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).
Introduction to Machine Learning
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Introduction to Machine Learning
Duke University
Программа курса: что вы изучите
Неделя
1Simple 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 видео (всего 164 мин.), 1 материал для самостоятельного изучения, 14 тестов
23 видео
What Is Machine Learning?5мин
Logistic Regression9мин
Interpretation of Logistic Regression9мин
Motivation for Multilayer Perceptron4мин
Multilayer Perceptron Concepts5мин
Multilayer Perceptron Math Model6мин
Deep Learning6мин
Example: Document Analysis3мин
Interpretation of Multilayer Perceptron9мин
Transfer Learning5мин
Model Selection7мин
Early History of Neural Networks14мин
Hierarchical Structure of Images6мин
Convolution Filters9мин
Convolutional Neural Network3мин
CNN Math Model6мин
How the Model Learns8мин
Advantages of Hierarchical Features4мин
CNN on Real Images9мин
Applications in Use and Practice10мин
Deep Learning and Transfer Learning7мин
Introduction to TensorFlow3мин
1 материал для самостоятельного изучения
Math for Data Science10мин
10 практического упражнения
Intro to Machine Learning8мин
Logistic Regression8мин
Multilayer Perceptron8мин
Deep Learning8мин
Model Selection8мин
History of Neural Networks8мин
CNN Concepts10мин
CNN Math Model4мин
Applications In Use and Practiceмин
Week 1 Comprehensiveмин
Неделя
2Basics 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 видео (всего 44 мин.), 5 тестов
6 видео
How Do We Evaluate Our Networks?12мин
How Do We Learn Our Network?7мин
How Do We Handle Big Data?10мин
Early Stopping2мин
Model Learning with TensorFlowмин
3 практического упражнения
Lesson One10мин
Lesson 210мин
Week 2 Comprehensiveмин
Неделя
3Image 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 видео (всего 45 мин.), 6 тестов
8 видео
Breakdown of the Convolution (1D and 2D)8мин
Core Components of the Convolutional Layer7мин
Activation Functions4мин
Pooling and Fully Connected Layers4мин
Training the Network6мин
Transfer Learning and Fine-Tuning4мин
CNN with TensorFlowмин
4 практического упражнения
Lesson One10мин
Lesson 210мин
Lesson 36мин
Week 3 Comprehensiveмин
Неделя
4Introduction 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 видео (всего 136 мин.), 5 тестов
13 видео
Words to Vectors7мин
Example of Word Embeddings11мин
Neural Model of Text14мин
The Softmax Function7мин
Methods for Learning Model Parameters9мин
More Details on How to Learn Model Parameters6мин
The Recurrent Neural Network11мин
Long Short-Term Memory20мин
Long Short-Term Memory Review11мин
Use of LSTM for Text Synthesis9мин
Simple and Effective Alternative Methods for Neural NLP15мин
Natural Language Processing with TensorFlowмин
4 практического упражнения
Lesson 12мин
Lesson 22мин
Lesson 32мин
Week 4 Comprehensive30мин
Преподаватель
Lawrence Carin
James L. Meriam Professor of Electrical and Computer EngineeringElectrical and Computer Engineering
О Duke University
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.
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