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Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера

Оценки: 7,338
Рецензии: 1,140

О курсе

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization....

Лучшие рецензии


14 мар. 2020 г.

Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..


12 нояб. 2020 г.

A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!

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801–825 из 1,142 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Faiz A

2 авг. 2020 г.

Course was quite good, but the last assignment was a little challenging,Well..that's what i really liked!. Also, i felt like more concepts in computer vision had to be covered like Object detection, segmentation. Fairly basic concepts were emphasized here.

автор: Pranjal J

12 дек. 2021 г.

This course provided a nice guidance about filtering, cleaning and augmenting the datasets. This will definately help to build the models where the custom dataset needs to be generated and then use it for training with reduced chances of overfitting.

автор: Marco

25 июля 2020 г.

I think some parts of the assignments are not really the main objective of the course, they focus more on methods that involve just creating folders and copying files, which is not what I was there for. Aside from that, great ML content right here :)

автор: Oscar D D L T

7 сент. 2020 г.

Excelente curso, casi no necesitas saber programar los conceptos super actuales y las actividades te permiten ejecutar procesos de inteligencia artificial y lograr resultados interesantes con un conocimiento tecnico minimo....super recomandable!!!!!

автор: Saeif A

20 авг. 2019 г.

This is another great course in the specialization. I wish only there were graded exercises like the previous course that we can submit and get a grade for. I understand maybe this is due to the long time of training and that is not possible to do.

автор: Voltaire L

15 янв. 2021 г.

The final project was missing some prompts for additional code. I'm all for research but there should be a heads up that we won't have all the prompts we need, since all the tests before specifically asked for the code needed to pass.

автор: Thomas L

4 нояб. 2019 г.

Maybe a bit repetitive, when you just finished Course 1. We see a lot of lines of codes explained again from course 1 and I think that could be avoided.

However, the new concepts are nicely introduced and very interesting to implement!

автор: Alvin M

27 окт. 2020 г.

Sudden spike of difficulty and approach in the final assignment, but overall, the pacing is really nice. You really can't solve the last assignment without reading the discussion forum or looking for things for yourself though.

автор: William G

16 авг. 2019 г.

It was good, but similar to other learners I feel a little light in content. Though in tandem with the deep learning specialization gives a good view on convolutional neural networks as well as its implementation in tensorflow.

автор: Leon R

26 окт. 2019 г.

Loved the course. I would have liked a module on saving your own models and then loading them later. The Inception one is nice, but it comes with some "niceties" that I don't think you have with loading a home grown model.

автор: Humberto d S N

9 июня 2019 г.

It's an great course with simple explanations about the Deep Learning topic. It's a perfect fit for beginners or those who want to have a practical review before starting using Tensorflow 2.0 with keras implemetations.

автор: Varun K M

27 апр. 2020 г.

It was a great course but there wasn't much theory into explaining why and what's happening. A course to get started with the coding without actually needing to require what is happening in the background.

автор: J N B P

26 янв. 2021 г.

A great course for those who want to start building their AI models using Tensorflow. It explains how to use the required tools for different purposes like data augmentation, transfer learning, etc.

автор: Kalana A

14 апр. 2020 г.

Nice course. Even though I have previously done some projects using CNN and multi-class classification still this course let me to have an insight to how these APIs work. Keep Up The Good Work!!!!!!

автор: Fahmi J

29 апр. 2020 г.

This course awesome, but the notebook from coursera "i think" doesn't support any experiment we want, so we have to do it on google colab. But great, limitation is okay as long it's still graded

автор: XX N

2 окт. 2019 г.

The course is really nice. But would be better if the convolutional layers were a bit more detailed. It was a bit difficult for me to understand all the parameters e.g: input/output filter size.

автор: luis a

29 сент. 2019 г.

The course was fine sometimes I feel too easy. I would like to see more of the available options for the layers, such as padding, stride. filter size, mean average, batch normalization, etc...

автор: nick b

17 авг. 2021 г.

Good one for understanding convulutional networks. But the last assignment is not good. No explanation and you need to change the code provided to finish it. What they say you should not do.

автор: C A

23 дек. 2020 г.

Assignments are good, but it should concentrate more on the actual problem rather than the file reading or any nitty gritty details without any hint. Thanks , this course is good in overall.

автор: Rajesh R

14 июня 2020 г.

Great course to learn newer aspects of TF. For me a great revision of ConvNets and a confidence builder. If there's one thing I'd fix, it would be the autograder and how often it crashes.

автор: Amit G

24 июня 2021 г.

I liked this course, and also the way progression is taking place but the time required to complete this course needs to be reevaluated. This course can be finished in 8-10 hours easily.

автор: Amit K D

24 сент. 2020 г.

Found the hands-on not very interesting. Couple of them focussed on file handling and stuff rather than on more important stuff that getting into the hoods of transfer learning, etc.

автор: Rakesh G

16 янв. 2020 г.

I think this was a good course but the standard of exercises and quizzes was too easy. More conceptual questions especially in quizzes would help in understanding the topic better

автор: ashish s

22 апр. 2020 г.

Overall good. Could have gone in bit more depth on how various hyper parameter tuning and regularization methods impact the model training. Provide some best practices tips .

автор: Gerardo S

26 сент. 2019 г.

the last exercise needed a big upload, made it imposible (for me) to do. This was a problem not related to the subject, should use data downloadable directly from internet.