《深度学习入门:基于Python的理论与实现》源代码,深入浅出学Python,Python源码

上传者: 42696333 | 上传时间: 2021-09-28 16:03:25 | 文件大小: 4.47MB | 文件类型: RAR
深度学习入门——基于python的理论与实现这本书的源代码

文件下载

资源详情

[{"title":"( 67 个子文件 4.47MB ) 《深度学习入门:基于Python的理论与实现》源代码,深入浅出学Python,Python源码","children":[{"title":"《深度学习入门:基于Python的理论与实现》源代码","children":[{"title":"common","children":[{"title":"functions.py <span style='color:#111;'> 1.13KB </span>","children":null,"spread":false},{"title":"util.py <span style='color:#111;'> 2.53KB </span>","children":null,"spread":false},{"title":"gradient.py <span style='color:#111;'> 1.17KB </span>","children":null,"spread":false},{"title":"multi_layer_net.py <span style='color:#111;'> 5.41KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"optimizer.py <span style='color:#111;'> 3.89KB </span>","children":null,"spread":false},{"title":"multi_layer_net_extend.py <span style='color:#111;'> 6.52KB </span>","children":null,"spread":false},{"title":"layers.py <span style='color:#111;'> 7.51KB </span>","children":null,"spread":false},{"title":"trainer.py <span style='color:#111;'> 3.11KB </span>","children":null,"spread":false}],"spread":true},{"title":"ch02","children":[{"title":"or_gate.py <span style='color:#111;'> 354B </span>","children":null,"spread":false},{"title":"xor_gate.py <span style='color:#111;'> 331B </span>","children":null,"spread":false},{"title":"and_gate.py <span style='color:#111;'> 357B </span>","children":null,"spread":false},{"title":"nand_gate.py <span style='color:#111;'> 360B </span>","children":null,"spread":false}],"spread":true},{"title":"ch05","children":[{"title":"gradient_check.py <span style='color:#111;'> 677B </span>","children":null,"spread":false},{"title":"two_layer_net.py <span style='color:#111;'> 2.43KB </span>","children":null,"spread":false},{"title":"train_neuralnet.py <span style='color:#111;'> 1.22KB </span>","children":null,"spread":false},{"title":"buy_apple_orange.py <span style='color:#111;'> 988B </span>","children":null,"spread":false},{"title":"buy_apple.py <span style='color:#111;'> 500B </span>","children":null,"spread":false},{"title":"layer_naive.py <span style='color:#111;'> 557B </span>","children":null,"spread":false}],"spread":true},{"title":"ch06","children":[{"title":"overfit_weight_decay.py <span style='color:#111;'> 1.98KB </span>","children":null,"spread":false},{"title":"batch_norm_gradient_check.py <span style='color:#111;'> 773B </span>","children":null,"spread":false},{"title":"hyperparameter_optimization.py <span style='color:#111;'> 2.60KB </span>","children":null,"spread":false},{"title":"overfit_dropout.py <span style='color:#111;'> 1.45KB </span>","children":null,"spread":false},{"title":"weight_init_compare.py <span style='color:#111;'> 1.86KB </span>","children":null,"spread":false},{"title":"batch_norm_test.py <span style='color:#111;'> 2.77KB </span>","children":null,"spread":false},{"title":"optimizer_compare_naive.py <span style='color:#111;'> 1.46KB </span>","children":null,"spread":false},{"title":"weight_init_activation_histogram.py <span style='color:#111;'> 1.19KB </span>","children":null,"spread":false},{"title":"optimizer_compare_mnist.py <span style='color:#111;'> 1.86KB </span>","children":null,"spread":false}],"spread":true},{"title":"ch03","children":[{"title":"sample_weight.pkl <span style='color:#111;'> 177.59KB </span>","children":null,"spread":false},{"title":"sig_step_compare.py <span style='color:#111;'> 361B </span>","children":null,"spread":false},{"title":"step_function.py <span style='color:#111;'> 267B </span>","children":null,"spread":false},{"title":"sigmoid.py <span style='color:#111;'> 212B </span>","children":null,"spread":false},{"title":"neuralnet_mnist.py <span style='color:#111;'> 1.06KB </span>","children":null,"spread":false},{"title":"neuralnet_mnist_batch.py <span style='color:#111;'> 1.13KB </span>","children":null,"spread":false},{"title":"mnist_show.py <span style='color:#111;'> 551B </span>","children":null,"spread":false},{"title":"relu.py <span style='color:#111;'> 198B </span>","children":null,"spread":false}],"spread":true},{"title":"ch08","children":[{"title":"half_float_network.py <span style='color:#111;'> 766B </span>","children":null,"spread":false},{"title":"deep_convnet_params.pkl <span style='color:#111;'> 966.16KB </span>","children":null,"spread":false},{"title":"misclassified_mnist.py <span style='color:#111;'> 1.57KB </span>","children":null,"spread":false},{"title":"deep_convnet.py <span style='color:#111;'> 5.78KB </span>","children":null,"spread":false},{"title":"awesome_net.py <span style='color:#111;'> 27B </span>","children":null,"spread":false},{"title":"train_deepnet.py <span style='color:#111;'> 699B </span>","children":null,"spread":false}],"spread":true},{"title":"dataset","children":[{"title":"lena_gray.png <span style='color:#111;'> 41.59KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"mnist.py <span style='color:#111;'> 3.43KB </span>","children":null,"spread":false},{"title":"lena.png <span style='color:#111;'> 115.20KB </span>","children":null,"spread":false}],"spread":true},{"title":"ch04","children":[{"title":"gradient_method.py <span style='color:#111;'> 755B </span>","children":null,"spread":false},{"title":"gradient_1d.py <span style='color:#111;'> 497B </span>","children":null,"spread":false},{"title":"two_layer_net.py <span style='color:#111;'> 2.34KB </span>","children":null,"spread":false},{"title":"train_neuralnet.py <span style='color:#111;'> 1.68KB </span>","children":null,"spread":false},{"title":"gradient_2d.py <span style='color:#111;'> 1.55KB </span>","children":null,"spread":false},{"title":"gradient_simplenet.py <span style='color:#111;'> 662B </span>","children":null,"spread":false}],"spread":true},{"title":"LICENSE.md <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 1.04KB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 37B </span>","children":null,"spread":false},{"title":"ch07","children":[{"title":"gradient_check.py <span style='color:#111;'> 544B </span>","children":null,"spread":false},{"title":"apply_filter.py <span style='color:#111;'> 1.57KB </span>","children":null,"spread":false},{"title":"visualize_filter.py <span style='color:#111;'> 801B </span>","children":null,"spread":false},{"title":"simple_convnet.py <span style='color:#111;'> 5.48KB </span>","children":null,"spread":false},{"title":"train_convnet.py <span style='color:#111;'> 1.41KB </span>","children":null,"spread":false},{"title":"params.pkl <span style='color:#111;'> 3.31MB </span>","children":null,"spread":false}],"spread":true},{"title":"ch01","children":[{"title":"simple_graph.py <span style='color:#111;'> 204B </span>","children":null,"spread":false},{"title":"hungry.py <span style='color:#111;'> 21B </span>","children":null,"spread":false},{"title":"sin_graph.py <span style='color:#111;'> 164B </span>","children":null,"spread":false},{"title":"img_show.py <span style='color:#111;'> 162B </span>","children":null,"spread":false},{"title":"sin_cos_graph.py <span style='color:#111;'> 381B </span>","children":null,"spread":false},{"title":"man.py <span style='color:#111;'> 318B </span>","children":null,"spread":false}],"spread":true}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明