Asteroids atlas of spaceCode, data, and instructions for mapping orbits of asteroids in the solar system
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ContextilyContext geo-tiles in Python
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Ipyvolume3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL
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Geodataviz ToolkitThe GeoDataViz Toolkit is a set of resources that will help you communicate your data effectively through the design of compelling visuals. In this repository we are sharing resources, assets and other useful links.
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Esmpy TutorialBasic tutorial for ESMPy Python package
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EqualareacartogramConverts a Shapefile, GeoJSON, or CSV to an equal area cartogram
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Simple Qa Emnlp 2018Code for my EMNLP 2018 paper "SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach"
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Few Shot Text ClassificationCode for reproducing the results from the paper Few Shot Text Classification with a Human in the Loop
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RustplotlibA pure Rust visualization library inspired by D3.js
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Stanford Project Predicting Stock Prices Using A Lstm NetworkStanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
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Pascal Voc PythonRepository for reading Pascal VOC data in Python, rather than requiring MATLAB to read the XML files.
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CaffeonsparkDistributed deep learning on Hadoop and Spark clusters.
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Py4fiPython for Finance (O'Reilly)
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Wine Deep LearningExploring applications of deep learning to the world of wine
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Spark Nlp ModelsModels and Pipelines for the Spark NLP library
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GeoswiftThe Swift Geometry Engine.
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PytorchPyTorch tutorials A to Z
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Curso data scienceCódigo para el curso "Aprende Data Science y Machine Learning con Python"
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Lstm chemImplementation of the paper - Generative Recurrent Networks for De Novo Drug Design.
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MagnetMAGNet: Multi-agents control using Graph Neural Networks
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Airbnb Dynamic Pricing Optimization[BA project] Dynamic Pricing Optimization for Airbnb listing to optimize yearly profit for host. Use Clustering for competitive analysis, kNN regression for demand forecasting, and find dynamic optimal price with Optimization model.
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Smiles TransformerOriginal implementation of the paper "SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery" by Shion Honda et al.
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Detection Hackathon Apt29Place for resources used during the Mordor Detection hackathon event featuring APT29 ATT&CK evals datasets
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Basketball analyticsRepository which contains various scripts and work with various basketball statistics
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Text objsegCode release for Hu et al. Segmentation from Natural Language Expressions. in ECCV, 2016
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Fcos tensorflowFCOS: Fully Convolutional One-Stage Object Detection.
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SamplevaeMulti-purpose tool for sound design and music production implemented in TensorFlow.
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Viz torch optimVideos of deep learning optimizers moving on 3D problem-landscapes
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Python For Data ScientistsDeliverable: This Jupyter notebook will help aspiring data scientists learn and practice the necessary python code needed for many data science projects.
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Deprecated Boot CampsDEPRECATED: please see individual lesson repositories for current material.
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Book Mlearn GyomuBook sample (AI Machine-learning Deep-learning)
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Kaggle CompetitionsThere are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
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BerkeleyThe Hacker Within at the University of California - Berkeley
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Ml Cv机器学习实战
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WotanAutomagically remove trends from time-series data
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