33 skills found · Page 1 of 2
kavgan / Nlp In PracticeStarter code to solve real world text data problems. Includes: Gensim Word2Vec, phrase embeddings, Text Classification with Logistic Regression, word count with pyspark, simple text preprocessing, pre-trained embeddings and more.
cair / PyTsetlinMachineImplements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranularity, clause indexing, and literal budget
const-ae / LemurLatent Embedding Multivariate Regression
prodriguezsosa / EmbeddingRegressionRepository for paper "Embedding Regression: Models for Context-Specific Description and Inference"
ejaasaari / LorannApproximate Nearest Neighbor search using reduced-rank regression, with extremely fast queries, tiny memory usage, and rapid indexing on modern vector embeddings.
qiyaoliang / Quantum Deep LearningRecent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!
epfl-dlab / Cr5Code and data for the WSDM '19 paper "Crosslingual Document Embedding as Reduced-Rank Ridge Regression (Cr5)"
shishirdas / Rain Fall Data Analysis Using Data ScienceContext Rainfall is very crucial things for any types of agricultural task. Climate related data is important to analyse agricultural and crop seeding related field, where those data can be used to show the predict the rainfall in different season also for different types of crops. Developed application can be found from http://ml.bigalogy.com/ Paper: http://dspace.uiu.ac.bd/handle/52243/178 Abstract Mankind have been attempting to predict the weather from prehistory. For good reason for knowing when to plant crops, when to build and when to prepare for drought and flood. In a nation such as Bangladesh being able to predict the weather, especially rainfall has never been so vitally important. The proposed research work pursues to produce prediction model on rainfall using the machine learning algorithms. The base data for this work has been collected from Bangladesh Meteorological Department. It is mainly focused on the development of models for long term rainfall prediction of Bangladesh divisions and districts (Weather Stations). Rainfall prediction is very important for the Bangladesh economy and day to day life. Scarcity or heavy - both rainfall effects rural and urban life to a great extent with the changing pattern of the climate. Unusual rainfall and long lasting rainy season is a great factor to take account into. We want to see whether too much unusual behavior is taking place another pattern resulting new clamatorial description. As agriculture is dependent on rain and heavy rainfall caused flood frequently leading to great loss to crops, rainfall is a very complex phenomenon which is dependent on various atmospheric, oceanic and geographical parameters. The relationship between these parameters and rainfall is unstable. Beside this changing behavior of clamatorial facts making the existing meteorological forecasting less usable to the users. Initially linear regression models were developed for monthly rainfall prediction of station and national level as per day month year. Here humidity, temperatures & wind parameters are used as predictors. The study is further extended by developing another popular regression analysis algorithm named Random Forest Regression. After then, few other classification algorithms have been used for model building, training and prediction. Those are Naive Bayes Classification, Decision Tree Classification (Entropy and Gini) and Random Forest Classification. In all model building and training predictor parameters were Station, Year, Month and Day. As the effect of rainfall affecting parameters is embedded in rainfall, rainfall was the label or dependent variable in these models. The developed and trained model is capable of predicting rainfall in advance for a month of a given year for a given area (for area we used here are the stations (weather parameters values are measured by Bangladesh Meteorological Department). The accuracy of rainfall estimation is above 65%. Accuracy percentage varies from algorithm to algorithm. Two regression analysis and three classification analysis models has been developed for rainfall prediction of 33 Bangladeshi weather station. Apache Spark library has been used for machine library in Scala programming language. The main idea behind the use of classification and regression analysis is to see the comparative difference between types of algorithms prediction output and the predictability along with usability. This thesis is a contribution to the effort of rainfall prediction within Bangladesh. It takes the strategy of applying machine learning models to historical weather data gathered in Bangladesh. As part of this work, a web-based software application was written using Apache Spark, Scala and HighCharts to demonstrate rainfall prediction using multiple machine learning models. Models are successively improved with the rainfall prediction accuracy. Content The given data has weather station and year wise monthly rainfall data of Bangladesh. Data is two format - 46 year (33 Weather Station) : From 1970 to 2016 Daily Rainfall Data Monthly Rainfall Data Columns: Station (Weather Station, along with Station Index) Year Month Day [For daily data file]
XUNIK8 / Undergraduate Assignments Projects汇总了本科期间所有的小作业/小项目（大项目为单独repo），课程包括：计算机图形、计算机视觉、数据结构软件开发、信号处理模式识别、计量经济回归分析、机器学习、运筹学数学建模、量化金融、嵌入式机器人、时间序列分析等。 Aggregates all small assignments/small projects (large projects are in separate repos) during the undergraduate years in courses such as: computer graphics, computer vision, data structure & software development, signal processing & pattern recognition, econometric & regression analysis, machine learning, mathematical modeling & operations research, quantitative finance, embedded robotics, and time series analysis.
tetratensor / ML Powered Resume AnalyserLocal, privacy-friendly resume analysis: convert, classify, and get advice using TF‑IDF, Logistic Regression, and sentence-transformer embeddings.
emerdem / MovieLens ML LSTMMovie Rating Prediction using GloVe Word Embeddings and Deep Learning (LSTM): Use MovieLens dataset to predict movie ratings using tags generated by users with 67% accuracy using an ensemble model of logistic regression that incorporates clustering of word embeddings and LSTM neural network.
DJAlexJ / LRCN For Video RegressionLRCN approach for video regression that uses CNNs for visual input and LSTMs to process sequences of frame embeddings
mrecos / KlrfomeKernel Logistic Regression on Focal Mean Embeddings
ksdkamesh99 / Spam ClassifierA Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomial-naive-bayes,logistic regression,svm,decision trees to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer,TFIDF Vetorizer,WordnetLemmatizer. It is implemented using LSTM and Word Embeddings to gain accuracy of 97.84%.
akbloodadarsh / Twitter Sentimental AnalysisI have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and ROC(Receiver Operating Characteristic) and AUC(Area Under Curve) and finally shown how they are classifying the tweet in positive and negative.
wangruichens / Bert Multi Gpu文本点击率 multi gpu version of bert with classification / regression， bert token embedding with textcnn
all-umass / LlerLocally Linear Embedding for Regression - Journal of Chemometrics 2015
SaniyaKhullar / NetREmNetwork Regression Embeddings reveal cell-type Transcription Factor coordination for target gene (TG) regulation
Ryan2486 / Nlp Emotion AnalysisExploratory NLP project using the DistilBERT transformer model and the HuggingFace "emotion" dataset. Includes tokenizer and encoder testing, UMAP-based visualization of embeddings, logistic regression classification, and fine-tuning of a pre-trained model for emotion detection.
MLFS-GRROOR / GRROORGlobal Redundancy and Relevance Optimization in Orthogonal Regression for Embedded Multi-label Feature Selection
