Voltar para Introduction to Deep Learning

4.6

estrelas

1,660 classificações

•

384 avaliações

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

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

TP

8 de Ago de 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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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 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!

por Abhinav U

•2 de Dez de 2017

It's a good course for people with some prior experience and background in machine learning (specially neural networks). The exercises and projects were a bit difficult and needed effort to get correct but helped reinforce the concepts.

por Milos V

•8 de Jan de 2019

Interesting and useful course. Capstone project was quite difficult, but I learned a lot - so I do not want to complain about it. Maybe a bit more code-related things during the lectures would be useful to make capstone project easier.

por nicole s

•18 de Mar de 2018

Very good content and teachers. Indeed advanced level, for the less advanced it would have been helpful to include some more clarifications towards solving the assignments and the mathematical derivation of the main concepts.

por Georgios P (

•26 de Abr de 2020

It is a good course overall, but some subjects feel a bit rushes. Also, it would be much better if authors were adding a week for learning the tools that are used in program assignments (Tensorflow and Keras specifically).

por Sachin

•1 de Mar de 2019

really nice course to hone your skills. but sometimes the assignments are really really tough and no hint is provided how to solve them. i was having problem because of my weak python skills. afterall course is relly nice

por Adam S

•26 de Fev de 2018

The course is very good. The last assignment was pretty ambitions with the image captioning but i am glad they did it. Some of the lectures the english is very difficult to follow. Other than that really helpful course.

por Tiandong W

•11 de Set de 2019

This is an ADVANCED DL course. If you have already learned Andrew Ng's deeplearning.ai course or other basic course, this course is good for you as a test. But if you don't know DL at all, this is not for you.

por Hamlet B

•20 de Jan de 2018

This course is incredibly challenging and the assignments can be frustrating with little guidance, and high bar to pass graders. I give it a high rating because it really pushed me to learn and master details

por Meetkumar R

•2 de Mai de 2020

It was a good experience as it introduced some basic concepts of Deep-Learning. But knowledge delivery was not good as it was hard to understand what the instructor was saying due to some language issues.

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