assigmnet hw 1

说明：  使用协同过滤的推荐系统测试代码Predicting movie ratings using collaborative filtering and latent concept extractors based on matrix decomposition methods like SVD and CUR on the movielens-100k dataset The project explores techniques for predicting unknown user ratings based on a large dataset of users and movie ratings.(Recommender-SystemsWe used 6 different recommender methods to predict user ratings across the data sets. Namely we set up Collaborative Filtering along with its baseline variant and Matrix decomposition techniques like Singular Value Decomposition and CUR. Moreover, we also implemented dimensionality reduction in these matrix decomposition techniques which preserved 90% of the energy so as to increase the computational efficiency while keeping the accuracy similar for these methods.)

使用python的flask框架 mysql实现的注册登录功能。

说明：  我们描述了所提出的方法对超声（US）信号的压缩多路复用的直接应用。该技术利用压缩多路复用器架构进行信号压缩，并依靠频域中US信号的联合稀疏性进行信号重建。由于换能器元件具有压电特性，因此可以获得有关US信号频率支持的准确先验知识，并且可以在联合稀疏算法中使用。 我们在数值实验中验证了所提出的方法，并显示了它们在秩次缺陷情况下相对于最新方法的优越性。我们还证明，与没有已知支持的重建相比，该技术可显着提高体内颈动脉图像的图像质量。(We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. Due to piezo-electric properties of transducer elements, accurate prior knowledge of the frequency support of US signals is available and can be used in joint-sparse algorithms. We validate the proposed methods on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without known support.)

这本书提供自然语言处理领域非常方便的入门指南。 它可以用来自学， 也可以作为自然 语言处理或计算语言学课程的教科书，或是人工智能、文本挖掘、语料库语言学课程的补充 读物。本书的实践性很强，包括几百个实际可用的例子和分级练习。(This book provides a very convenient entry guide for Natural Language Processing. It can be used for self-study or for nature. Textbooks for language processing or computational linguistics, or supplements to courses in artificial intelligence, text mining, Corpus Linguistics Reading. This book is highly practical, including hundreds of practical examples and grading exercises.)

可以用来训练出模型，并识别出猫或狗，直接可以运行识别猫狗(It can be used to train models and identify cats or dogs. It can run directly to recognize cats and dogs.)

分类器的性能比较与调优: 使用scikit-learn 包中的tree，贝叶斯，knn，对数据进行模型训练，尽量了解其原理及运用。 使用不同分析三种分类器在实验中的性能比较，分析它们的特点。 本实验采用的数据集为house与segment。(Performance comparison and optimization of classifiers: We use tree, Bayesian and KNN in scikit-learnpackage to train the data model and try to understand its principle and application. The performances of three classifiers are compared and their characteristics are analyzed. The data set used in this experiment is house and segment.)

用python编写的基于CV2的图像融合程序，实现偏振信息到IHS的转换、融合。(Using python to write the CV2-based image fusion program to achieve the polarization information to IHS conversion, integration.)

python编程：从入门到实践，高清pdf书本，以及原书代码，有需要的可以下载学习

说明：  倒谱分析的python程序，希望可以帮到大家(I hope the Python code of cepstrum can help you)

测试数据集的代码mapreduce编程