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다음 특화 과정의 4개 강좌 중 3번째 강좌:
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중급 단계

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have a basic familiarity with building models in TensorFlow

완료하는 데 약 19시간 필요
영어
자막: 영어

배울 내용

  • Leverage built-in datasets with just a few lines of code

  • Use APIs to control how you split your data

  • Process all types of unstructured data

귀하가 습득할 기술

TensorflowMachine Learning
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have a basic familiarity with building models in TensorFlow

완료하는 데 약 19시간 필요
영어
자막: 영어

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강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 5시간 필요

Data Pipelines with TensorFlow Data Services

완료하는 데 5시간 필요
14개 동영상 (총 27분), 2 개의 읽기 자료, 2 개의 테스트
14개의 동영상
Introduction1m
Popular datasets2m
Data pipelines58
Extract, transform, load3m
Versioning datasets2m
Looking at the notebook1m
Introduction43
Legacy API and Subsplits5m
Splits API (S3)2m
Introduction22
Legacy API in code1m
Splits API (S3) in code1m
Week 1 wrap up43
2개의 읽기 자료
Downloading the Coding Examples and Exercises10m
Try out the notebook yourself10m
1개 연습문제
Week 1 Quiz
2

2

완료하는 데 6시간 필요

Exporting your data into the training pipeline

완료하는 데 6시간 필요
21개 동영상 (총 44분), 5 개의 읽기 자료, 2 개의 테스트
21개의 동영상
Introduction22
Input data1m
Basic mechanics2m
Numeric and bucketized columns2m
Vocabulary and hashed columns, feature crossing2m
Embedding columns2m
Introduction24
Notebook walkthrough4m
Introduction19
Numpy, Pandas and Images2m
CSV3m
Text and TFRecord1m
Generators1m
Introduction17
Notebook walkthrough4m
Introduction1m
Numpy and Pandas2m
Images1m
CSV4m
Text2m
5개의 읽기 자료
Link to the notebook10m
Link to the CNN course10m
Link to the notebook10m
CSV: colab10m
Link to the tokenization10m
1개 연습문제
Week 2 Quiz
3

3

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Performance

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11개 동영상 (총 20분)
11개의 동영상
Introduction36
ETL2m
What happens when you train a model2m
Introduction25
Caching58
Parallelism APIs2m
Autotuning2m
Parallelizing data extraction2m
Best practices for code improvements3m
A few words by Laurence34
1개 연습문제
Week 3 Quiz
4

4

완료하는 데 5시간 필요

Publishing your datasets

완료하는 데 5시간 필요
11개 동영상 (총 24분), 2 개의 읽기 자료, 2 개의 테스트
11개의 동영상
Introduction44
How to start using a dataset2m
Implementation4m
File access and possible problems in data3m
Publishing the dataset3m
Introduction18
Going through the colab (1)2m
Going through the colab (2)2m
Closing words14
A conversation with Andrew Ng1m
2개의 읽기 자료
URLs10m
Link to the colab10m
1개 연습문제
Week 4 Quiz

검토

DATA PIPELINES WITH TENSORFLOW DATA SERVICES의 최상위 리뷰

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TensorFlow: Data and Deployment 특화 과정 정보

Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. This Specialization builds upon skills learned in the TensorFlow in Practice Specialization. We recommend learners complete that Specialization prior to enrolling in TensorFlow: Data and Deployment....
TensorFlow: Data and Deployment

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