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Heavy dry goods, the first time served
Neural Networks
Since the breakthrough in 2012 CNN's imagenet, neural network-based network began to rage deep learning academics and industry. Let's look at a picture, the number of google internal depth study on the project. And a very wide field of application, from Android to find drugs to youtube.
The amount of our past lives under review from the neural network together:
• 1958: Perceptron (linear model)
• 1969: Perceptron has limitation
• 1980s: Multi-layer perceptron
• Do not have significant difference from DNN today
• 1986: Backpropagation
• Usually more than 3 hidden layers is not helpful
• 1989: 1 hidden layer is “good enough”, why deep?
• 2006: RBM initialization (breakthrough)
• 2009: GPU
• 2011: Start to be popular in speech recognition
• 2012: win ILSVRC image competition
Deep learning is a branch of machine learning, to say what is currently the most important branch. Learn how to learn the depth of some of it?
But it is still crucial three steps:
1. Select the neural network
2. Define the quality of neural networks
3. Select the best set of parameters
The following is a diagram of neural networks:
And θ b are all within neurons
1. fully connected network (Fully Connection)
2. The depth of the network DEEP
Many layer depth =
Then someone will ask:
* 到底多少层深度合适？每层多个神经元？
答：这个看经验和实验的结果，不断调整。
* 结构能被自动设定吗？
答：可以通过进化网络实现。
* 我们能自己设计网络结构吗？
答： CNN 就是设计出来的网络结构。
3. 定义神经网络的好坏Loss
我们以minist 数字识别为例，一组数字识别为例
4. 选择最好的神经网络（找到参数集）
核心方法：
* Gradient Descent
* BackPropagation
深度学习基本知识点了解到了，但是为什么越Deep，效果会越好？ 以前都是做类比思考，比如电路模型，但是近期的lpaper上在理论上有严格的证明，我们后续博客会介绍
本专栏图片、公式很多来自台湾大学李弘毅老师、斯坦福大学cs229、cs231n 、斯坦福大学cs224n课程。在这里，感谢这些经典课程，向他们致敬！
作者简介：武强 兰州大学博士，谷歌全球开发专家Google Develop Expert（GDE Machine Learing 方向）
CSDN：https://me.csdn.net/dukuku5038
知乎：https://www.zhihu.com/people/Dr.Wu/activities
漫画人工智能公众号：DayuAI-Founder
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