Вернуться к Introduction to Deep Learning

4.6

звезд

Оценки: 1,691

•

Рецензии: 394

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 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.

Фильтр по:

автор: Meng Z

•8 июня 2019 г.

This is a good course, though the instructors failed to keep their pace. If possible, I hope the course updates along TensorFlow 2.0 and provides more readings. As mentioned by other students, we don't want to watch videos on gradient descent again and again. I hope the instructors save time to talk more about some state of the art models and more about TensorFlow, links to good readings, and maybe more exercises on gradient descent and other fundamental techniques.

автор: Abhishek S

•12 июня 2018 г.

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

автор: Федоров Ф

•18 нояб. 2018 г.

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

автор: Igor B

•8 февр. 2019 г.

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.

автор: Anupkumar M

•11 окт. 2018 г.

Too much mathematical

Some of the instructors were tough to follow

автор: Miten S

•24 авг. 2020 г.

You should update it to TF2

автор: Jeremy C

•4 февр. 2020 г.

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

автор: Ivan M

•14 окт. 2019 г.

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!

автор: Siddhartha D

•31 мар. 2018 г.

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.

автор: Ayush T

•26 июля 2018 г.

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.

автор: Samuel Y

•14 янв. 2018 г.

Very fast and solid course, requiring in-depth knowledge and hard working. Especially the most criticized program assignment part, some is not well detailed and guided (even broken), but it is also partly realistic to mimic actual machine learning project development. It might also take days to tune and try to beat some required passing-level. Fix it yourself is really helpful to master the essence. Running those notebooks locally or in own server env with GPU support is strongly recommended to avoid wasting hours to find coursera kernel dead during training.

автор: khirod

•16 апр. 2020 г.

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.

автор: Rabia

•31 дек. 2019 г.

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.

автор: Shervin S

•16 авг. 2019 г.

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.

автор: Niculae I

•5 февр. 2018 г.

I guess for ML beginners this course is hard, because some of the lectures are pretty succint and you must figure out by yourself what is going on. But that helps too to understand better some issues. And at least for me the estimated time for the assignments was a bit too optimistic. Still I like the course and learned a lot of new things, altough I was not really new to this matter.

автор: nithin k m

•17 апр. 2020 г.

There aren't many Courses as this which gives us detailed understanding of Why things Work and Do not Work and What can you do to make them Work. Their theory explanation and practical application's of AutoEncoder's and RNN's is highlight of this Course which aren't offered by many. In loved the projects which do involve every small detail and helped me in Office-Work.

автор: Kelvin L

•12 мар. 2018 г.

This program is more suitable to those who already have mid level knowledge about the nuts and bots of Deep Learning and looking for hands-on opportunities to advanced skills. Having said that, the projects are really rewarding.

The main problem of this program is that. the teaching is very brief and there's no supporting resource at all, except the discussion forum.

автор: Marko P

•22 нояб. 2020 г.

The course is very good with great lecturers. All lecturers explained their parts clearly and understandable. The assignments were challenging, allowing participant to dive into deep learning with tensorflow.

The negative side is that the lectures (or at least the assignments) are still not updated for tensorflow 2, but I hope that it will change in the future.

автор: Ravi P B

•4 нояб. 2020 г.

Introduction to Deep Learning is an excellent course to dive into the beautiful world of deep learning and artificial intelligence. Covers a wide range of topics. Instructors,course content,programming assignments are absolutely amazing. The assignments are bit advanced and challenging and this makes the course even more enjoyable. Absolutely loved it.

автор: Dalton H

•24 мая 2019 г.

This course was great. I thought the lectures were good, and the quizzes are good at testing your knowledge, but the bets part of the course comes from the assignments. The assignments were both fun and interesting, and allowed me to try different tasks I would have been too intimidated to try otherwise (such as GANs). I really enjoyed this course.

автор: Michał G

•26 июня 2020 г.

The name of the course highlight the scope of it. Indeed, it is intro to deep learning. I like the Programming Assigmnents and very to-the-ground approach. Neural Nets with using only numpy was great. Highly recommended for people who are in the industry of Data Science and want to have broad, practical experience with deep learning.

автор: Kris J

•26 апр. 2018 г.

