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<body><div class="nonumber_header"><h2><a href="index.html">Bugfinder Hall of Fame</a></h2></div><div class="section"><div id="toc">
<p class="toc_title"><a href="index.html">ニューラルネットワークと深層学習</a></p><p class="toc_not_mainchapter"><a href="about.html">What this book is about</a></p><p class="toc_not_mainchapter"><a href="exercises_and_problems.html">On the exercises and problems</a></p><p class='toc_mainchapter'><a id="toc_using_neural_nets_to_recognize_handwritten_digits_reveal" class="toc_reveal" onMouseOver="this.style.borderBottom='1px solid #2A6EA6';" onMouseOut="this.style.borderBottom='0px';"><img id="toc_img_using_neural_nets_to_recognize_handwritten_digits" src="images/arrow.png" width="15px"></a><a href="chap1.html">ニューラルネットワークを用いた手書き文字認識</a><div id="toc_using_neural_nets_to_recognize_handwritten_digits" style="display: none;"><p class="toc_section"><ul><a href="chap1.html#perceptrons"><li>Perceptrons</li></a><a href="chap1.html#sigmoid_neurons"><li>Sigmoid neurons</li></a><a href="chap1.html#the_architecture_of_neural_networks"><li>The architecture of neural networks</li></a><a href="chap1.html#a_simple_network_to_classify_handwritten_digits"><li>A simple network to classify handwritten digits</li></a><a href="chap1.html#learning_with_gradient_descent"><li>Learning with gradient descent</li></a><a href="chap1.html#implementing_our_network_to_classify_digits"><li>Implementing our network to classify digits</li></a><a href="chap1.html#toward_deep_learning"><li>Toward deep learning</li></a></ul></p></div>
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});</script><p class='toc_mainchapter'><a id="toc_how_the_backpropagation_algorithm_works_reveal" class="toc_reveal" onMouseOver="this.style.borderBottom='1px solid #2A6EA6';" onMouseOut="this.style.borderBottom='0px';"><img id="toc_img_how_the_backpropagation_algorithm_works" src="images/arrow.png" width="15px"></a><a href="chap2.html">逆伝播の仕組み</a><div id="toc_how_the_backpropagation_algorithm_works" style="display: none;"><p class="toc_section"><ul><a href="chap2.html#warm_up_a_fast_matrix-based_approach_to_computing_the_output_from_a_neural_network"><li>Warm up: a fast matrix-based approach to computing the output from a neural network</li></a><a href="chap2.html#the_two_assumptions_we_need_about_the_cost_function"><li>The two assumptions we need about the cost function</li></a><a href="chap2.html#the_hadamard_product_$s_\odot_t$"><li>The Hadamard product, $s \odot t$</li></a><a href="chap2.html#the_four_fundamental_equations_behind_backpropagation"><li>The four fundamental equations behind backpropagation</li></a><a href="chap2.html#proof_of_the_four_fundamental_equations_(optional)"><li>Proof of the four fundamental equations (optional)</li></a><a href="chap2.html#the_backpropagation_algorithm"><li>The backpropagation algorithm</li></a><a href="chap2.html#the_code_for_backpropagation"><li>The code for backpropagation</li></a><a href="chap2.html#in_what_sense_is_backpropagation_a_fast_algorithm"><li>In what sense is backpropagation a fast algorithm?</li></a><a href="chap2.html#backpropagation_the_big_picture"><li>Backpropagation: the big picture</li></a></ul></p></div>
