 de Jan de 2018

I found the first assignment (Week2) very difficult if you didn't have enough experience in Tensorflow to start with. Later on, the assignments became more enjoyable.

The course is more advanced than Machine Learning and DeepLearning.AI. Lots of concepts are gone through very quickly. It is not ideal if you are new to the subject. However, it covers great details in a short course.

por Saptashwa B

20 de Jan de 2020

Very nice course with a great project in the end. I just think this course is little too big (7 weeks) and still at times fail to cover important points in detail. I assume they are covered in the next courses of the specialization. Specially convolutional neural network for image classification requires better explanations at some part. Just my opinion though !

por Juho H

25 de Jun de 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!

por Ipsita S

17 de Fev de 2020

As I'm familiar with deep learning I took a advanced course in order to learn new things and enhance what I already know. I have given a four star because I didn't find things new for me but I continued because the course is well structured and the assignments actually were helpful for practical learning.

Overall a good experience for me!

por Emanuel P F

9 de Jan de 2019

It is not a introductory course! The course provides an excellent path showing the most tools in deep learning techniques but you have to spend more time looking for additional material to supplementary this course. In general you will learn the basic about Neural Networks, Convolutional Neural Networks, and Natural Language Processing.

por Alexey Z

19 de Mar de 2020

Autoencoders, RNN: Theory ovekill, which seems to be pretty useless, as after listening and trying to follow the lectures logic, you need to go outside to read explanations. E.g., after lectures I had 0 understanding of how LSTM is implemented, how it really works, even how actually it helps avoding gradient expls/vanishing.

por Γεώργιος Κ

13 de Jan de 2020

This was absolutely an interesting and enlightening course. There are things left unexplained and appear from nowhere in the programming assignment like RMSprop. Though the assignments can be passed even with these dark spots I think this is a reason that this is not a five-star course. In fact, I would rate it as 4.5 stars.

por Driaan J

29 de Abr de 2019

The content of the course is really excellent, and the lecturers' knowledge is just superb.

The only drawback of the course is that the lecturers' native language is not English, and accordingly it is sometimes difficult to understand them. But there are subtext to the lectures in English that one can refer to.

por GOUTAM K

28 de Mai de 2020

Lectures were short and to understand the topic, we need to browse those topics online. Programming assignments were tough and interesting but mostly pre-coded. But still the code quality was good and reading the code was interesting. Overall a good course but not much recommendable for a beginner.

por Yaran J

6 de Jan de 2019

Good overview of deep learning topics like CNN and RNN, and also hands on coding assignment of Tensorflow. However, this is a big gap between the video material and the programming assignment. Need to add more training for Tensorflow before deep learning models. And the instructors speak too fast.

por Max P Z

19 de Nov de 2017

The content of the course and programming assignments is well designed. However, there're some technical issues with the assignments (eg. unable to submit the results for honor content). And some requirements for the accuracy/loss in the programming assignments are really too high.

por Margarita C

29 de Jul de 2019

My impression of the course is controversial, like it itself is: an introduction to advanced DL. Tough and frustrating for the first experience in DL. The course was useful, but, as everyone notes, in the end you learn from materials you find in the Internet to complete the tasks.

por Tue R L C

20 de Mar de 2018

This is a relative new courses which shows in some of the assignments e.g. minor mistakes and weird hacks required to pass them. The final project is a bit of a let down as it basically requires the user to do some data processing in python but no "real" machine learning.

por Javier C D

1 de Jan de 2021

Very interesting course if you have some previous knowledge of Machine Learning. Lectures are interesting and the exercises are very insightful and very well designed. The only bad aspect is that the exercises use the old tensorflow 1 instead of the current tensorflow 2.

por Kirill K

28 de Fev de 2021

Thank you for your course and assistance at forum.

The one cons: I expected more fundamental knowledge with more detailed explanation of NN work.

I made a lot of additional researches to understand clearly what is backpropagation, activation function, embeddings, etc.

por Gonzalo C

20 de Jun de 2020

You should record again all the videos of week 4, because the pronunciation in that videos are not good enough to understand well all the details, and It's kind annoying to listen all the videos, and keep listening for long time. The rest was a great course

por Andrei V

8 de Jun de 2018

Nice intro to DL. Final assignement is quite hard to accomplish, as you don't know the goal - loss should not too small, not too big (but are the boundaries?). For me it was ok, as I'm running on GPUs, but it should be painfull path for CPU folks.

por Thomas L

29 de Ago de 2020

The course is greatly taught and benefits from having several teachers, each having their own touch and approach to the material.

An upgrade of the programming assignments to the latest version of tensorflow would however be more than welcome!