2018.5.7

吳恩達深度學習視頻教程網址

網易云課堂：https://mooc.study.163.com/smartSpec/detail/1001319001.htm

Coursera：https://www.coursera.org/learn/neural-networks-deep-learning

PS：網易云上不提供測驗和作業，Cousera上有。

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深度學習概論：

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本篇主要關于深度學習的一些介紹和幾個相關的術語

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Single/Multiple neural network:


深度學習的本質： Given data (input and output) and fit a function that will predict output.

修正線性單元：（Rectified Linear Unit –ReLU)一種人工神經網絡中常用的激活函數（activation function）

卷積神經網絡： Convolution Neural Network (CNN) used often for image application

循環神經網絡： Recurrent Neural Network (RNN) used for one-dimensional sequencedata such as translating English to Chinses or a temporal component such astext transcript.

結構化數據： Structured data refers to things that has a defined meaning such as price, age

非結構化數據： Unstructured data refers to thing like pixel, raw audio, text.

深度學習三要素： 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

訓練神經網絡是一個迭代過程： 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.

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