吴恩达深度学习笔记1-Course1-Week1【深度学习概论】
2018.5.7
吴恩达深度学习****网址
网易云课堂:https://mooc.study.163.com/smartSpec/detail/1001319001.htm
Coursera:https://www.coursera.org/learn/neural-networks-deep-learning
PS:网易云上不提供测验和作业,Cousera上有。
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深度学习概论:
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本篇主要关于深度学习的一些介绍和几个相关的术语
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Single/Multiple neural network:
深度学习的本质:Given data (input and output) and fit a function that will predict output.
修正线性单元:(Rectified Linear Unit --ReLU)一种人工神经网络中常用的**函数(activation function)
卷积神经网络:Convolution Neural Network (CNN) used often for image application
循环神经网络:Recurrent Neural Network (RNN) used for one-dimensional sequencedata such as translating English to Chinses or a temporal component such astext transcript.
结构化数据:Structured data refers to things that has a defined meaning such as price, age
非结构化数据:Unstructured data refers to thing like pixel, raw audio, text.
深度学习三要素: large amount of data available with label, fast computation and neural network algorithm.
Two things have to be considered to get to the high level of performance:
1. Being able to train a big enough neural network
2. Huge amount of labeled data
训练神经网络是一个迭代过程:Idear--Code--Experiment
It could take a good amount of time to train a neural network, which affects your productivity. Faster computation helps to iterate and improve new algorithm.