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 upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
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TensorFlow in Practice 특화 과정deeplearning.ai이 강좌에 대하여
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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 30시간 필요
영어
자막: 영어, 스페인어, 러시아어, 일본어
배울 내용
Learn best practices for using TensorFlow, a popular open-source machine learning framework
Build a basic neural network in TensorFlow
Train a neural network for a computer vision application
Understand how to use convolutions to improve your neural network
귀하가 습득할 기술
Computer VisionTensorflowMachine Learning
학습자 경력 결과
40%
가 이 강좌를 수료한 후 새로운 커리어를 시작함
43%
가 이 강좌를 통해 확실한 경력상 이점을 얻음
12%
가 급여 인상 또는 승진 성취
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 1번째 강좌:
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 30시간 필요
영어
자막: 영어, 스페인어, 러시아어, 일본어
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 6시간 필요
A New Programming Paradigm
Welcome to this course on going from Basics to Mastery of TensorFlow. We're excited you're here! In week 1 you'll get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along.
완료하는 데 6시간 필요
4개 동영상 (총 16분), 5 개의 읽기 자료, 3 개의 테스트
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
완료하는 데 7시간 필요
Introduction to Computer Vision
Welcome to week 2 of the course! In week 1 you learned all about how Machine Learning and Deep Learning is a new programming paradigm. This week you’re going to take that to the next level by beginning to solve problems of computer vision with just a few lines of code!
완료하는 데 7시간 필요
7개 동영상 (총 15분), 6 개의 읽기 자료, 3 개의 테스트
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 vision1시간
See how to implement Callbacks10m
Week 2 Resources10m
1개 연습문제
Week 2 Quiz
완료하는 데 8시간 필요
Enhancing Vision with Convolutional Neural Networks
Welcome to week 3! In week 2 you saw a basic Neural Network for Computer Vision. It did the job nicely, but it was a little naive in its approach. This week we’ll see how to make it better, as discussed by Laurence and Andrew here.
완료하는 데 8시간 필요
6개 동영상 (총 19분), 6 개의 읽기 자료, 3 개의 테스트
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 yourself1시간
Experiment with filters and pools1시간
Week 3 Resources10m
1개 연습문제
Week 3 Quiz
완료하는 데 9시간 필요
Using Real-world Images
Last week you saw how to improve the results from your deep neural network using convolutions. It was a good start, but the data you used was very basic. What happens when your images are larger, or if the features aren’t always in the same place? Andrew and Laurence discuss this to prepare you for what you’ll learn this week: handling complex images!
완료하는 데 9시간 필요
9개 동영상 (총 27분), 10 개의 읽기 자료, 3 개의 테스트
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 classifier1시간
Get hands-on and use validation30m
Get Hands-on with compacted images30m
Week 4 Resources10m
Wrap up10m
1개 연습문제
Week 4 Quiz
검토
INTRODUCTION TO TENSORFLOW FOR ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING의 최상위 리뷰
AS 제공2019년 3월 8일
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?
JJ 제공2019년 4월 27일
Very well organized. Good speakers. Content is comprehensive for a Introductory Course. A little more explanation on Validation versus Testing and on some of the evaluation functions would be helpful.
RD 제공2019년 8월 13일
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.
MG 제공2020년 3월 26일
this course was helpful for me to learn more about CNN. it starts from basic concepts and improve that slowly and step by step. i truthfully recommend how what to start working with neural networks.
TensorFlow in Practice 특화 과정 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.
Looking for more advanced TensorFlow content? Check out the new TensorFlow: Data and Deployment Specialization.
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What will I get if I subscribe to this Specialization?
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