重磅！深度学习圣经“花书”核心笔记、代码发布

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《深度学习》，又名“花书”。该书由三位大佬 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 撰写，是深度学习领域奠基性的经典教材，被誉为深度学习的“圣经”。

原书内容非常充实，接近 800 页。这本书内容很深很全面，但起点稍微高了一些，对数学理论基础知识要求的比较多。因此，读完之后，及时进行高度概括和经验总结是十分有帮助的。石头君最近在 GitHub 上发现一个关于花书各章摘要的项目，内容非常精炼，除了笔记的同时，部分章节还配备代码，值得推荐，我们一起来看一下。

该项目的名称是：Deep-Learning-Book-Chapter-Summaries，作者是 Aman Dalmia 和 Ameya Godbole 两位小哥。项目地址为：

https://github.com/dalmia/Deep-Learning-Book-Chapter-Summaries

主要内容

这份花书核心笔记主要涉及的章节包括：

• ch02 线性代数

• ch03 概率与信息理论

• ch04 数值优化

• ch07 深度学习正则化

• ch08 深度模型中的优化

• ch09 卷积网络

• ch11 实践方法论

• ch13 线性因子模型

笔记的形式是 .ipynb，便于在 Jupyter Notebook 上打开和观看。例如，我们来看一下第二章线性代数的笔记。

可见，Jupyter 笔记不仅包含了知识点的总结，也有相关代码。再来看第九章的卷积网络部分，配备了一些完整的图片处理代码。

import numpy as np
from scipy import signal
from scipy import misc
import matplotlib.pyplot as plt

# %matplotlib inline

img = misc.ascent()
kernel = np.random.randn(5,5)
# kernel = np.array([[0,-10,0,10,0],[-10,-30,0,30,10],[0,-10,0,10,0]])

img = img.astype(np.float32)/255
orig_in = img

offsetx = offsety = 20
shift_in = np.zeros(orig_in.shape)
shift_in[offsetx:,offsety:] = img[:-offsetx,:-offsety]

rot_in = misc.imrotate(img, 90)
scale_in = misc.imresize(orig_in, 1.5)

output1 = signal.convolve2d(orig_in, kernel, mode='same')
output2 = signal.convolve2d(shift_in, kernel, mode='same')
output3 = signal.convolve2d(rot_in, kernel, mode='same')
output4 = signal.convolve2d(scale_in, kernel, mode='same')

fig, axes = plt.subplots(2, 4, figsize=(14, 7))
ax_orig = axes[0,0]
ax_shift = axes[0,1]
ax_rot = axes[0,2]
ax_scale = axes[0,3]

diff_orig = axes[1,0]
diff_shift = axes[1,1]
diff_rot = axes[1,2]
diff_scale = axes[1,3]

ax_orig.imshow(output1, cmap='gray')
ax_orig.set_title('Original')
ax_shift.imshow(output2, cmap='gray')
ax_shift.set_title('Shifted')
ax_rot.imshow(output3, cmap='gray')
ax_rot.set_title('Rotated')
ax_scale.imshow(output4, cmap='gray')
ax_scale.set_title('Scaled')

def shift(arr, offset):
    output = np.zeros(arr.shape)
    output[offset:, offset:] = arr[:-offset,:-offset]
    return output

def rotate(arr, angle):
    return misc.imrotate(arr, angle)

def resize(arr, scale):
    return misc.imresize(arr, scale)

diff_orig.hist(np.ravel(output1),bins=100)
diff_orig.set_title('Output histogram')
diff_shift.hist(np.ravel(np.abs(output2-shift(output1, 20))),bins=100)
diff_shift.set_title('Shift histogram difference')
diff_rot.hist(np.ravel(np.abs(output3-rotate(output1, 10))),bins=100)
diff_rot.set_title('Rotate histogram difference')
diff_scale.hist(np.ravel(np.abs(output4-resize(output1, 1.5))),bins=100)
diff_scale.set_title('Scale histogram difference')

ax_orig.set_xticks([])
ax_shift.set_xticks([])
ax_rot.set_xticks([])
ax_scale.set_xticks([])

ax_orig.set_yticks([])
ax_shift.set_yticks([])
ax_rot.set_yticks([])
ax_scale.set_yticks([])

plt.tight_layout()
# plt.show()
plt.savefig('images/conv_equivariance.png')

