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学生对 DeepLearning.AI 提供的 Convolutional Neural Networks in TensorFlow 的评价和反馈

4.7
8,215 个评分

课程概述

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 DeepLearning.AI TensorFlow Developer 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 Developer 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....

热门审阅

MH

May 23, 2019

A very comprehensive and easy to learn course on Tensor Flow. I am really impressed by the Instructor ability to teach difficult concept with ease. I will look forward another course of this series.

CM

Apr 30, 2019

A patient and coherent introduction. At the end, you have good working code you can use elsewhere. Remarkably, the primary lecturer, Laurence Moroney, responds fairly quickly to posts in the forum.

筛选依据:

426 - Convolutional Neural Networks in TensorFlow 的 450 个评论(共 1,269 个)

创建者 Victor C

Jan 27, 2020

implement example is helpful, hope I can use these in my try

创建者 Apurv j

Sep 12, 2019

Short sweet and perfect videos to be understood very easily.

创建者 Desiré D W

Aug 8, 2019

Great content, excellent explanations, no assignment hassles

创建者 Aptha G

Jul 24, 2019

Really helped a lot in understanding CNN, transfer learning.

创建者 Vinodh

Mar 1, 2021

Great Start to begin learning Convolutional Neural Networks

创建者 Suraj

Feb 27, 2021

Amazing intuitive explanations by Laurence moroney sir 🙌🏻

创建者 Alexey A

May 31, 2020

Great course! Especially final programming task is the best

创建者 Shubham

May 11, 2020

It was a relatively easy course but a good one nonetheless.

创建者 Muhammad A A K

Dec 20, 2019

I ready enjoyed learning this course. It was truly awesome.

创建者 Pachi C

Jun 26, 2019

Great course and fantastic professors (Laurence and Andrew)

创建者 Mohsen P

Nov 30, 2021

It is a practical course for computer vision applications.

创建者 Pravin B

Feb 26, 2021

Excellent course to brush up the knowledge and learn more.

创建者 Alistair W

Apr 23, 2020

Great course - easy to follow. Clear, concise and fun! :-)

创建者 Peter B

Sep 13, 2019

An amazing course that was written by masters of the field

创建者 p g

Aug 23, 2023

Great learning material as always in that set of courses!

创建者 Idriss C

Jan 2, 2022

Very informative as an introduction to image processing/

创建者 Ngọc L L

Oct 4, 2021

nice course, explain really clear and easy to understand.

创建者 Allan H

Sep 19, 2021

Well explained and quite thoughtful. Thanks Dr. Moroney!

创建者 Developing T

May 8, 2020

Amazin Experience was with course tutor. I love coursera.

创建者 Nagamalla T

Apr 5, 2020

So much learning in a very short period, feeling amazing.

创建者 Andrew S

Feb 25, 2020

Great course! Really helped me learn the basics of keras.

创建者 Phạm Đ T

May 5, 2021

This course help me a lot to practice in Neuron Network.

创建者 Leo C

May 3, 2020

Very little info overall, but also very quick to finish.

创建者 Sachin

Feb 14, 2020

The Way this has been driven, I never felt disconnected.