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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이 강좌에 대하여
최근 조회 45,760회
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 6개 강좌 중 5번째 강좌:
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일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 12시간 필요
영어
자막: 영어
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 6개 강좌 중 5번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 12시간 필요
영어
자막: 영어
제공자:
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 4시간 필요
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
완료하는 데 4시간 필요
4개 동영상 (총 24분), 1 개의 읽기 자료, 4 개의 테스트
1개의 읽기 자료
Syllabus10m
1개 연습문제
Deep Neural Networks and TensorFlow30m
완료하는 데 2시간 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.
완료하는 데 2시간 필요
3개 동영상 (총 22분)
3개의 동영상
Convolutional Neural Networks (CNNs) for Classification4m
Convolutional Neural Networks (CNNs) Architecture13m
1개 연습문제
Convolutional Neural Networks30m
완료하는 데 3시간 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.
완료하는 데 3시간 필요
4개 동영상 (총 22분)
4개의 동영상
Recurrent Neural Networks (RNNs)5m
The Long Short Term Memory (LSTM) Model5m
Language Modelling7m
1개 연습문제
Recurrent Neural Networks30m
완료하는 데 2시간 필요
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 2시간 필요
2개 동영상 (총 10분)
1개 연습문제
Restricted Boltzmann Machines30m
검토
BUILDING DEEP LEARNING MODELS WITH TENSORFLOW의 최상위 리뷰
HJ 제공2020년 5월 17일
It would have been nice if the video tutorials would explain the code section as well, and if there would have been some in-depth teaching of the code part. But this course did benefit.
ZR 제공2020년 7월 2일
Deep Learning made me feel that there is a way to build models and classify data so easily and in a skillful way. Amazing course!
PD 제공2020년 1월 4일
Very clear explanation and well organized course. I give 4 stars because videos of Week 5 are missing the audio and subtitles.
DO 제공2020년 5월 26일
Not so often i wish a course would be longer and more in depth I really enjoyed using TF I'll look some other courses about it
IBM AI Engineering 전문 자격증 정보
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.
You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
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