对于池化层的代码示例：

import numpy as np
np.random.seed(101)

from scipy import signal
from scipy import misc
import matplotlib.pyplot as plt
%matplotlib inline

img = misc.ascent()
img = img.astype(np.float32)/255

# The image is more interesting here
orig_in = img[-200:,-300:-100]
offsetx = offsety = 15
shift_in = img[-200-offsetx:-offsetx,-300-offsety:-100-offsety]
kernel1 = np.random.randn(5,5)
kernel2 = np.random.randn(5,5)
kernel3 = np.random.randn(5,5)

def sigmoid(arr):
    # Lazy implementation of sigmoid activation
    return 1./(1 + np.exp(-arr))

def maxpool(arr, poolsize, stride):
    # Lazy looping implementation of maxpool
    output_shape = np.floor((np.array(arr.shape)-poolsize)/stride)+1
    output_shape = output_shape.astype(np.int32)
    output = np.zeros(output_shape)
    for x in range(output_shape[0]):
        for y in range(output_shape[1]):
            output[x,y] = np.max(arr[x*stride:x*stride+poolsize,y*stride:y*stride+poolsize])
    return output

output1_1 = signal.convolve2d(orig_in, kernel1, mode='valid')
pool1_1 = maxpool(output1_1, 2, 2)
actv1_1 = sigmoid(pool1_1)
output1_2 = signal.convolve2d(actv1_1, kernel2, mode='valid')
pool1_2 = maxpool(output1_2, 2, 2)
actv1_2 = sigmoid(pool1_2)
output1_3 = signal.convolve2d(actv1_2, kernel3, mode='valid')
pool1_3 = maxpool(output1_3, 2, 2)

output2_1 = signal.convolve2d(shift_in, kernel1, mode='valid')
pool2_1 = maxpool(output2_1, 2, 2)
actv2_1 = sigmoid(pool2_1)
output2_2 = signal.convolve2d(actv2_1, kernel2, mode='valid')
pool2_2 = maxpool(output2_2, 2, 2)
actv2_2 = sigmoid(pool2_2)
output2_3 = signal.convolve2d(actv2_2, kernel3, mode='valid')
pool2_3 = maxpool(output2_3, 2, 2)

fig, axes = plt.subplots(4, 3, figsize=(10, 10))

k1, k2, k3 = axes[0,:]
p1_1, p1_2, p1_3 = axes[1,:]
p2_1, p2_2, p2_3 = axes[2,:]
h1, h2, h3 = axes[3,:]

k1.imshow(kernel1, cmap='gray')
k1.set_title('kernel1')
k2.imshow(kernel2, cmap='gray')
k2.set_title('kernel2')
k3.imshow(kernel3, cmap='gray')
k3.set_title('kernel3')
k1.set_xticks([])
k2.set_xticks([])
k3.set_xticks([])
k1.set_yticks([])
k2.set_yticks([])
k3.set_yticks([])

p1_1.imshow(pool1_1, cmap='gray')
p1_1.set_title('pool1_1')
p1_2.imshow(pool1_2, cmap='gray')
p1_2.set_title('pool1_2')
p1_3.imshow(pool1_3, cmap='gray')
p1_3.set_title('pool1_3')
p1_1.set_xticks([])
p1_2.set_xticks([])
p1_3.set_xticks([])
p1_1.set_yticks([])
p1_2.set_yticks([])
p1_3.set_yticks([])

p2_1.imshow(pool2_1, cmap='gray')
p2_1.set_title('pool2_1')
p2_2.imshow(pool2_2, cmap='gray')
p2_2.set_title('pool2_2')
p2_3.imshow(pool2_3, cmap='gray')
p2_3.set_title('pool2_3')
p2_1.set_xticks([])
p2_2.set_xticks([])
p2_3.set_xticks([])
p2_1.set_yticks([])
p2_2.set_yticks([])
p2_3.set_yticks([])

h1.hist(np.ravel(np.abs(pool1_1-pool2_1)),bins=100)
h1.set_title('Pool 1 diff')
h2.hist(np.ravel(np.abs(pool1_2-pool2_2)),bins=100)
h2.set_title('Pool 2 diff')
h3.hist(np.ravel(np.abs(pool1_3-pool2_3)),bins=100)
h3.set_title('Pool 3 diff')

plt.tight_layout()
# plt.show()
plt.savefig('images/pool_invariance.png')

博客笔记

该项目的作者还在自己的个人网站上发布了花书的精炼笔记，地址为：

https://medium.com/inveterate-learner/tagged/deep-learning

附加资源

除了这份花书重点章节摘要之外，石头君还推荐一份来自 Microsoft 计算机软件工程师 Jeff Macaluso 总结的关于花书的经验法则！

在线版阅读地址：

https://jeffmacaluso.github.io/post/DeepLearningRulesOfThumb/

离线地址：

链接：

https://pan.baidu.com/s/1eLlJy3xB6Hs0w_Q7bO536g 

提取码：7q1d

希望这份资源对你有所帮助~

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