Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
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귀하가 습득할 기술
Deep LearningArtificial Neural NetworkArtificial Intelligence (AI)Machine Learningkeras
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 6개 강좌 중 3번째 강좌:
유동적 마감일
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중급 단계
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자막: 영어
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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 2시간 필요
Introduction to Neural Networks and Deep Learning
In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Finally, you will learn about how neural networks feed data forward through the network.
완료하는 데 2시간 필요
4개 동영상 (총 17분), 1 개의 읽기 자료, 2 개의 테스트
4개의 동영상
Welcome2m
Introduction to Deep Learning4m
Neurons and Neural Networks3m
Artificial Neural Networks5m
1개의 읽기 자료
Syllabus10m
1개 연습문제
Introduction to Neural Networks and Deep Learning30m
완료하는 데 1시간 필요
Artificial Neural Networks
In this module, you will learn about the gradient descent algorithm and how variables are optimized with respect to a defined function. You will also learn about backpropagation and how neural networks learn and update their weights and biases. Futhermore, you will learn about the vanishing gradient problem. Finally, you will learn about activation functions.
완료하는 데 1시간 필요
4개 동영상 (총 22분)
1개 연습문제
Artificial Neural Networks30m
완료하는 데 3시간 필요
Keras and Deep Learning Libraries
In this module, you will learn about the diifferent deep learning libraries namely, Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.
완료하는 데 3시간 필요
3개 동영상 (총 16분)
1개 연습문제
Keras and Deep Learning Libraries30m
완료하는 데 2시간 필요
Deep Learning Models
In this module, you will learn about the difference between the shallow and deep neural networks. You will also learn about convolutional networks and how to build them using the Keras library. Finally, you will also learn about recurrent neural networks and autoencoders.
완료하는 데 2시간 필요
4개 동영상 (총 17분)
4개의 동영상
Convolutional Neural Networks8m
Recurrent Neural Networks2m
Autoencoders2m
1개 연습문제
Deep Learning Models30m
검토
INTRODUCTION TO DEEP LEARNING & NEURAL NETWORKS WITH KERAS의 최상위 리뷰
AM 제공2020년 6월 24일
Good course. It is a very direct approach. It is a basic introduction to keras. Doing the labs is recommended, and also previous knowledge about machine learning is encouraged.
AS 제공2020년 3월 19일
A good course. Could be better if it was explained how to select the optimal number of layers and nodes. This was not covered and explained anywhere. Overall it was good.
FB 제공2020년 3월 24일
It is a really good course. The labs could have had more for us to do, much of the labs was already implemented.\n\nStill, great introduction to the proposed subjects.
MC 제공2020년 2월 19일
Best suited for beginner in Deep learning with Keras. Good content, hands on experience with Jupiter notebook with IBM Developer tool. Worked Exercise code for CNN & RNN.
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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- 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.
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