Chevron Left
Вернуться к Convolutional Neural Networks in TensorFlow

Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера deeplearning.ai

4.7
звезд
Оценки: 6,818
Рецензии: 1,059

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 deeplearning.ai 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 deeplearning.ai 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

MS
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!

JM
11 сент. 2019 г.

great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.

Фильтр по:

201–225 из 1,055 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: KISHOR

18 авг. 2020 г.

I learnt a lot of augmentation and image processing methods using tensorflow and several other tools for computer vision applications.

автор: Houssem A

24 июля 2019 г.

The course is well structured and explained from trainer I feel that I have more information and get knowledge in tensorflow practices

автор: Mohanad A N

31 мая 2019 г.

I hope all courses to be like this course or like andrew's ones. Very clear, easy to follow along, tons of info, direct to the point.

автор: Jorge M Z P

8 дек. 2020 г.

A sequential and easy to understand course. Contains all the basics to learn how to construct and improve Conv Nets on Tensorflow 2.

автор: Pablo C

6 апр. 2020 г.

Excellent explanations, and a lot of interesting and practical examples. Recommended if you have some experience with ML literature.

автор: Aguirre M

2 мар. 2020 г.

Excellent hands on course... if you have previously taken a more theoretical neural network course (for example Andrew Ng's classes)

автор: Prashant J

1 апр. 2020 г.

It's been a great journey about Convolutional Neural Networks. The most interesting thing of these course is Exercise on notebooks.

автор: Loveprit S

12 июня 2020 г.

This course is useful to those who have basic understanding of computer vision and want to know how they can use TensorFlow in it.

автор: Selçuk K

31 мар. 2020 г.

Great course. Thank you, Laurence.

However, I should admit submitting the exercises is a pain sometimes. Discussions help though.

автор: Juan S

30 нояб. 2020 г.

Genial el curso. Muchos ejemplos prácticos y usables para todo tipo de situaciones. Me gusta mucho como explican los profesores.

автор: Pushkaraj J S

11 июля 2020 г.

Great course. Introduces you to various data generation techniques which can be really useful when dealing with smaller dataset.

автор: 邹波波

24 сент. 2019 г.

The course is very great! You can see how convolution works, image processing, transfer learning and so on. Thank you teachers!

автор: Norifumi I

13 дек. 2020 г.

Although the topics are getting more complex, I liked how the lecturer keeps it simple and relates each data to the real world.

автор: Akhil K

22 июня 2019 г.

This course worked as a great reference for my project on Neural Networks. This is one of the great and well-structured course.

автор: Gustavo A

10 авг. 2019 г.

I enjoyed this course a lot, but I missed the code evaluation step. On the other hand, the content was as good as it has been.

автор: Dustin Z

27 июня 2020 г.

Fun course. A good balance of easy and challenging material. The lessons based primarily on notebooks, and are very hands-on.

автор: Jay H

15 июня 2020 г.

It was a very helpful course. The discussions forum help a bit. Maybe, more questions asked or answers could help even more.

автор: Diego O S

12 янв. 2021 г.

Really clear and with the exact amount of examples. (I would like to see more examples and not only the ones seen in class)

автор: VAGHELA H N S

18 июня 2020 г.

course is well designed. The codes and notebooks provided in the course can be very much helpful for any beginner to learn.

автор: Tze C L

18 июня 2020 г.

Very insightful and involving course that teaches you how to perform image classification with Convolution Neural Networks.

автор: Bonthu S

22 апр. 2021 г.

Simply the best course to implement convolution neural network in tensorflow but don't look for deep understanding of CNN.

автор: Zarinah C

20 февр. 2021 г.

One of the best online courses I've taken. Well explained, accessible, and references so that you can deep dive as needed.

автор: SRIKANTH T

12 сент. 2020 г.

the course in nice , the explanation given by Laurence Moroney is excellent , so the course is highly recommended to take

автор: Gogul I

22 июня 2019 г.

Amazing course to learn concepts such as Dropouts, Augmentation and Transfer Learning to solve real world image problems.

автор: Márton B

4 июня 2021 г.

I think this is a useful, understandable and practical course. I liked it. The codes in Jupyter notebooks are very good.