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添加了线性代数和THEANO的卷积神经网络部分，修复了第一节显示的问题
Former-commit-id: 166b729
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‎01. python tools/01.01 python overview.ipynb

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‎04. scipy/04.09 linear algbra.ipynb

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‎04. scipy/04.10 sparse linear algebra.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 稀疏矩阵的线性代数"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"对于稀疏矩阵来说，其线性代数操作可以使用 `scipy.sparse.linalg` 实现："
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import scipy.sparse.linalg"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 矩阵操作"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- `scipy.sparse.linalg.inv`\n",
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" - 稀疏矩阵求逆\n",
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"- `scipy.sparse.linalg.expm`\n",
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" - 求稀疏矩阵的指数函数"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 矩阵范数"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- `scipy.sparse.linalg.norm`\n",
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" - 稀疏矩阵求范数"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 线性方程组求解"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"提供了一系列求解方法：\n",
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"http://docs.scipy.org/doc/scipy/reference/sparse.linalg.html#solving-linear-problems\n",
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"\n",
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"主要使用的是迭代方法求解。"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 特征值分解和奇异值分解"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"对于特别大的矩阵，原来的方法可能需要太大的内存，考虑使用这两个方法替代：\n",
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"\n",
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"- `scipy.sparse.linalg.eigs`\n",
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" - 返回前 k 大的特征值和特征向量\n",
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"- `scipy.sparse.linalg.svds`\n",
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" - 返回前 k 大的奇异值和奇异向量"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## <font color=\"red\">所有的这些操作既可以在稀疏矩阵上使用，也可以在普通矩阵上使用。</font>"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎09. theano/09.03 gpu on windows.ipynb

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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using gpu device 0: Tesla K10.G2.8GB (CNMeM is disabled)\n",
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"Using gpu device 0: Tesla C2075 (CNMeM is disabled)\n",
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"using device: gpu\n"
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]
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}
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Looping 1000 times took 3.730786 seconds\r\n",
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"Looping 1000 times took 3.498123 seconds\r\n",
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"Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761\r\n",
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" 1.62323284]\r\n",
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"Used the cpu\r\n"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using gpu device 0: Tesla K10.G2.8GB (CNMeM is disabled)\n",
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"Looping 1000 times took 1.214745 seconds\n",
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"Using gpu device 0: Tesla C2075 (CNMeM is disabled)\n",
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"Looping 1000 times took 0.847006 seconds\n",
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"Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761\n",
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" 1.62323296]\n",
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"Used the gpu\n"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using gpu device 0: Tesla K10.G2.8GB (CNMeM is disabled)\n",
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"Looping 1000 times took 0.586156 seconds\n",
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"Result is <CudaNdarray object at 0x7f86ef3ad8b0>\n",
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"Using gpu device 0: Tesla C2075 (CNMeM is disabled)\n",
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"Looping 1000 times took 0.318359 seconds\n",
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"Result is <CudaNdarray object at 0x7f7bb701fb70>\n",
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"Numpy result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761\n",
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" 1.62323296]\n",
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"Used the gpu\n"

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