2018.5.7

吳恩達(dá)深度學(xué)習(xí)視頻教程網(wǎng)址

網(wǎng)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(wǎng)易云上不提供測(cè)驗(yàn)和作業(yè)，Cousera上有。

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深度學(xué)習(xí)概論：

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本篇主要關(guān)于深度學(xué)習(xí)的一些介紹和幾個(gè)相關(guān)的術(shù)語

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Single/Multiple neural network:


深度學(xué)習(xí)的本質(zhì)： 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(jīng)網(wǎng)絡(luò)中常用的激活函數(shù)（activation function）

卷積神經(jīng)網(wǎng)絡(luò)： Convolution Neural Network (CNN) used often for image application

循環(huán)神經(jīng)網(wǎng)絡(luò)： Recurrent Neural Network (RNN) used for one-dimensional sequencedata such as translating English to Chinses or a temporal component such astext transcript.

結(jié)構(gòu)化數(shù)據(jù)： Structured data refers to things that has a defined meaning such as price, age

非結(jié)構(gòu)化數(shù)據(jù)： Unstructured data refers to thing like pixel, raw audio, text.

深度學(xué)習(xí)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(xùn)練神經(jīng)網(wǎng)絡(luò)是一個(gè)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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