文件名称:Python深度学习实战_原书代码
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深度学习正在为广泛的行业带来革命性的变化。对于许多应用来说，深度学习通过做出更快和更准确的预测，证明其已经超越人类的预测。本书提供了自上而下和自下而上的方法来展示深度学习对不同领域现实问题的解决方案。这些应用程序包括计算机视觉、自然语言处理、时间序列预测和机器人。(Deep learning is bringing revolutionary changes to a wide range of industries. For many applications, deep learning proves to be beyond human prediction by making faster and more accurate predictions. This book provides top-down and bottom-up approaches to demonstrate deep learning solutions to practical problems in different areas. These applications include computer vision, natural language processing, time series prediction and robotics.)相关搜索： Python深度学习实战_原书代码
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下载文件列表
| 文件名 | 大小 | 更新时间 | |
|---|---|---|---|
| 原书代码\.gitattributes | 66 | 2018-05-22 | |
| 原书代码\9781484235157.jpg | 28610 | 2018-05-22 | |
| 原书代码\Chapter10_RNN and LSTM in visual\sp500.csv | 48119 | 2018-05-22 | |
| 原书代码\Chapter10_RNN and LSTM in visual\Time Series forcasting with lstm model.ipynb | 58177 | 2018-05-22 | |
| 原书代码\Chapter11_Speech to text and vice versa\audio.wav | 704556 | 2018-05-22 | |
| 原书代码\Chapter11_Speech to text and vice versa\Speech to Text API and Text to Speech.ipynb | 14096 | 2018-05-22 | |
| 原书代码\Chapter12_Developing Chatbots\intent1.csv | 1393 | 2018-05-22 | |
| 原书代码\Chapter12_Developing Chatbots\Removing Punctuations.ipynb | 72 | 2018-05-22 | |
| 原书代码\Chapter12_Developing Chatbots\Removing Stopwords.ipynb | 72 | 2018-05-22 | |
| 原书代码\Chapter12_Developing Chatbots\TF-IDF and Word2Vec.ipynb | 26152 | 2018-05-22 | |
| 原书代码\Chapter12_Developing Chatbots\Tokenization.ipynb | 72 | 2018-05-22 | |
| 原书代码\Chapter13_Face Recognition\Face_Detection.py | 3062 | 2018-05-22 | |
| 原书代码\Chapter13_Face Recognition\Face_Recognition.py | 5288 | 2018-05-22 | |
| 原书代码\Chapter13_Face Recognition\Face_Tracking.py | 6612 | 2018-05-22 | |
| 原书代码\Chapter1_Prerequisites of Deep Learning Numpy | Pandas and Scikit-Learn\chapter1_summary.ipynb | 341456 | 2018-05-22 |
| 原书代码\Chapter2_Basics of Tensorflow\chapter2_summary.ipynb | 27538 | 2018-05-22 | |
| 原书代码\Chapter2_Basics of Tensorflow\TFBasics.ipynb | 6190 | 2018-05-22 | |
| 原书代码\Chapter3_Understanding and working on Keras\chapter3_summary.ipynb | 34522 | 2018-05-22 | |
| 原书代码\Chapter3_Understanding and working on Keras\MLPMNIST.ipynb | 32446 | 2018-05-22 | |
| 原书代码\Chapter3_Understanding and working on Keras\model.h5 | 13115488 | 2018-05-22 | |
| 原书代码\Chapter3_Understanding and working on Keras\modelWeight.h5 | 6564312 | 2018-05-22 | |
| 原书代码\Chapter3_Understanding and working on Keras\Softmax _RegressionB.ipynb | 9107 | 2018-05-22 | |
| 原书代码\Chapter5_Regresson to MLP in Tensorflow\Implementing a hidden layer MLP.ipynb | 23657 | 2018-05-22 | |
| 原书代码\Chapter5_Regresson to MLP in Tensorflow\Linear Regression Tensorflow.ipynb | 48633 | 2018-05-22 | |
| 原书代码\Chapter5_Regresson to MLP in Tensorflow\Logistic Regression Tensorflow.ipynb | 23254 | 2018-05-22 | |
| 原书代码\Chapter5_Regresson to MLP in Tensorflow\Saved Games\desktop.ini | 282 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\Fashion MNIST Data Logistic Regression in Keras.ipynb | 3749 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\fashion_mnist.py | 1520 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\iris_test.csv | 511 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\iris_train.csv | 4151 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\Log Linear Model using scikit learn and keras.ipynb | 15944 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\Logistic regression using scikit Learn and keras.ipynb | 4958 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\MLP on Iris Dataset.ipynb | 16811 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\MLP on MNIST dataset digit classification.ipynb | 7781 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\MLP on randomly generated data.ipynb | 5545 | 2018-05-22 | |
| 原书代码\Chapter6_Regression to MLP in Keras\my_model.h5 | 1367784 | 2018-05-22 | |
| 原书代码\Chapter8_CNN with Tensorflow\chapter 8.ipynb | 10151 | 2018-05-22 | |
| 原书代码\Chapter9_CNN with Keras\CNN_with_Keras.ipynb | 6051 | 2018-05-22 | |
| 原书代码\Chapter9_CNN with Keras\horse.jpg | 6232 | 2018-05-22 | |
| 原书代码\Chapter9_CNN with Keras\Image Classifier with cifar10 data.ipynb | 4099 | 2018-05-22 | |
| 原书代码\Chapter9_CNN with Keras\Pre-trained Models.ipynb | 123152 | 2018-05-22 | |
| 原书代码\Contributing.md | 677 | 2018-05-22 | |
| 原书代码\errata.md | 225 | 2018-05-22 | |
| 原书代码\LICENSE.txt | 1350 | 2018-05-22 | |
| 原书代码\README.md | 553 | 2018-05-22 | |
| 原书代码\Thumbs.db | 14336 | 2019-05-27 | |
| 原书代码\Chapter5_Regresson to MLP in Tensorflow\Saved Games | 0 | 2019-01-03 | |
| 原书代码\Chapter10_RNN and LSTM in visual | 0 | 2019-01-03 | |
| 原书代码\Chapter11_Speech to text and vice versa | 0 | 2019-01-03 | |
| 原书代码\Chapter12_Developing Chatbots | 0 | 2019-01-03 | |
| 原书代码\Chapter13_Face Recognition | 0 | 2019-01-03 | |
| 原书代码\Chapter1_Prerequisites of Deep Learning Numpy | Pandas and Scikit-Learn | 0 | 2019-01-03 |
| 原书代码\Chapter2_Basics of Tensorflow | 0 | 2019-01-03 | |
| 原书代码\Chapter3_Understanding and working on Keras | 0 | 2019-01-03 | |
| 原书代码\Chapter5_Regresson to MLP in Tensorflow | 0 | 2019-01-03 | |
| 原书代码\Chapter6_Regression to MLP in Keras | 0 | 2019-01-03 | |
| 原书代码\Chapter8_CNN with Tensorflow | 0 | 2019-01-03 | |
| 原书代码\Chapter9_CNN with Keras | 0 | 2019-01-03 | |
| 原书代码 | 0 | 2019-01-03 |