Acerca de este curso: 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).
Para quién es esta clase: 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.
Creada por: Duke University
Enseñado por: Lawrence Carin , James L. Meriam Professor of Electrical and Computer Engineering
Electrical and Computer Engineering
| Nivel | Intermedio |
| Compromiso | 4 weeks of study, 2-5 hours/week |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 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 videos, 1 lectura, 9 cuestionarios de práctica
- Vídeo: What Is Machine Learning?
- Тренировочный тест: Intro to Machine Learning
- Vídeo: Logistic Regression
- Vídeo: Interpretation of Logistic Regression
- Материал для самостоятельного изучения: Math for Data Science
- Vídeo: Motivation for Multilayer Perceptron
- Тренировочный тест: Logistic Regression
- Элемент LTI: Jupyter Notebook: Python Prerequisites
- Vídeo: Multilayer Perceptron Concepts
- Vídeo: Multilayer Perceptron Math Model
- Тренировочный тест: Multilayer Perceptron
- Vídeo: Deep Learning
- Vídeo: Example: Document Analysis
- Vídeo: Interpretation of Multilayer Perceptron
- Vídeo: Transfer Learning
- Тренировочный тест: Deep Learning
- Vídeo: Model Selection
- Тренировочный тест: Model Selection
- Vídeo: Early History of Neural Networks
- Vídeo: Modern History of Neural Networks
- Тренировочный тест: History of Neural Networks
- Vídeo: Hierarchical Structure of Images
- Vídeo: Convolution Filters
- Vídeo: Convolutional Neural Network
- Тренировочный тест: CNN Concepts
- Vídeo: CNN Math Model
- Vídeo: How the Model Learns
- Vídeo: Advantages of Hierarchical Features
- Тренировочный тест: CNN Math Model
- Vídeo: CNN on Real Images
- Vídeo: Applications in Use and Practice
- Vídeo: Deep Learning and Transfer Learning
- Тренировочный тест: Applications In Use and Practice
- Элемент LTI: Jupyter Notebook: Installing TensorFlow
- Vídeo: Introduction to TensorFlow
- Элемент LTI: Jupyter Notebook: Overview of Coding Envrionment
- Элемент LTI: Jupyter Notebook: TensorFlow Basics
Calificado: Week 1 Comprehensive
SEMANA 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 videos, 2 cuestionarios de práctica
- Vídeo: How Do We Evaluate Our Networks?
- Тренировочный тест: Lesson One
- Vídeo: How Do We Learn Our Network?
- Vídeo: How Do We Handle Big Data?
- Vídeo: Early Stopping
- Тренировочный тест: Lesson 2
- Vídeo: Model Learning with TensorFlow
- Элемент LTI: Jupyter Notebook: Logistic Regression
- Элемент LTI: Jupyter Notebook: Multilayer Perceptron Assignment
Calificado: Week 2 Comprehensive
SEMANA 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 videos, 3 cuestionarios de práctica
- Vídeo: Breakdown of the Convolution (1D and 2D)
- Тренировочный тест: Lesson One
- Vídeo: Core Components of the Convolutional Layer
- Vídeo: Activation Functions
- Vídeo: Pooling and Fully Connected Layers
- Тренировочный тест: Lesson 2
- Vídeo: Training the Network
- Vídeo: Transfer Learning and Fine-Tuning
- Тренировочный тест: Lesson 3
- Vídeo: CNN with TensorFlow
- Элемент LTI: Jupyter Notebook: CNNs
- Элемент LTI: Jupyter Notebook: CNN Assignment
Calificado: Week 3 Comprehensive
SEMANA 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 videos, 3 cuestionarios de práctica
- Vídeo: Words to Vectors
- Vídeo: Example of Word Embeddings
- Тренировочный тест: Lesson 1
- Vídeo: Neural Model of Text
- Vídeo: The Softmax Function
- Vídeo: Methods for Learning Model Parameters
- Vídeo: More Details on How to Learn Model Parameters
- Тренировочный тест: Lesson 2
- Vídeo: The Recurrent Neural Network
- Vídeo: Long Short-Term Memory
- Vídeo: Long Short-Term Memory Review
- Vídeo: Use of LSTM for Text Synthesis
- Тренировочный тест: Lesson 3
- Vídeo: Simple and Effective Alternative Methods for Neural NLP
- Vídeo: Natural Language Processing with TensorFlow
- Элемент LTI: Jupyter Notebook: NLP
Calificado: Week 4 Comprehensive
Preguntas Frecuentes
Cómo funciona
Задания курса
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Creadores
Duke University
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Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
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SK
Very Good course explaining the theoretical concepts related to deep learning . Thank you