# ICPS 2015上认知语言学之父George Lakoff的演讲全文

Most thought is unconscious, and the usual estimate is around 98 percent. But if you believe the work that Stan Dehaene talked about the other night, it is more than 98 percent. Consciousness is the tip of the iceberg of thought. It is there that things are put together in an interesting way and the interesting way is the following, that before consciousness, what happens is that your brain unconsciously changes what you perceive or what you think. This is something remarkable. I think one of the best papers I heard on this was by Shin Shimojo who is a vision scientist at the Caltech. He came to Berkeley a couple of months ago and gave a truly remarkable overview of experiments that showed this, many of them which were his. Let me give you a sense of this. Suppose you know that if there are flashing lights and they are going along and they are going fast enough, they look like a single stream. Read more

1.1 引言
1.2 基本术语
1.3 假设空间
1.4 归纳偏好
1.5 发展历程
1.6 应用现状

# 变分自编码器（Variational Autoencoder, VAE）通俗教程

### 1. 神秘变量与数据集

$$z = \left( \begin{array}{c} z_1\\ z_2\\ \vdots\\ z_n \end{array} \right)$$

z也起个名字叫神秘组合

# Deep Learning Tutorial 深度学习教程翻译

### 前置阅读

Learning Deep Architectures for AI (Foundations & Trends in Machine Learning, 2009).

Theano basic tutorial

### 正式教程

1. Logistic Regression – 简单使用Theano > 前往
2. Multilayer perceptron – 介绍layer >前往
3. Deep Convolutional Network – LeNet5的简化版本 >前往

• Auto Encoders, Denoising Autoencoders， 自编码器，去噪自编码器 – 自编码器描述 >前往
• Stacked Denoising Auto-Encoders，堆栈式自编码器 – 进行深度网络无监督预训练的简单步骤 >前往
• Restricted Boltzmann Machines，受限玻尔兹曼机 -单层生成式RBM模型
• Deep Belief Networks – 深度信念网络 -先进行栈式RBMs的无监督生成式预训练再进行有监督微调

• HMC Sampling，混合蒙特卡罗采样 -混合（又名汉密尔顿）蒙特卡洛采样 scan()

• Contractive auto-encoders code，收缩自编码器代码 – 代码中有基础文档

• Semantic Parsing of Speech using Recurrent Net

• LSTM network

• Modeling and generating sequences of polyphonic music，和弦音乐序列的建模与生成