Об этом курсе: 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).
Для кого этот курс: This course is for anyone interested in learning the basics of machine learning (ML), and on how it is applied in multiple settings. You will need some experience programming in Python as well as some basic math skills to succeed in this course. Duke's Data Science Math Skills provides a solid foundation in math for data science: https://www.coursera.org/learn/datasciencemathskills/. And there are many great introductory Python courses online.
Автор: Duke University
Преподаватели: Lawrence Carin , James L. Meriam Professor of Electrical and Computer Engineering
Electrical and Computer Engineering
| Уровень | Учащийся среднего уровня |
| Выполнение | 4 weeks of study, 2-5 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
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.
24 видео, 1 материал для самостоятельного изучения, 9 тренировочных теста
- Видео: What Is Machine Learning?
- Practice Quiz: Intro to Machine Learning
- Видео: Logistic Regression
- Видео: Interpretation of Logistic Regression
- Reading: Math for Data Science
- Видео: Motivation for Multilayer Perceptron
- Practice Quiz: Logistic Regression
- LTI Item: Jupyter Notebook: Python Prerequisites
- Видео: Multilayer Perceptron Concepts
- Видео: Multilayer Perceptron Math Model
- Practice Quiz: Multilayer Perceptron
- Видео: Deep Learning
- Видео: Example: Document Analysis
- Видео: Interpretation of Multilayer Perceptron
- Видео: Transfer Learning
- Practice Quiz: Deep Learning
- Видео: Model Selection
- Practice Quiz: Model Selection
- Видео: Early History of Neural Networks
- Видео: Modern History of Neural Networks
- Practice Quiz: History of Neural Networks
- Видео: Hierarchical Structure of Images
- Видео: Convolution Filters
- Видео: Convolutional Neural Network
- Practice Quiz: CNN Concepts
- Видео: CNN Math Model
- Видео: How the Model Learns
- Видео: Advantages of Hierarchical Features
- Practice Quiz: CNN Math Model
- Видео: CNN on Real Images
- Видео: Applications in Use and Practice
- Видео: Deep Learning and Transfer Learning
- Practice Quiz: Applications In Use and Practice
- LTI Item: Jupyter Notebook: Installing TensorFlow
- Видео: Introduction to TensorFlow
- LTI Item: Jupyter Notebook: Overview of Coding Envrionment
- LTI Item: Jupyter Notebook: TensorFlow Basics
Оцениваемый: Week 1 Comprehensive
НЕДЕЛЯ 2
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 видео, 2 тренировочных теста
- Видео: How Do We Evaluate Our Networks?
- Practice Quiz: Lesson One
- Видео: How Do We Learn Our Network?
- Видео: How Do We Handle Big Data?
- Видео: Early Stopping
- Practice Quiz: Lesson 2
- Видео: Model Learning with TensorFlow
- LTI Item: Jupyter Notebook: Logistic Regression
- LTI Item: Jupyter Notebook: Multilayer Perceptron Assignment
Оцениваемый: Week 2 Comprehensive
НЕДЕЛЯ 3
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 видео, 3 тренировочных теста
- Видео: Breakdown of the Convolution (1D and 2D)
- Practice Quiz: Lesson One
- Видео: Core Components of the Convolutional Layer
- Видео: Activation Functions
- Видео: Pooling and Fully Connected Layers
- Practice Quiz: Lesson 2
- Видео: Training the Network
- Видео: Transfer Learning and Fine-Tuning
- Practice Quiz: Lesson 3
- Видео: CNN with TensorFlow
- LTI Item: Jupyter Notebook: CNNs
- LTI Item: Jupyter Notebook: CNN Assignment
Оцениваемый: Week 3 Comprehensive
НЕДЕЛЯ 4
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 видео, 3 тренировочных теста
- Видео: Words to Vectors
- Видео: Example of Word Embeddings
- Practice Quiz: Lesson 1
- Видео: Neural Model of Text
- Видео: The Softmax Function
- Видео: Methods for Learning Model Parameters
- Видео: More Details on How to Learn Model Parameters
- Practice Quiz: Lesson 2
- Видео: The Recurrent Neural Network
- Видео: Long Short-Term Memory
- Видео: Long Short-Term Memory Review
- Видео: Use of LSTM for Text Synthesis
- Practice Quiz: Lesson 3
- Видео: Simple and Effective Alternative Methods for Neural NLP
- Видео: Natural Language Processing with TensorFlow
- LTI Item: Jupyter Notebook: NLP
Оцениваемый: Week 4 Comprehensive
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Авторы
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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SK
Very Good course explaining the theoretical concepts related to deep learning . Thank you