Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
제공자:
TensorFlow: Data and Deployment 특화 과정deeplearning.ai이 강좌에 대하여
최근 조회 43,913회
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
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
제공자:
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 5시간 필요
Data Pipelines with TensorFlow Data Services
You'll learn about the types of data that you would normally come across when doing machine learning.
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
완료하는 데 6시간 필요
Exporting your data into the training pipeline
This week you’re going to start looking at the code for using the data with input pipelines!
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
완료하는 데 4시간 필요
Performance
How you load your data into your model can have a huge impact on how efficiently the model trains. You'll learn how to handle your data input to avoid bottlenecks, race conditions and more!
완료하는 데 4시간 필요
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
완료하는 데 5시간 필요
Publishing your datasets
This week let’s learn about how you can share your data with the world in a way that’s easy for others to consume!
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
YO 제공2020년 5월 16일
Debugging exercises due to errors in indentation sounded stupid in the first place. But the joy of finally getting a "yes" in the assignment auto-grader beats them all.
PC 제공2020년 4월 16일
I understand why most of the students are furious about, but content wise, it one of those extremely helpful and important courses in Coursera. Really loved it!
MI 제공2020년 4월 3일
This course is quite essential, yet it is prepared too quickly? I had a hard time on Week 3 and Week 4 because the exercises are hard to grasp.
GL 제공2020년 3월 2일
Laurence cares deeply about the students. Not only about what they learn, but that they actually enjoy and learn it. What a fantastic teacher.
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.
자주 묻는 질문
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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