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  1. 编程语言:Python
  2. 代码类别:网络
  3. 发布时间:近三天
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1. 机器学习实战 = Machine Learning in Action

说明:  机器学习是人工智能研究领域中的一个极其重要的方向。在现今大数据时代的背景下,捕获数据并从中萃取有价值的信息或模式,使得这一过去为分析师与数学家所专属的研究领域越来越为人们瞩目。本书通过精心编排的实例,切入日常工作任务,摒弃学术化语言,利用高效可复用的Python 代码阐释如何处理统计数据,进行数据分析及可视化。读者可从中学到一些核心的机器学习算法,并将其运用于某些策略性任务中,如分类、预测及推荐等。(Machine learning is an extremely important direction in the field of artificial intelligence research. In the context of the current era of big data, capturing data and extracting valuable information or patterns from it has made this research area that was exclusive to analysts and mathematicians more and more attractive. Through well-organized examples, this book cuts into daily work tasks, abandons academic languages, and uses efficient and reusable Python code to explain how to process statistical data, perform data analysis and visualization. Readers can learn some core machine learning algorithms and apply them to certain strategic tasks, such as classification, prediction, and recommendation.)

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2019-12-06发布

2. deep_learing_pytorch

说明:  pytorch架构下用CNN架构识别mnist,其中含有多种优化器(Recognition of MNIST under pytorch architecture)

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2019-12-04发布

3. Proximal_Policy_Optimization

说明:  强化学习可以按照方法学习策略来划分成基于值和基于策略两种。而在深度强化学习领域将深度学习与基于值的Q-Learning算法相结合产生了DQN算法,通过经验回放池与目标网络成功的将深度学习算法引入了强化学习算法。(Reinforcement learning can be divided into value-based learning and strategy based learning according to method learning strategies. In the field of deep reinforcement learning, dqn algorithm is generated by combining deep learning with value-based Q-learning algorithm. Through experience playback pool and target network, deep learning algorithm is successfully introduced into reinforcement learning algorithm.)

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2019-12-04发布

4. 2_Q_Learning_maze

说明:  一个基于Q-learning的强化学习经典算法,实现机器人自动寻找目标位置(A classical reinforcement learning algorithm based on Q-learning, which can automatically find the target position of robot)

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2019-11-29发布

5. 神经

说明:  (1)使用Python实现样本从输入层到隐层传输。 (2)使用Python实现网络输出。 (3)使用Python实现单样本网络训练。 (4)使用Python实现全样本网络训练。 (5)使用Python评价所构建的网络模型性能。 (6)调用sklearn实现神经网络算法。((1) using Python to implement the sample transfer from the input layer to the hidden layer. (2) using Python to achieve network output. (3) using Python to realize single sample network training. (4) using Python to realize full sample network training. (5) using Python to evaluate the performance of the network model. (6) call sklearn to implement the neural network algorithm.)

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2019-11-28发布

6. sklearn-SVM

说明:  支持向量机(SVM)——分类预测,包括核函数调参,不平衡数据问题,特征降维,网格搜索,管道机制,学习曲线,混淆矩阵,AUC曲线等(Support vector machine (SVM) - classification prediction, including kernel function parameter adjustment, unbalanced data problem, feature dimensionality reduction, grid search, pipeline mechanism, learning curve, confusion matrix, AUC curve, etc)

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2019-11-28发布

7. word-language-model

说明:  自然语言处理, Python編程語言的自然語言處理工具包(Natural language processing, RNN model)

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2019-11-27发布

8. 基于tensorflow的交叉熵算法

说明:  基于tensorflow的交叉熵算法,适合深度学习的初学者(Cross entropy algorithm based on tensorflow, suitable for beginners of deep learning)

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80
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2019-11-23发布

9. MM

说明:  前向算法后向算法双向算法方法实现中文匹配(Forward algorithm backward algorithm two way algorithm for Chinese matching)

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2019-11-12发布

10. shape_predictor_81_face_landmarks-master

说明:  基于dlib库的人脸68个特征点训练模型的扩展模型,可识别出人脸81个特征点,包括额头部分。(Based on the extended model of the face training model of 68 feature points based on Dlib database, 81 feature points can be recognized, including the forehead part.)

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2019-11-07发布

11. Bayes algorithm

说明:  利用贝叶斯分类器的原理,对0-9手写数字进行识别(Using the principle of Bayes classifier to recognize 0-9 handwritten digits)

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2019-11-02发布

12. NTU_ML2017_Hung-yi-Lee_HW-master

说明:  李宏毅老师机器学习视频代码,这是第五段,用python编写(Li Hongyi teacher video code)

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62
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2019-11-01发布

13. python http 客户端

python http 客户端 实现文件或者文本上传下载

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124
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2019-10-29发布

14. SVR

说明:  支持向量机SVM在回归方面的用法SVR,其中包括例子(Support vector machine SVM in the use of regression SVR, including examples)

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2019-10-28发布

15. RF

说明:  用随机森林方法对因变量的影响因素进行特征选择,即影响度排序(Using random forest method to select the factors affecting the dependent variable, that is, the order of influence)

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2019-10-28发布

16. 1天学懂深度学习

说明:  英文版的深度学习教程,英文好的可以学习一下(English version of the deep learning tutorial, English good can learn.)

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2019-10-17发布

17. VAE实战

说明:  autoencoder是一种无监督的学习算法,主要用于数据的降维或者特征的抽取,在深度学习中,autoencoder可用于在训练阶段开始前,确定权重矩阵的初始值。(Autoencoder is an unsupervised learning algorithm, which is mainly used for dimensionalization reduction or feature extraction of data. In deep learning, autoencoder can be used to determine the initial value of weight matrix before the training stage.)

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2019-10-16发布

18. lesson51-WGAN实战

说明:  生成式对抗网络(GAN, Generative Adversarial Networks )是一种深度学习模型,是近年来复杂分布上无监督学习最具前景的方法之一。模型通过框架中(至少)两个模块:生成模型(Generative Model)和判别模型(Discriminative Model)的互相博弈学习产生相当好的输出。(Emergent against network (GAN, Generative Adversarial Networks) is a kind of deep learning model, is a complex distribution in recent years on one of the most promising method for unsupervised learning. The frame of the Model by (at least) two modules: generation Model (Generative Model) and the discriminant Model (Discriminative Model) of the game to learn each other produce fairly good output.)

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2019-10-16发布

19. AINDANE-master

说明:  针对图像亮度不均匀问题,Li Tao与2005年提出的这个算法。其主要用于提升再低照度或者不均匀光照条件下拍摄到的图像亮度。该算法主要由两部分构成:自适应亮度增强模块和自适应对比度增强。(Li Tao and this algorithm proposed in 2005 for the problem of uneven image brightness. It is mainly used to enhance the brightness of images captured under low illumination or uneven illumination. The algorithm is mainly composed of two parts: adaptive brightness enhancement module and adaptive contrast enhancement.)

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2019-10-01发布

20. Keras快速上手:基于Python的深度学习实战

说明:  keras实战,人工智能类书籍,非常适合新手(Keas practical books, very suitable for beginners)

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2019-09-27发布