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
教学大纲 - 您将从这门课程中学到什么
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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 个视频（共 164 分钟）, 1 个阅读材料, 14 个测验
23 个视频
What Is Machine Learning?5分钟
Logistic Regression9分钟
Interpretation of Logistic Regression9分钟
Motivation for Multilayer Perceptron4分钟
Multilayer Perceptron Concepts5分钟
Multilayer Perceptron Math Model6分钟
Deep Learning6分钟
Example: Document Analysis3分钟
Interpretation of Multilayer Perceptron9分钟
Transfer Learning5分钟
Model Selection7分钟
Early History of Neural Networks14分钟
Hierarchical Structure of Images6分钟
Convolution Filters9分钟
Convolutional Neural Network3分钟
CNN Math Model6分钟
How the Model Learns8分钟
Advantages of Hierarchical Features4分钟
CNN on Real Images9分钟
Applications in Use and Practice10分钟
Deep Learning and Transfer Learning7分钟
Introduction to TensorFlow3分钟
1 个阅读材料
Math for Data Science10分钟
10 个练习
Intro to Machine Learning8分钟
Logistic Regression8分钟
Multilayer Perceptron8分钟
Deep Learning8分钟
Model Selection8分钟
History of Neural Networks8分钟
CNN Concepts10分钟
CNN Math Model4分钟
Applications In Use and Practice分钟
Week 1 Comprehensive分钟
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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 个视频（共 44 分钟）, 5 个测验
6 个视频
How Do We Evaluate Our Networks?12分钟
How Do We Learn Our Network?7分钟
How Do We Handle Big Data?10分钟
Early Stopping2分钟
Model Learning with TensorFlow分钟
3 个练习
Lesson One10分钟
Lesson 210分钟
Week 2 Comprehensive分钟
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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 个视频（共 45 分钟）, 6 个测验
8 个视频
Breakdown of the Convolution (1D and 2D)8分钟
Core Components of the Convolutional Layer7分钟
Activation Functions4分钟
Pooling and Fully Connected Layers4分钟
Training the Network6分钟
Transfer Learning and Fine-Tuning4分钟
CNN with TensorFlow分钟
4 个练习
Lesson One10分钟
Lesson 210分钟
Lesson 36分钟
Week 3 Comprehensive分钟
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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 个视频（共 136 分钟）, 5 个测验
13 个视频
Words to Vectors7分钟
Example of Word Embeddings11分钟
Neural Model of Text14分钟
The Softmax Function7分钟
Methods for Learning Model Parameters9分钟
More Details on How to Learn Model Parameters6分钟
The Recurrent Neural Network11分钟
Long Short-Term Memory20分钟
Long Short-Term Memory Review11分钟
Use of LSTM for Text Synthesis9分钟
Simple and Effective Alternative Methods for Neural NLP15分钟
Natural Language Processing with TensorFlow分钟
4 个练习
Lesson 12分钟
Lesson 22分钟
Lesson 32分钟
Week 4 Comprehensive30分钟
讲师
Lawrence Carin
James L. Meriam Professor of Electrical and Computer EngineeringElectrical and Computer Engineering
关于 Duke University
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