The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
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영어
자막: 프랑스어, 포르투갈어 (유럽), 러시아어, 영어, 스페인어
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
중급 단계
완료하는 데 약 7시간 필요
영어
자막: 프랑스어, 포르투갈어 (유럽), 러시아어, 영어, 스페인어
제공자:
IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
강의 계획표 - 이 강좌에서 배울 내용
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Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
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5개 동영상 (총 28분), 2 개의 읽기 자료, 5 개의 테스트
완료하는 데 1시간 필요
Supervised Learning Models
In this module, you will learn about about Convolutional Neural Networks, and the building blocks of a convolutional neural network, such as convolution and feature learning. You will also learn about the popular MNIST database. Finally, you will learn how to build a Multi-layer perceptron and convolutional neural networks in Python and using TensorFlow.
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3개 동영상 (총 22분)
완료하는 데 2시간 필요
Supervised Learning Models (Cont'd)
In this module, you will learn about the recurrent neural network model, and special type of a recurrent neural network, which is the Long Short-Term Memory model. Also, you will learn about the Recursive Neural Tensor Network theory, and finally, you will apply recurrent neural networks to language modelling.
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4개 동영상 (총 22분)
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Unsupervised Deep Learning Models
In this module, you will learn about the applications of unsupervised learning. You will learn about Restricted Boltzmann Machines (RBMs), and how to train an RBM. Finally, you will apply Restricted Boltzmann Machines to build a recommendation system.
완료하는 데 1시간 필요
2개 동영상 (총 10분)
검토
- 5 stars60.46%
- 4 stars24.46%
- 3 stars9.23%
- 2 stars2.92%
- 1 star2.92%
BUILDING DEEP LEARNING MODELS WITH TENSORFLOW의 최상위 리뷰
VV 제공2021년 3월 4일
This course is the best out of all courses in the specialization, the pace of the speaker was perfect.
XH 제공2020년 6월 29일
The course concepts are not in-depth enough, and the server for Jupyter notebook running is way too slow...
PC 제공2020년 3월 31일
The coding part was hard to understand. If that part could also be covered in videos as a tutorial.
RC 제공2020년 5월 14일
Good an simple videos to understand the concept. The notebooks are very detailed and give a second layer of knowledge with practical example
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