# Code samples for "Neural Networks and Deep Learning"
This repository contains code samples for my book on ["Neural Networks
and Deep Learning"](http://neuralnetworksanddeeplearning.com).
The code is written for Python 2.6 or 2.7. Michal Daniel Dobrzanski
has a repository for Python 3
[here](https://github.com/MichalDanielDobrzanski/DeepLearningPython35). I
will not be updating the current repository for Python 3
compatibility.
The program `src/network3.py` uses version 0.6 or 0.7 of the Theano
library. It needs modification for compatibility with later versions
of the library. I will not be making such modifications.
As the code is written to accompany the book, I don't intend to add
new features. However, bug reports are welcome, and you should feel
free to fork and modify the code.
## License
MIT License
Copyright (c) 2012-2018 Michael Nielsen
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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MIT神经网络算法以及数据集
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2019-02-26
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共104个文件
png:41个
py:26个
json:14个
MIT神经网络算法以及数据集(随机梯度下降、梯度下降、Backpropagation算法、Softmax和Overfitting、Regulization、Regulazition和Dropout、ConvolutionNerualNetwork算法、提高版本的手写数字识别实现、神经网络参数hyper parameters选择、用ReL解决VanishingGradient问题、ConvolutionNerualNetwork算法)
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MIT神经网络算法以及数据集 (104个子文件)
config 272B
development 166B
experimental 166B
FETCH_HEAD 419B
.gitignore 52B
mnist_expanded.pkl.gz 79.52MB
mnist.pkl.gz 16.26MB
HEAD 152B
HEAD 23B
pack-e12025ba95f48d6b145048cee9c7878d3e182538.idx 33KB
index 8KB
data_1000.json 5.13MB
norms_during_training_4_layers.json 46KB
norms_during_training_3_layers.json 35KB
norms_during_training_2_layers.json 23KB
overfitting.json 21KB
regularized.json 21KB
multiple_eta.json 2KB
overfitting_full.json 2KB
regularized_full.json 2KB
weight_initialization_100.json 456B
weight_initialization_30.json 456B
initial_gradient.json 272B
more_data.json 63B
more_data_svm.json 63B
master 166B
master 152B
master 41B
README.md 2KB
README.md 263B
pack-e12025ba95f48d6b145048cee9c7878d3e182538.pack 20.42MB
packed-refs 240B
misleading_gradient.png 186KB
false_minima.png 147KB
valley.png 90KB
valley2.png 83KB
pca_hard_data_fit.png 80KB
regularized2.png 67KB
overfitting2.png 62KB
misleading_gradient_contours.png 58KB
mnist_100_digits.png 57KB
pca_hard_data.png 52KB
training_speed_4_layers.png 50KB
regularized_full.png 49KB
multiple_eta.png 45KB
overfitting_full.png 44KB
training_speed_3_layers.png 43KB
more_data_comparison.png 43KB
training_speed_2_layers.png 42KB
weight_initialization_30.png 41KB
weight_initialization_100.png 40KB
overfitting3.png 38KB
overfitting1.png 36KB
regularized1.png 36KB
more_data_log.png 34KB
more_data.png 32KB
overfitting4.png 30KB
backprop_magnitude_nabla.png 29KB
relu.png 25KB
tanh.png 23KB
sigmoid.png 23KB
test.png 18KB
step.png 17KB
mnist_really_bad_images.png 12KB
digits.png 8KB
digits_separate.png 8KB
mnist_2_and_1.png 5KB
more_data_rotated_5.png 5KB
mnist_complete_zero.png 5KB
mnist_first_digit.png 5KB
more_data_5.png 5KB
mnist_other_features.png 5KB
mnist_top_left_feature.png 4KB
.project 387B
network2.py 14KB
network3.py 13KB
conv.py 12KB
mnist.py 12KB
overfitting.py 7KB
network.py 6KB
generate_gradient.py 4KB
more_data.py 4KB
mnist_loader.py 3KB
weight_initialization.py 3KB
backprop_magnitude_nabla.py 3KB
mnist_average_darkness.py 3KB
multiple_eta.py 2KB
expand_mnist.py 2KB
valley2.py 1KB
misleading_gradient.py 1KB
valley.py 1KB
serialize_images_to_json.py 1KB
false_minima.py 1KB
pca_limitations.py 814B
mnist_svm.py 758B
misleading_gradient_contours.py 514B
relu.py 440B
sigmoid.py 426B
step.py 417B
tanh.py 349B
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