This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with TensorFlow, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
제공자:
6,998명이 이미 등록했습니다!
듀크대학교
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 8시간 필요
Simple Introduction to Machine Learning
The focus of this module is to introduce the concepts of machine learning with as little mathematics as possible. We will introduce basic concepts in machine learning, including logistic regression, a simple but widely employed machine learning (ML) method. Also covered is multilayered perceptron (MLP), a fundamental neural network. The concept of deep learning is discussed, and also related to simpler models.
23 videos (Total 164 min), 1 reading, 10 quizzes
23개의 동영상
What Is Machine Learning?5m
Logistic Regression9m
Interpretation of Logistic Regression9m
Motivation for Multilayer Perceptron4m
Multilayer Perceptron Concepts5m
Multilayer Perceptron Math Model6m
Deep Learning6m
Example: Document Analysis3m
Interpretation of Multilayer Perceptron9m
Transfer Learning5m
Model Selection7m
Early History of Neural Networks14m
Hierarchical Structure of Images6m
Convolution Filters9m
Convolutional Neural Network3m
CNN Math Model6m
How the Model Learns8m
Advantages of Hierarchical Features4m
CNN on Real Images9m
Applications in Use and Practice10m
Deep Learning and Transfer Learning7m
Introduction to TensorFlow3m
1개의 읽기 자료
Math for Data Science10m
10개 연습문제
Intro to Machine Learning8m
Logistic Regression8m
Multilayer Perceptron8m
Deep Learning8m
Model Selection8m
History of Neural Networks8m
CNN Concepts10m
CNN Math Model4m
Applications In Use and Practice
Week 1 Comprehensive
주
2완료하는 데 3시간 필요
Basics of Model Learning
In this module we will be discussing the mathematical basis of learning deep networks. We’ll first work through how we define the issue of learning deep networks as a minimization problem of a mathematical function. After defining our mathematical goal, we will introduce validation methods to estimate real-world performance of the learned deep networks. We will then discuss how gradient descent, a classical technique in optimization, can be used to achieve this mathematical goal. Finally, we will discuss both why and how stochastic gradient descent is used in practice to learn deep networks.
6 videos (Total 44 min), 3 quizzes
6개의 동영상
How Do We Evaluate Our Networks?12m
How Do We Learn Our Network?7m
How Do We Handle Big Data?10m
Early Stopping2m
Model Learning with TensorFlow49
3개 연습문제
Lesson One10m
Lesson 210m
Week 2 Comprehensive
주
3완료하는 데 3시간 필요
Image Analysis with Convolutional Neural Networks
This week will cover model training, as well as transfer learning and fine-tuning. In addition to learning the fundamentals of a CNN and how it is applied, careful discussion is provided on the intuition of the CNN, with the goal of providing a conceptual understanding.
8 videos (Total 45 min), 4 quizzes
8개의 동영상
Breakdown of the Convolution (1D and 2D)8m
Core Components of the Convolutional Layer7m
Activation Functions4m
Pooling and Fully Connected Layers4m
Training the Network6m
Transfer Learning and Fine-Tuning4m
CNN with TensorFlow52
4개 연습문제
Lesson One10m
Lesson 210m
Lesson 36m
Week 3 Comprehensive
주
4완료하는 데 4시간 필요
Introduction to Natural Language Processing
This week will cover the application of neural networks to natural language processing (NLP), from simple neural models to the more complex. The fundamental concept of word embeddings is discussed, as well as how such methods are employed within model learning and usage for several NLP applications. A wide range of neural NLP models are also discussed, including recurrent neural networks, and specifically long short-term memory (LSTM) models.
13 videos (Total 136 min), 4 quizzes
13개의 동영상
Words to Vectors7m
Example of Word Embeddings11m
Neural Model of Text14m
The Softmax Function7m
Methods for Learning Model Parameters9m
More Details on How to Learn Model Parameters6m
The Recurrent Neural Network11m
Long Short-Term Memory20m
Long Short-Term Memory Review11m
Use of LSTM for Text Synthesis9m
Simple and Effective Alternative Methods for Neural NLP15m
Natural Language Processing with TensorFlow41
4개 연습문제
Lesson 12m
Lesson 22m
Lesson 32m
Week 4 Comprehensive30m
4.7
16개의 리뷰최상위 리뷰
I like this introductory course, very good one to start to learn machine learning. I will definitely continue studying and re-watch the videos.
This was a really great course for understanding the basics of machine learning through a lot of simple but relevant, real world examples.
강사
Lawrence Carin
James L. Meriam Professor of Electrical and Computer EngineeringElectrical and Computer Engineering
듀크대학교 정보
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
이 수료증을 구매하면 무엇을 이용할 수 있나요?
수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
Coursera는 세계 최고의 교육에 대한 보편적인 접근 기회를 제공하며,
최고의 대학 및 기관들과 제휴하여 온라인 강좌를 제공합니다.
© 2019 Coursera Inc. All rights reserved.
