haja-k

haja-k's Stars

• kelseyhightower/kubernetes-the-hard-way

  Bootstrap Kubernetes the hard way. No scripts.

  41.2k14.1k

• Ditectrev/Scrum-Developer-I-PSD-I-Practice-Tests-Exams-Questions-Answers

  ⛳️ PASS: Scrum Developer I (PSD I) by learning based on our Questions & Answers (Q&A) Practice Tests Exams.

  14263

• NajibSheikh/EXAM

  LFW211 Exam

  Language:JavaScript1

• chamithrepo/nodejs-redis-session-store

  Session Management in Nodejs Using Redis as Session Store

  Language:JavaScript237

• leoantony72/E-commerce-Api

  E-Commerce API Made With NodeJs,Typescript,PostgreSQL,Redis And Kafka

  Language:TypeScript6919

• onury/accesscontrol

  Role and Attribute based Access Control for Node.js

  Language:TypeScript2.2k178

• MoathShraim/Nodejs-rest-api-project-structure-Express

  Nodejs project structure practices for building RESTful APIs using Express framework and MongoDB.

  Language:JavaScript349142

• maitraysuthar/rest-api-nodejs-mongodb

  A boilerplate for REST API Development with Node.js, Express, and MongoDB

  Language:JavaScript1.2k421

• MoH-Malaysia/kkmnow-front

  Language:TypeScript293

• xojs/xo

  ❤️ JavaScript/TypeScript linter (ESLint wrapper) with great defaults

  Language:JavaScript7.7k289

• Tirth8038/Tweets-Sentiment-Classification

  The main aim of the project is to analyze the Twitter data describing the covid situation and to build a text classification model which can distinguish the tweets into 5 categories such as Extremely Negative (0), Negative (1), Neutral (2), Positive (3) and Extremely Positive (4). The provided dataset contains tweets with dimension (37041, 2) and numerical labels with dimension (37041,2) of above categories separately. However, the provided tweets need to be cleaned as it contains irrelevant elements such as mentions (@), HTTP links, HTML tags, punctuation marks and URL. Using the regex function, I removed those elements and Stopwords from tweets. Apart from this, to normalize the terms, I implemented Porter Stemmer and used WordNet Lemmatizer to convert the term to its base form. After this, to convert the words into vectors of equal length, I tokenized the tweets and converted it to sequence and then post padded the sequence with zero and kept the length of largest sequence in tweets as maximum length. After Preprocessing the data, the Tweet dataset has dimension of (37041, 286). For Model Selection, I build 3 different models consisting of one Baseline model such as Multinomial Naive Bayes and 2 advanced Recurrent Neural Network models such as GRU Architecture with a single Embedding layer, 1 Bidirectional layer followed by Global Average Pooling 1D and 2 Dense layers & LSTM Architecture with a single Embedding layer followed by 2 Bidirectional layers and 2 Dense layers. In addition to this, I also tried applying Dropout with a 40% dropout rate during training of RNN models and Early Stopping method for preventing overfitting and evaluated that Early Stopping gave better results than Dropout. For evaluation of models, I splitted the dataset into training,testing and validation split with (80,10,10) ratio and calculated F1 macro, AUC Score on test data and using the Confusion Matrix, I calculated the accuracy by dividing the sum of diagonal elements by the sum of all elements. In addition to this, I plotted training vs. validation loss and accuracy graphs to visualize the performance of models. Interestingly, by not implementing the preprocessing techniques like removing stopwords, Porter Stemmer or WordNetLemmatizer and using just basic text cleaning function in the RNN model with LSTM architecture, the accuracy of the model was increased from 73.87% to 77.1% and had AUC score of 0.95.

  Language:Jupyter Notebook2

• sarveshwar-s/pump_anomaly_detection

  Detects the anomalies in water pump sensors

  Language:Jupyter Notebook1

• khundman/telemanom

  A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

  Language:Jupyter Notebook1k249

• javieraviles/node-typescript-koa-rest

  REST API boilerplate using NodeJS and KOA2, typescript. Logging and JWT as middlewares. TypeORM with class-validator, SQL CRUD. Docker included. Swagger docs, actions CI and valuable README

  Language:TypeScript1k173

• imironica/liveness

  Liveness detection experiments

  Language:Python5316

• vakidzaci/color_texture_analysis

  Language:Python31

• Elroborn/awesome-face-anti-spoofing

  😎 face anti-spoofing releated algorithm, dataset and paper

  459

• DerrickFeiWang/HandwritingRecognition_GoogleCloudVision

  Language:Jupyter Notebook337