I didn't watch the videos as I wanted to try my current know-how on the assignments directly, but I can only recommend doing them, as they will provide you with great guidelines on implementing and training different types of neural networks. Even for a fairly experienced data scientist, the assignments were compelling enough.

автор: Eric A S

•21 мая 2018 г.

This is an amazing course, though I would not recommend it to people who are new to machine learning. If you are familiar with the basics, this course is a great intro to more advanced topics, which are explained in easy to understand terms. The assignments are not easy and a lot of work is required, but it is well worth it.

автор: Ivan K

•14 дек. 2017 г.

I like the course, it gives nice overview of neural nets, frameworks and general implementation approaches. It's a bit time consuming if you're using commodity laptop to train models, but very realistic at the same time. There were a couple of issues with programming assignments, but I'm still giving the course solid 5.

автор: Jorge

•7 авг. 2019 г.

Sometimes it's hard to follow, since it is more advanced machine learning than introduction, but that's not bad. It gives a general vision of how machine learning solves different problems. Assigments are truly demanding but, again, that's not bad. Resolving them guarantees that you have learned concerned concepts.

- Поиск цели и смысла жизни
- Понимание медицинских исследований
- Японский язык для начинающих
- Введение в облачные вычисления
- Основы самоосознанности
- Основы финансов
- Машинное обучение
- Машинное обучение с использованием Sas Viya
- Наука благополучия
- COVID-19: отслеживание контактов
- Искусственный интеллект для каждого
- Финансовые рынки
- Введение в психологию
- Начало работы с AWS
- Международный маркетинг
- C++
- Прогнозная аналитика и интеллектуальный анализ данных
- Получение навыков обучения от Калифорнийского университета в Сан-Диего
- Программирование для всех от Мичиганского университета
- Программирование на языке R от Университета Джонса Хопкинса
- Курс CPI для CBRS от Google

- Обработка естественного языка (NLP)
- Искусственный интеллект в медицине
- Мастер слова: письмо и редактирование
- Моделирование инфекционных заболеваний
- Американское произношение английского языка
- Автоматизация тестирования программного обеспечения
- Глубокое обучение
- Python для всех
- Наука о данных
- Основы бизнеса
- Навыки Excel для бизнеса
- Наука о данных с Python
- Финансы для каждого
- Навыки общения для инженеров
- Курс по продажам
- Управление карьерным ростом
- Бизнес-аналитика от Уортонской школы бизнеса
- Позитивная психология от Университета Пенсильвании
- Машинное обучение от Вашингтонского университета
- Графический дизайн от Калифорнийского института искусств

- Профессиональные сертификаты
- Сертификаты MasterTrack
- ИТ-поддержка Google
- Наука о данных IBM
- Инженерия данных от Google Cloud
- Прикладной искусственный интеллект от IBM
- Облачная архитектура от Google Cloud
- Аналитик по кибербезопасности от IBM
- ИТ-автоматизация с помощью Python от Google
- Специалист по работе с мейнфреймами на IBM z/OS
- Прикладное управление проектами от Калифорнийского университета в Ирвайне
- Сертификат по педагогическому дизайну
- Сертификат по проектированию и управлению в строительстве
- Сертификат по большим данным
- Сертификат по машинному обучению для аналитики
- Сертификат по управлению инновациями и предпринимательству
- Сертификат по экологии и устойчивому развитию
- Сертификат по социальной работе
- Сертификат по искусственному интеллекту и машинному обучению
- Сертификат по пространственному анализу данных и визуализации

- Степени в области компьютерных наук
- Степени в области бизнеса
- Степени в области общественного здравоохранения
- Степени в области науки о данных
- Степени бакалавра
- Бакалавриат в области компьютерных наук
- Магистр в области электротехнического проектирования
- Степень бакалавра
- Магистр в области управления
- Магистр компьютерных наук
- Магистр общественного здравоохранения
- Степень магистра в области бухгалтерского учета
- Магистр компьютерных и информационных технологий
- Диплом магистра делового администрирования онлайн
- Магистр прикладной науки о данных
- Международная программа MBA
- Магистр в области инноваций и предпринимательской деятельности
- Магистр компьютерных наук в области науки о данных
- Магистр в области компьютерных наук
- Магистр здравоохранения