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Вернуться к Convolutional Neural Networks in TensorFlow

Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера

Оценки: 7,429

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:

901–925 из 1,158 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Eran Y

20 июня 2020 г.

Some references to relevant code in tests would be nice, don't send me searching.

автор: Gregor F

7 янв. 2020 г.

Straight to point with a lot of useful techniques! Interesting examples as well.


26 авг. 2020 г.

Nice course to learn about the basics about how to implement CNN's with keras.

автор: Sebastián M A

6 дек. 2019 г.

No le pongo las 5 estrellas por la falta de ejercicios prácticos calificables.

автор: Arun P R

18 апр. 2020 г.

Need to more specific about the instructions given for Programming Assignment

автор: Damon W

8 окт. 2019 г.

Good practical course. A bit heavy on visual images, but very informative.

автор: Ma Y

19 июня 2020 г.

Excellent materials and assignments! May be lectures can be a bit longer.

автор: Shaun M

23 февр. 2020 г.

greaded assignments are needed as they help to understand the code better

автор: Ruben A M

20 авг. 2020 г.

the course is good, however, I feel that the content is stretched a lot.

автор: Chidvilas K R

5 окт. 2020 г.

The course could have been a bit more complicated. It feels too simple.

автор: Prashant

28 авг. 2020 г.

Last week excercise was a little bit challenging ,overall great course.

автор: Vikas l

7 июня 2020 г.

This Covers pretty well more than basics and dig little deep into CNNs.

автор: Ahmed K

19 мая 2020 г.

Focusing more on the code for python beginners would be much better!

автор: Rishabh B

1 февр. 2020 г.

Could have had tougher exercises. Great basics course nevertheless!

автор: Xiangzhen Z

18 авг. 2019 г.

a little bit too easy compared to Andrew Ng's deep learning course.

автор: Sergio R

9 дек. 2020 г.

Excellent course, the teacher is good, I love learning all, thanks

автор: Zhou D

19 июня 2019 г.

actually I hope there will be the implementation of detection task

автор: Amardeep S

29 дек. 2019 г.

The homework assignments should be required and more challenging

автор: Longchao J

6 февр. 2021 г.

The last assignment is poorly designed. Others are good enough.

автор: Sharvil G

6 авг. 2019 г.

Transfer learning part should have been in more detail. Thanks.

автор: Ifrah M

14 мая 2020 г.

A little bit of more explanation of the notebooks are required

автор: Daniel A O C

3 апр. 2021 г.

El curso es útil para implementar una red convolucional desde

автор: Muhammad U

18 авг. 2019 г.

A well taught course with interesting coursework and projects

автор: Bethel H

11 мая 2020 г.

One of the best courses offered by community

автор: Fernando P

30 июля 2021 г.

amazing!! I really understand the path with TensorFlow!!