机器学习的神经网络

Neural Networks for Machine Learning

This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform.

多伦多大学

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  • 分类: 计算机
  • 平台: Coursera
  • 语言: 英语

关于此课程: Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform.

Please be advised that the course is suited for an intermediate level learner – comfortable with calculus and with experience programming (Python).

授课大纲

WEEK 1
Introduction
Introduction to the course – machine learning and neural nets

WEEK 2
The Perceptron learning procedure
An overview of the main types of neural network architecture

WEEK 3
The backpropagation learning proccedure
Learning the weights of a linear neuron

WEEK 4
Learning feature vectors for words
Learning to predict the next word

WEEK 5
Object recognition with neural nets
In this module we look at why object recognition is difficult.

WEEK 6
Optimization: How to make the learning go faster
We delve into mini-batch gradient descent as well as discuss adaptive learning rates.

WEEK 7
Recurrent neural networks
This module explores training recurrent neural networks

WEEK 8
More recurrent neural networks
We continue our look at recurrent neural networks

WEEK 9
Ways to make neural networks generalize better
We discuss strategies to make neural networks generalize better

WEEK 10
Combining multiple neural networks to improve generalization
This module we look at why it helps to combine multiple neural networks to improve generalization

WEEK 11
Hopfield nets and Boltzmann machines

WEEK 12
Restricted Boltzmann machines (RBMs)
This module deals with Boltzmann machine learning

WEEK 13
Stacking RBMs to make Deep Belief Nets

WEEK 14
Deep neural nets with generative pre-training

WEEK 15
Modeling hierarchical structure with neural nets

WEEK 16
Recent applications of deep neural nets

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