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.

Owner

GitHub Repository

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

Related tags

Overview

BTC_LightGBM

Owner

As a part of the HAKE project, includes the reproduced SOTA models and the corresponding HAKE-enhanced versions (CVPR2020).

Few-Shot-Intent-Detection includes popular challenging intent detection datasets with/without OOS queries and state-of-the-art baselines and results.

Contrastive Learning for Compact Single Image Dehazing, CVPR2021

Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent

Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.

Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral]

Graph-based community clustering approach to extract protein domains from a predicted aligned error matrix

Image Segmentation using U-Net, U-Net with skip connections and M-Net architectures

Turn based roguelike in python

Implement face detection, and age and gender classification, and emotion classification.

Code and data to accompany the camera-ready version of "Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation" in EMNLP 2021

[NeurIPS2021] Code Release of K-Net: Towards Unified Image Segmentation

AIR^2 for Interaction Prediction

Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

Code and hyperparameters for the paper "Generative Adversarial Networks"

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

Data, model training, and evaluation code for "PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models".

RodoSol-ALPR Dataset

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