165 skills found · Page 1 of 6
nordquant / Complete Dbt Bootcamp Zero To HeroSupplementary Materials for the The Complete dbt (Data Build Tool) Bootcamp Udemy course
vsbuffalo / Bds FilesSupplementary files for my book, "Bioinformatics Data Skills"
Seyed-Ali-Ahmadi / Awesome Satellite Benchmark DatasetsSupplementary material for our paper "THERE IS NO DATA LIKE MORE DATA" is provided.
dachengxiaocheng / NDT TransformerThis github is a supplementary material including data, code, trained model and demo for the paper "NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation".
bowang-lab / Joint Ner And ReThis repository contains the corpora and supplementary data, along with instructions for recreating the experiments, for our paper: "End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models".
agohr / Deep SpeckSupplementary code and data to "Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning"
hechtlinger / Graph CnnSupplementary code to "Convolutional Neural Networks Generalization Utilizing the Data Graph Structure"
deepak223098 / Long Term Stock Price Growth Prediction Using NLP On 10 K Financial ReportsA 10-K FInancial Report is a comprehensive report which must be filed annually by all publicly traded companies about its financial performance. These reports are filed to the US Securities Exchange Commission (SEC). This is even more detailed than the annual report of a company. The 10K documents contain information about the Business' operations, risk factors, selected financial data, the Management's discussion and analysis (MD&A) and also Financial Statements and supplementary data. I have been expected to build an NLP pipeline that ingests 10-K reports of various publicly traded companies and build a machine learning model which can uncover the hidden signals to predict the long term stock performance of a company from the 10-K docs using the ‘Loughran McDonald Master Dictionary’. The Dictionary contain words that are specifically curated in the context of financial reports
jcoreyes / EvolvingrlSupplementary Data for Evolving Reinforcement Learning Algorithms
stanis-morozov / ProdigeA supplementary code for Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs.
vrdmr / CS273a Introduction To Machine LearningIntroduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".
monty-se / PINstimationA comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.
BruntonUWBio / PlumetracknetsSupplementary data and code accompanying: https://arxiv.org/abs/2109.12434
GrapheneOS-Archive / AttestationSamplesA small subset of the submitted sample data from https://github.com/GrapheneOS/Auditor. It has a sample attestation certificate chain per device model (ro.product.model) along with a subset of the system properties from the sample as supplementary information.
ropensci / SuppdataGrabbing SUPPlementary DATA in R
BaderLab / Transfer Learning BNER Bioinformatics 2018This repository contains supplementary data, and links to the model and corpora used for the paper: Transfer learning for biomedical named entity recognition with neural networks.
AET-MetallicGlass / Supplementary Data CodesThis folder contains the source codes and data for the metallic glass paper
spatialaudio / Data Driven Audio Signal Processing LectureSupplementary materials to the lecture data driven audio signal processing
RealityBending / TemplateResultsA template for a data analysis folder that can be easily exported as a webpage or as Supplementary Materials
mingzhangPHD / Supplementary DatasetThis is the original dataset that we use in our research work, but it may difficult to find in the original link.
