An extremely good course for anyone starting to build deep learning models. I am very satisfied at the end of this course as i was able to code models easily using pytorch. Definitely recomended!!
This is not a bad course at all. One feedback, however, is making the quizzes longer, and adding difficult questions especially concept-based one in the quiz will be more rewarding and valuable.
par Tarun C•
This course is a disorganized and unfocused. For example, much of the section on Bernoulli distribution is misleading or completely incorrect. It's also presented without context. Much of this is redundant give the other courses in this certificate program do a much better job of teaching ML concepts. The novelty of this course is about implementation using pytorch and most of the important details about how to use PyTorch and why certain parameters are used are glossed over.
Is this a course about ML and Neural Networks? Is this a course on PyTorch? It does both poorly.
for how to improve.
par Christian T•
Lots of errors in the questions and answers, annoying content structure, bad videos (speed, cadence, auto-generated voice that consistently mis-pronounces things). Labs that are identical to the videos. No context setting or understanding beyond trivial mechanics.
Even worse, the quizzes contain typing/syntax errors that you have to ignore and then suddenly some of the quizzes contain errors that you must not ignore.
This is a ridiculuously bad course and I have no idea how it got to getting this many good ratings.
ABSOLUTE WASTE OF TIME. CHOOSE A DIFFERENT COURSE!
par Timur U•
Too many complicated theoretical materials and unclear practical instructions. I have lost motivation for this course.
par sada n•
it is too deep
par A A A•
This course is really good in explaining the concepts and pytorch. Everything was explained in a detailed way, well structured. However, I found the course too segmented. Some lectures, some quizzes, and some labs can be combined. Example for week 1, I think 1.1 (introduction to tensors), 1.2 (1d tensors) and 1.3 (2d tensors) can be combined to single lecture or all 3 lectures be one after another making it appear like it’s together. The 2 labs can be combined into a single notebook. The 2 quizzes can be combined into 1 quiz of maybe 5 or more questions. Similarly, 1.4 (Simple Datasets) and 1.5 (Datasets) can be combined, and so on. I also think that the honours content about batch normalization should be included as part of normal contents. Maybe more advanced concepts can be put up as honours contents.
par Анатолий М•
Курс "Deep Neural Networks with PyTorch" подходит для новичков, людей с базовым математическим аппаратом, с базовыми знаниями программирования Python и для тех, кого интересует математика нейронных сетей и машинного обучения. Курс делает упор на самостоятельность обучающихся и людей, которые сами заинтересованы в прохождении лабораторных работ. Здесь есть много инструментов для обучения, вычисления метрик, визуализации результатов, которые могут пригодится Вам в проектах. Курс прекрасно подходит для людей со средним знанием английского языка (материал разработан так, что он понятен и глазам, и ушам). Советую пройти данный курс на английском языке или с английскими субтитрами, чтобы погрузиться в изучение PyTorch и профессиональной терминологии разработчиков.
par Erdem Ş•
even with no mandatory peer graded assignment, for me it was the hardest course to learn in "IBM AI Engineering". So many topics and so many codes to check for each week. i liked it. i believe i will revisit the materials in the future.
par Georgios C•
Great introduction to deep learning with pytorch. It would help if the notebooks in the labs take shorter to run so that the students can experiment with the code and the models.
par Kartikey C•
In-depth course, goes in much more detail than the usual introductory courses, also emphasizes on practical hands on rather than theoretical knowledge
par Benjamin P•
Good pacing, great examples and the assignments are doable within the time allocated for them. Combines both technical information and applied code.
par Tobias B•
Great course. Although some of the material clearly wasn't made by native english speakers, and the language usage could be improved in future.
par Yashwardhan B•
The course content was very well presented and was relatively easy to understand even when the pytorch framework is a bit complex. Thank you!
par THOMONT B•
Joseph Santarcangelo is one of the best teacher i've seen in data science.
Courses were difficult but his explanations were really clear.
par OMAIMA E A•
the course was perfect goes step by step and keeps reminding the student what he studies in the sections before, I love it
par Adil D•
Really good course!! Theres few typos in the video lectures but a good way to see if you really understand things ;)
par ayush k•
I really enjoy this course. it really helps me to boost my knowledge of PyTorch and deep neural network.
par SUNIL K N•
It is an amazing course, I learned a lot, videos and labs everything is amazing, thanks Coursera!
par Garrett M•
Excellent course, well put together labs and videos, overall a very dense resource for the topic.
par Vaseekaran V•
Really great intro to PyTorch. Well explained the basics of Deep Learning along with PyTorch.
par Aryal G•
this is no doubt THE BEST and the most well thought pytorch and deep learning course so far .
par Andres I C R•
Really good structured with very clear explanation of the math behind the different topics
par Theophile T•
It was my first experience programming in PyTorch and I was amazed.
par Sourabh K•
One of the best course in the IBM AI Engineer Specialization !
par Emanuel N•
Gran curso super detallista y explica muy bien los conceptos
par Milad E N•
it goes through neural network and builds it from scratch.