Chevron Left
Вернуться к Convolutional Neural Networks in TensorFlow

Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера

Оценки: 6,036
Рецензии: 918

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

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.

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:

601–625 из 911 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Gaurav P

8 авг. 2019 г.

Too basic

автор: Mohamed M F

2 авг. 2020 г.


автор: Christos M

21 мар. 2020 г.


автор: Shailesh K

4 авг. 2020 г.


автор: Vaibhav v s

2 июня 2020 г.


автор: Mazahir R

17 апр. 2020 г.


автор: Santiago G

12 мар. 2020 г.


автор: hitashu k

13 янв. 2020 г.


автор: Kavya B

5 дек. 2019 г.


автор: Jeff L J D

5 нояб. 2020 г.


автор: Josef N

19 июня 2020 г.


автор: Ben B

6 мая 2020 г.


автор: Aji S

3 мая 2020 г.


автор: Johnnie W

22 сент. 2020 г.



25 июля 2020 г.


автор: Rifat R

7 июня 2020 г.


автор: PANG M Q

29 мая 2020 г.


автор: Amit K

13 мая 2020 г.


автор: Nho N

17 мар. 2020 г.


автор: zhenzhen w

18 нояб. 2019 г.


автор: Jurassic

6 сент. 2019 г.


автор: 林韋銘

20 авг. 2019 г.


автор: João A J d S

3 авг. 2019 г.

I think I might say this for every course of this specialisation:

Great content all around!

It has some great colab examples explaining how to put these models into action on TensorFlow, which I'm know I'm going to revisit time and again.

There's only one thing that I think it might not be quite so good: the evaluation of the course. There isn't one, apart from the quizes. A bit more evaluation steps, as per in Andrew's Deep Learning Specialisation, would require more commitment from students.

автор: Anand H

12 сент. 2019 г.

One challenge i have faced is with deploying the trained models. I find very little coverage on that across courses. It's one thing to save a model.h5 or model.pb. It would be nice if you can add a small piece on deployment of these models using TF Serving or something similar. There is some distance between just getting these files outputted and deploying. TF documentation is confusing about some of these things. Would be nice if you can include a module on that.

автор: AbdulSamad M Z

1 авг. 2020 г.

Great course! Builds on the concepts of Course 1 in this Specialization although the course can be taken without having completed Course 1. Concepts are explained in a super clear and engaging way and the hands-on exercises give you the experience you need to become proficient. The course covers plenty of practical concepts including some pitfalls for practitioners to avoid, but the theoretical concepts are covered less than I expected.