Evgeny Sokolov
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 National Research University – Higher School …
Instructor Details
Courses : 1
Specification: Introduction to Deep Learning
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49 reviews for Introduction to Deep Learning
Add a review
| Price | Free |
|---|---|
| Provider | |
| Duration | 35 hours |
| Year | 2017 |
| Level | Expert |
| Language | English ... |
| Certificate | Yes |
| Quizzes | Yes |
FREE
Samuel R –
Great lectures and homeworks.They were challenging
Hermon A –
The explanation of TensorFlow is not enough and the programming homeworks have already a lot of already written (because, i would be very difficult to programming the all of the homework by ourselves in this stage of learning). I think it is better programming homeworks with examples more easy, but with more programming by ourselves. At least, I think it is already well enough for the final evaluation, the automatic correction and then, the correction by peer only delay the evaluation.
Zhou C –
The instructions are often unclear and the ipynb files often does not even run.
Parag H S –
Amazing
Shervin S –
Thoughtful course with good examples and code. Instructor presentations and graphics used were very organized and clear. Assignments are fairly simple, in terms of work required, but they require you to understand the context and reading through the code is useful. The forum was fairly useful, too. Some further comments explaining code blocks and practices would have made this experience complete.
Shahzaib M –
theory was good but at the time of Assignment i really felt blank as i have studied nothing, which i mean there is no technical support given in lectures, may be this is my fault that i cannot cop–up to the complexity. but still there is a room for improvement may be 5 to 10 min video to help student understand what they are supposed to do. starting with coder decoder i literally gave up on assignment. so i had to search web and i felt lack of external matters too from where i could get help i am hoping that this response will the up coming student. i focused on the things need to be improved but it does not mean that the course was not good over all. starting week was quite good i rate those week 5/5 stars. but later on the journey i had problem in understanding the pronunciation but than i realized its not that it is the material i am not clear of. Thanks.
Aleksandr G –
Very advanced!
Pavan K U –
Overall course content is good and engaging. But I feel a little bit gap while doing assignments. I feel instructions are clear but for better understanding, it would have been better if a sample input and output is present after every function we implement.
Timothy G –
Very good course and training
Jens R –
I learned a lot. I had a tiny typo in the last exercise, which took most of my time. But searching for this mistake was probably the time I learnt the most ;).
heechan s –
Good
Sylvain D –
Very nice and instructive.
Tiandong W –
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.
dinesh k –
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
Yevgen A –
Great course. Even though I’ve done Andrew Ng’s ML course twice and completed his Deep Learning Specialization, I learned a lot of new things in this Intro Course of AML specialization.
Diego T B –
I think this is a nice course! however I felt like I cannot do many of the things I did in the course from scratch. Nice topics, well taught. The only bad point is that subtitles in English are terrible, they need to be more accurate.
Dmitry –
Good starting point! Thanks to the creators of the course.
franco p –
Amazing!
Thomas F –
Great course! How to use deep neuro nets for image recognition!
Abdul R –
Well prepared course!
Xin Q –
A really nice course as an introduction to DL.
Ivan M –
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!
Tadas ` –
Quite good – not too basic.
Pun C S –
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
Alexandru C –
It is quite a rare case, but the course is quite challenging, in a good way. I would definitely not recommend you to start machine learning with it, but it is a good course to advance
Christhian F –
Very good and challenging!!
Philip G –
Out dated
Hussein N –
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
Sinan G –
Wide and deep range of important topics, a high level for an introduction course.
Tanishq S –
Really informative course on fundamentals of deep learning.
Wadim W –
Intensive course with tough exercises. Very educational. Nevertheless, in my opinion, the mandatory nature of peer reviewing is no suitable for online courses.
David P –
This is an extremely poorly prepared course in which the lecturers just throw material at you without bothering to make it even slightly comprehensible. One has to struggle really hard to understand what they are talking about –– they often use concepts and terms that have never been defined before, the slides are sloppy and often formulas make no sense. That said, I’ll probably continue struggling with the lectures, since this is (unfortunately) the only advanced resource for deep learning at the moment. I really hope the lecturers will listen to the multitude of negative reviews and make an effort to improve their presentation.
Arnav G –
highly satisfied…will do it again
Alfredo G Z –
I definitely learnt a lot, this is called Intro to Deep Learning yet it is much more than an Intro. Assignments are hard but challenge you to think a lot. The only thing I would improve is if the staff did more maintaining and responded more quickly to the forums.
Rabia –
This was a great course with a lot of hands–on programming time. I liked that the programming assignments didn’t have a lot of hand–holding and I ended up learning a decent amount of numpy/tensorflow/keras on my own. It would have been better to have a little more guidance in terms of functional requirements for some of the later assignments which would have saved some unnecessary frustration. But overall it was awesome – looking back on it, I’m amazed at the breadth of material they covered.
Andrey M –
Very nice course to meet deep learning. Based on “Learn by doing”. Very good teachers.
Aparna S –
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. –
Samson D –
The video content is quite good and I’ve learned a lot. However, the preparation for the exercises is insufficient and the format of fill in the blanks is not really as educative as it is confusing.
Yijie Z –
Taking Andrew’s machine learning class helps you enter the world, this series would take you to another level
Saptashwa B –
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 !
Siddharth P –
Tensorflow 2 would have been great
Chen Z –
Good course learned a lot!
Amoghavarsha B –
It’s a very hands on course. Lots of programming assignments which really helps improve our programming skills.
Jeremy C –
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
kareem j –
The content is great, but sometimes some concepts need more illustration. Also, sometimes the language is not spoken perfectly which makes it a bit hard to understand.
imran k –
The course was awesome, I have learned lots of new things, clear some doubts, I have enjoyed a lot.
Ipsita S –
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!
Anselmo F –
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.
Malay P –
Assignments and Project provided insight into the topic but, sometimes I had to look for some topics from elsewhere as I didn’t understand the course videos properly.