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Aprox. 14 horas para completar

Sugerido: 4 weeks of study, 2-5 hours/week...

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Legendas: Inglês

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 14 horas para completar

Sugerido: 4 weeks of study, 2-5 hours/week...

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
8 horas para concluir

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 vídeos (total de (Total 164 mín.) min), 1 leitura, 10 testes
23 videos
What Is Machine Learning?5min
Logistic Regression9min
Interpretation of Logistic Regression9min
Motivation for Multilayer Perceptron4min
Multilayer Perceptron Concepts5min
Multilayer Perceptron Math Model6min
Deep Learning6min
Example: Document Analysis3min
Interpretation of Multilayer Perceptron9min
Transfer Learning5min
Model Selection7min
Early History of Neural Networks14min
Hierarchical Structure of Images6min
Convolution Filters9min
Convolutional Neural Network3min
CNN Math Model6min
How the Model Learns8min
Advantages of Hierarchical Features4min
CNN on Real Images9min
Applications in Use and Practice10min
Deep Learning and Transfer Learning7min
Introduction to TensorFlow3min
1 leituras
Math for Data Science10min
10 exercícios práticos
Intro to Machine Learning8min
Logistic Regression8min
Multilayer Perceptron8min
Deep Learning8min
Model Selection8min
History of Neural Networks8min
CNN Concepts10min
CNN Math Model4min
Applications In Use and Practice
Week 1 Comprehensive
Semana
2
3 horas para concluir

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 vídeos (total de (Total 44 mín.) min), 3 testes
6 videos
How Do We Evaluate Our Networks?12min
How Do We Learn Our Network?7min
How Do We Handle Big Data?10min
Early Stopping2min
Model Learning with TensorFlow49s
3 exercícios práticos
Lesson One10min
Lesson 210min
Week 2 Comprehensive
Semana
3
3 horas para concluir

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 vídeos (total de (Total 45 mín.) min), 4 testes
8 videos
Breakdown of the Convolution (1D and 2D)8min
Core Components of the Convolutional Layer7min
Activation Functions4min
Pooling and Fully Connected Layers4min
Training the Network6min
Transfer Learning and Fine-Tuning4min
CNN with TensorFlow52s
4 exercícios práticos
Lesson One10min
Lesson 210min
Lesson 36min
Week 3 Comprehensive
Semana
4
4 horas para concluir

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 vídeos (total de (Total 136 mín.) min), 4 testes
13 videos
Words to Vectors7min
Example of Word Embeddings11min
Neural Model of Text14min
The Softmax Function7min
Methods for Learning Model Parameters9min
More Details on How to Learn Model Parameters6min
The Recurrent Neural Network11min
Long Short-Term Memory20min
Long Short-Term Memory Review11min
Use of LSTM for Text Synthesis9min
Simple and Effective Alternative Methods for Neural NLP15min
Natural Language Processing with TensorFlow41s
4 exercícios práticos
Lesson 12min
Lesson 22min
Lesson 32min
Week 4 Comprehensive30min
4.7
16 avaliaçõesChevron Right

Melhores avaliações

por AUNov 12th 2018

I like this introductory course, very good one to start to learn machine learning. I will definitely continue studying and re-watch the videos.

por EROct 5th 2018

This was a really great course for understanding the basics of machine learning through a lot of simple but relevant, real world examples.

Instrutores

Avatar

Lawrence Carin

James L. Meriam Professor of Electrical and Computer Engineering
Electrical and Computer Engineering

Sobre Universidade Duke

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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