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});</script><p class='toc_mainchapter'><a id="toc_a_visual_proof_that_neural_nets_can_compute_any_function_reveal" class="toc_reveal" onMouseOver="this.style.borderBottom='1px solid #2A6EA6';" onMouseOut="this.style.borderBottom='0px';"><img id="toc_img_a_visual_proof_that_neural_nets_can_compute_any_function" src="images/arrow.png" width="15px"></a><a href="chap4.html">ニューラルネットワークが任意の関数を表現できることの視覚的証明</a><div id="toc_a_visual_proof_that_neural_nets_can_compute_any_function" style="display: none;"><p class="toc_section"><ul><a href="chap4.html#two_caveats"><li>Two caveats</li></a><a href="chap4.html#universality_with_one_input_and_one_output"><li>Universality with one input and one output</li></a><a href="chap4.html#many_input_variables"><li>Many input variables</li></a><a href="chap4.html#extension_beyond_sigmoid_neurons"><li>Extension beyond sigmoid neurons</li></a><a href="chap4.html#fixing_up_the_step_functions"><li>Fixing up the step functions</li></a><a href="chap4.html#conclusion"><li>Conclusion</li></a></ul></p></div>
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<p class='toc_mainchapter'><a id="toc_deep_learning_reveal" class="toc_reveal" onMouseOver="this.style.borderBottom='1px solid #2A6EA6';" onMouseOut="this.style.borderBottom='0px';"><img id="toc_img_deep_learning" src="images/arrow.png" width="15px"></a><a href="chap6.html">深層学習</a><div id="toc_deep_learning" style="display: none;"><p class="toc_section"><ul><a href="chap6.html#introducing_convolutional_networks"><li>Introducing convolutional networks</li></a><a href="chap6.html#convolutional_neural_networks_in_practice"><li>Convolutional neural networks in practice</li></a><a href="chap6.html#the_code_for_our_convolutional_networks"><li>The code for our convolutional networks</li></a><a href="chap6.html#recent_progress_in_image_recognition"><li>Recent progress in image recognition</li></a><a href="chap6.html#other_approaches_to_deep_neural_nets"><li>Other approaches to deep neural nets</li></a><a href="chap6.html#on_the_future_of_neural_networks"><li>On the future of neural networks</li></a></ul></p></div>
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<p class="toc_not_mainchapter"><a href="sai.html">
Appendix: 知性のある <i>シンプルな</i> アルゴリズムはあるか?</a></p>
<p class="toc_not_mainchapter"><a href="acknowledgements.html">Acknowledgements</a></p><p class="toc_not_mainchapter"><a href="faq.html">Frequently Asked Questions</a></p>
<hr>
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<p class="sidebar">著者と共にこの本を作り出してくださった<a
href="supporters.html">サポーター</a>の皆様に感謝いたします。
また、<a
href="bugfinder.html">バグ発見者の殿堂</a>に名を連ねる皆様にも感謝いたします。
また、日本語版の出版にあたっては、<a
href="translators.html">翻訳者</a>の皆様に深く感謝いたします。
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<p class="sidebar">この本は目下のところベータ版で、開発続行中です。
エラーレポートは [email protected] まで、日本語版に関する質問は [email protected] までお送りください。
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著：<a href="http://michaelnielsen.org">Michael Nielsen</a> / 2014年9月-12月 <br > 訳：<a href="https://github.com/nnadl-ja/nnadl_site_ja">「ニューラルネットワークと深層学習」翻訳プロジェクト</a>
</p>
</div>
<p>
Thankyou to the following people for their help in finding and
eliminating bugs and other errors in the book:</p>
<p>Kartik Agaram, Martin Aulbach, Joseph Barrow, Maxim Baz, Charles
Brauer, Cristi Burcă, Gildas Chabot, Jen Dodd, Chris Ferrie, Tim
Gowers, Timo Hannay, Vojtěch Havlíček, Nikola Henezi,
Jay Huie, Michael Lennon, John McNally, Juergen Schmidhuber, and
Sharad Sundararajan.
</p>
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</div><div class="footer"> <span class="left_footer"> In academic work,
please cite this book as: Michael A. Nielsen, "Neural Networks and
Deep Learning", Determination Press, 2015
<br/>
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build on this book, but not to sell it. If you're interested in
commercial use, please <a
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Last update: Sun Dec 21 14:49:05 2014
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