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다음 전문 분야의 4개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

완료하는 데 약 8시간 필요

권장: 4 weeks, 4-5 hours/week...

영어

자막: 영어, 스페인어, 러시아어

배울 내용

  • Check

    Learn best practices for using TensorFlow, a popular open-source machine learning framework

  • Check

    Build a basic neural network in TensorFlow

  • Check

    Train a neural network for a computer vision application

  • Check

    Understand how to use convolutions to improve your neural network

귀하가 습득할 기술

Computer VisionTensorflowMachine Learning

다음 전문 분야의 4개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

완료하는 데 약 8시간 필요

권장: 4 weeks, 4-5 hours/week...

영어

자막: 영어, 스페인어, 러시아어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 6시간 필요

A New Programming Paradigm

4개 동영상 (총 16분), 5 readings, 3 quizzes
4개의 동영상
A primer in machine learning3m
The ‘Hello World’ of neural networks5m
Working through ‘Hello World’ in TensorFlow and Python3m
5개의 읽기 자료
Before you begin: TensorFlow 2.0 and this course10m
From rules to data10m
Try it for yourself10m
Introduction to Google Colaboratory10m
Week 1 Resources10m
1개 연습문제
Week 1 Quiz
2
완료하는 데 7시간 필요

Introduction to Computer Vision

7개 동영상 (총 15분), 6 readings, 3 quizzes
7개의 동영상
An Introduction to computer vision2m
Writing code to load training data2m
Coding a Computer Vision Neural Network2m
Walk through a Notebook for computer vision3m
Using Callbacks to control training1m
Walk through a notebook with Callbacks1m
6개의 읽기 자료
Exploring how to use data10m
The structure of Fashion MNIST data10m
See how it's done10m
Get hands-on with computer vision1h
See how to implement Callbacks10m
Week 2 Resources10m
1개 연습문제
Week 2 Quiz
3
완료하는 데 8시간 필요

Enhancing Vision with Convolutional Neural Networks

6개 동영상 (총 19분), 6 readings, 3 quizzes
6개의 동영상
What are convolutions and pooling?2m
Implementing convolutional layers1m
Implementing pooling layers4m
Improving the Fashion classifier with convolutions4m
Walking through convolutions3m
6개의 읽기 자료
Coding convolutions and pooling layers10m
Learn more about convolutions10m
Getting hands-on, your first ConvNet10m
Try it for yourself1h
Experiment with filters and pools1h
Week 3 Resources10m
1개 연습문제
Week 3 Quiz
4
완료하는 데 9시간 필요

Using Real-world Images

9개 동영상 (총 27분), 10 readings, 3 quizzes
9개의 동영상
Understanding ImageGenerator4m
Defining a ConvNet to use complex images2m
Training the ConvNet with fit_generator2m
Walking through developing a ConvNet2m
Walking through training the ConvNet with fit_generator3m
Adding automatic validation to test accuracy4m
Exploring the impact of compressing images3m
A conversation with Andrew1m
10개의 읽기 자료
Explore an impactful, real-world solution10m
Designing the neural network10m
Train the ConvNet with ImageGenerator10m
Exploring the solution10m
Training the neural network10m
Experiment with the horse or human classifier1h
Get hands-on and use validation30m
Get Hands-on with compacted images30m
Week 4 Resources10m
Wrap up10m
1개 연습문제
Week 4 Quiz
4.7
706개의 리뷰Chevron Right

43%

이 강좌를 수료한 후 새로운 경력 시작하기

40%

이 강좌를 통해 확실한 경력상 이점 얻기

11%

급여 인상 또는 승진하기

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning의 최상위 리뷰

대학: ASMar 9th 2019

Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?

대학: RDAug 14th 2019

Great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.

강사

Avatar

Laurence Moroney

AI Advocate
Google Brain

deeplearning.ai 정보

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

TensorFlow in Practice 전문 분야 정보

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. Courses 1-3 are available now, with Course 4 launching in July....
TensorFlow in Practice

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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