Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

Last update: Nov 27, 2021

Overview

BTC_LightGBM

Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

LightGBM is a gradient boosting algorithm that is based on decision tree learning algorithms. Boosting algorithms include generating and detecting weak learners and then combining all of them to form one strong entity. To increase the accuracy of the data set, they are joined together to form a stronger learning attribute. This code snippet has also compared the different types of modes of LightGBM and effectiveness of these models on the dataset.

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Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

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Overview

BTC_LightGBM

Owner

Joint learning of images and text via maximization of mutual information

Image inpainting using Gaussian Mixture Models

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

LRBoost is a scikit-learn compatible approach to performing linear residual based stacking/boosting.

a pytorch implementation of auto-punctuation learned character by character

A PyTorch Toolbox for Face Recognition

Source code for CVPR2022 paper "Abandoning the Bayer-Filter to See in the Dark"

Python script that takes an Impulse response .wav and a input .wav to demonstrate audio convolution.

An implementation of a discriminant function over a normal distribution to help classify datasets.

A Structured Self-attentive Sentence Embedding

Progressive Coordinate Transforms for Monocular 3D Object Detection

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

This is a virtual picture dragging application. Users may virtually slide photos across the screen. The distance between the index and middle fingers determines the movement. Smaller distances indicate click and motion, whereas bigger distances indicate only hand movement.

An implementation of the BADGE batch active learning algorithm.

M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images

Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)

Demo code for paper "Learning optical flow from still images", CVPR 2021.

Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"

LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)