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Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера

Оценки: 6,208
Рецензии: 961

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!

Фильтр по:

801–825 из 955 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Xinhui H

16 сент. 2019 г.

Some overlap with first course.

автор: Kumari M

17 июня 2020 г.

Pretty much enhanced my skills

автор: Cheng H Z

17 дек. 2019 г.

Too little things were covered

автор: Lorenzo G R N

9 дек. 2020 г.

Course is missing classnotes

автор: Subhendu R M

12 авг. 2020 г.

A nice well-balanced course.

автор: Rohit K S

18 сент. 2020 г.

Mind Boggling Experience!!

автор: Walter G

29 нояб. 2019 г.

A very brief quick course.

автор: Guilherme R M

10 июня 2019 г.

Bom curso, muito prático.

автор: Loutzidis A

16 мар. 2020 г.

The quiz were quite easy

автор: Prabesh G

23 мая 2019 г.

Okey.. So easy but okey

автор: Tanguy C

24 апр. 2020 г.

Thanks. Enjoyed it.

автор: j_lokesh

15 июня 2020 г.

that's was awesome

автор: Patrick L

26 дек. 2019 г.

I like this course

автор: Vivek S

24 июня 2019 г.

Super cool stuff!

автор: Paulo A C

23 апр. 2020 г.

Great content!!

автор: ashraf s t m

31 июля 2019 г.

Voice is low

автор: Venkatesh S

2 дек. 2019 г.


автор: Bingcheng L

12 нояб. 2019 г.

quite easy

автор: Suraj

11 февр. 2020 г.

Too easy.

автор: Hamzeh A

6 авг. 2019 г.

Very Cool

автор: Omar M

16 июля 2019 г.

Was okay

автор: S. M S H

21 сент. 2020 г.


автор: Michael

26 июля 2019 г.

A bit too basic and shallow in terms of conducting the lecture. You are left doing most of the things on your own as the trainer assumes you know. Like using the jupyter notebook, configuring the tensorfow. Some of the google collab books do not work or took too long to load, the videos are too short no notes provided at all. After finishing the course there is nothing to refer to and its starting all over again. Given the level of machine learning course with Professor Adrew Ng, the standard is very high and you will expect that same level. Nevertheless, the concepts are very useful and the lecture explain very well. There level of material left for students to practice on their own,like assignments, notes. Not to be referred to existing material.

автор: Muthukumarasamy S

4 авг. 2019 г.

Overall learning from this course is less compared to the expectations from a 4 week course. I was expecting to learn variety of TensorFlow implementations for CNN like Face recognition, Object detection. But this course only talks about Image classification. It would have been better if you could also discuss more about implementing various architectures in TensorFlow like ResNets, Inception. Also, You talked only about using sequential layers in Keras and concatenation of layers in Keras is not discussed here. I know all these concepts are discussed in Deep Learning specialization. I was only expecting to learn their implementation in TensorFlow from this course.

автор: Pablo A

4 сент. 2020 г.

It's a nice next step after the first course in this series, however, I think a lot of this could be summarized in a shorter course or even added to course 1. I was particularly annoyed by some of the assignments as they required knowledge of other libraries that are not part of the course. Particularly Week 2 and 4, I spent a lot of time figuring out how different libraries worked just so I could preprocess my data before even gettin on to the course material. Week 4 in particular feels cramped up and the assignment uses a lot of tools not previously discussed, I don't think I learned much from it, I just wanted to be done.