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Volver a Deep Neural Networks with PyTorch

Opiniones y comentarios de aprendices correspondientes a Deep Neural Networks with PyTorch por parte de IBM

4.4
estrellas
995 calificaciones
226 reseña

Acerca del Curso

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered. Learning Outcomes: After completing this course, learners will be able to: • explain and apply their knowledge of Deep Neural Networks and related machine learning methods • know how to use Python libraries such as PyTorch for Deep Learning applications • build Deep Neural Networks using PyTorch...

Principales reseñas

SY
29 de abr. de 2020

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!!

RA
15 de may. de 2020

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.

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101 - 125 de 227 revisiones para Deep Neural Networks with PyTorch

por Octavio L

28 de ago. de 2020

Me resultó un tanto tedioso y demasiado largo. Se solapa con contenidos de otros cursos del certificado

por Hüseyin D K

27 de dic. de 2020

Very short videos. Speaking so fast. It's like presenting not education.

por Aditya L

12 de ago. de 2020

I had very high expectations for this course since it was offered by IBM and being taught by someone with excellent credentials. I completed the course material for the first 2 weeks and I found the lectures to me unmotivating, inadequately explained, and very clearly the lecturer read from a script. Important concepts were not explained neither the conceptual deep learning one nor the PyTorch programming ones. They were very briefly explained often with one short sentence. I thought the ungraded labs were very well designed but the lecture quality was so poor, it seemed I was just googling and learning 90% of PyTorch myself. I had expected quality from this course however, I did not get it so I decided not to pay the $50 subscription and canceled the course. I was disappointed since I did spend good 15-20 hours on this course.

por Tarun C

3 de abr. de 2020

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.

Please see

https://www.coursera.org/mastertrack/instructional-design-illinois

for how to improve.

por Timur U

29 de mar. de 2020

Too many complicated theoretical materials and unclear practical instructions. I have lost motivation for this course.

por sada n

10 de ene. de 2020

it is too deep

por A A A

7 de jul. de 2020

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.

por Анатолий М

9 de may. de 2021

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.

por Erdem Ş

17 de jun. de 2020

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.

por Georgios C

4 de ago. de 2020

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.

por Kartikey C

7 de nov. de 2020

In-depth course, goes in much more detail than the usual introductory courses, also emphasizes on practical hands on rather than theoretical knowledge

por Benjamin P

19 de ene. de 2021

Good pacing, great examples and the assignments are doable within the time allocated for them. Combines both technical information and applied code.

por Tobias B

14 de jun. de 2021

G​reat course. Although some of the material clearly wasn't made by native english speakers, and the language usage could be improved in future.

por Yashwardhan B

20 de abr. de 2021

The course content was very well presented and was relatively easy to understand even when the pytorch framework is a bit complex. Thank you!

por THOMONT B

13 de ene. de 2021

Joseph Santarcangelo is one of the best teacher i've seen in data science.

Courses were difficult but his explanations were really clear.

por OMAIMA E A

25 de may. de 2021

the course was perfect goes step by step and keeps reminding the student what he studies in the sections before, I love it

por jonathan b

10 de nov. de 2021

A​ good introduction to deep learning with pytorch. The examples are very clear and the learning rate is not to steep

por Adil D

29 de oct. de 2020

Really good course!! Theres few typos in the video lectures but a good way to see if you really understand things ;)

por ayush k

21 de ene. de 2021

I really enjoy this course. it really helps me to boost my knowledge of PyTorch and deep neural network.

por SUNIL K N

5 de jun. de 2021

It is an amazing course, I learned a lot, videos and labs everything is amazing, thanks Coursera!

por Garrett M

12 de nov. de 2020

Excellent course, well put together labs and videos, overall a very dense resource for the topic.

por Vaseekaran V

23 de oct. de 2020

Really great intro to PyTorch. Well explained the basics of Deep Learning along with PyTorch.

por Aryal G

9 de ago. de 2020

this is no doubt THE BEST and the most well thought pytorch and deep learning course so far .

por Andres I C R

9 de ene. de 2021

Really good structured with very clear explanation of the math behind the different topics

por Theophile T

25 de feb. de 2021

It was my first experience programming in PyTorch and I was amazed.