About this Course
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다음 전문 분야의 4개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.

완료하는 데 약 7시간 필요

권장: 4 weeks of study, 4-5 hours/week...

영어

자막: 영어
User
Course을(를) 수강하는 학습자
  • Machine Learning Engineers
  • Data Scientists
  • Chief Technology Officers (CTOs)
  • Data Engineers
  • Scientists

배울 내용

  • Check

    Handle real-world image data

  • Check

    Plot loss and accuracy

  • Check

    Explore strategies to prevent overfitting, including augmentation and dropout

  • Check

    Learn transfer learning and how learned features can be extracted from models

귀하가 습득할 기술

Inductive TransferAugmentationDropoutsMachine LearningTensorflow
User
Course을(를) 수강하는 학습자
  • Machine Learning Engineers
  • Data Scientists
  • Chief Technology Officers (CTOs)
  • Data Engineers
  • Scientists

다음 전문 분야의 4개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.

완료하는 데 약 7시간 필요

권장: 4 weeks of study, 4-5 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 4시간 필요

Exploring a Larger Dataset

8개 동영상 (총 18분), 5 readings, 3 quizzes
8개의 동영상
A conversation with Andrew Ng1m
Training with the cats vs. dogs dataset2m
Working through the notebook4m
Fixing through cropping49
Visualizing the effect of the convolutions1m
Looking at accuracy and loss1m
Week 1 Wrap up33
5개의 읽기 자료
Before you Begin: TensorFlow 2.0 and this Course10m
The cats vs dogs dataset10m
Looking at the notebook10m
What you'll see next10m
What have we seen so far?10m
1개 연습문제
Week 1 Quiz30m
2
완료하는 데 4시간 필요

Augmentation: A technique to avoid overfitting

7개 동영상 (총 14분), 6 readings, 3 quizzes
7개의 동영상
Introducing augmentation2m
Coding augmentation with ImageDataGenerator3m
Demonstrating overfitting in cats vs. dogs1m
Adding augmentation to cats vs. dogs1m
Exploring augmentation with horses vs. humans1m
Week 2 Wrap up37
6개의 읽기 자료
Image Augmentation10m
Start Coding...10m
Looking at the notebook10m
The impact of augmentation on Cats vs. Dogs10m
Try it for yourself!10m
What have we seen so far?10m
1개 연습문제
Week 2 Quiz30m
3
완료하는 데 4시간 필요

Transfer Learning

7개 동영상 (총 14분), 5 readings, 3 quizzes
7개의 동영상
Understanding transfer learning: the concepts2m
Coding transfer learning from the inception mode1m
Coding your own model with transferred features2m
Exploring dropouts1m
Exploring Transfer Learning with Inception1m
Week 3 Wrap up36
5개의 읽기 자료
Start coding!10m
Adding your DNN10m
Using dropouts!10m
Applying Transfer Learning to Cats v Dogs10m
What have we seen so far?10m
1개 연습문제
Week 3 Quiz30m
4
완료하는 데 4시간 필요

Multiclass Classifications

6개 동영상 (총 12분), 5 readings, 3 quizzes
6개의 동영상
Moving from binary to multi-class classification44
Explore multi-class with Rock Paper Scissors dataset2m
Train a classifier with Rock Paper Scissors1m
Test the Rock Paper Scissors classifier2m
A conversation with Andrew Ng1m
5개의 읽기 자료
Introducing the Rock-Paper-Scissors dataset10m
Check out the code!10m
Try testing the classifier10m
What have we seen so far?10m
Wrap up10m
1개 연습문제
Week 4 Quiz30m
4.7
193개의 리뷰Chevron Right

15%

이 강좌를 통해 확실한 경력상 이점 얻기

11%

급여 인상 또는 승진하기

Convolutional Neural Networks in TensorFlow의 최상위 리뷰

대학: JMSep 12th 2019

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.

대학: PSSep 14th 2019

An excellent course by Laurence Moroney on explaining how ConvNets are prepared using Tensorflow. A really good strategy to have the programming exercises on Google Colab to speed up the processing.

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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