A simple python stock Predictor

A simple python stock Predictor Demo Run Locally Clone the project git clone https://github.com/yashraj-n/stock-price-predictor Go to the project directory Install dependencies pip install autots numpy pandas matplotlib seaborn Run Usage/Examples To download dataset,Visit Yahoo Finance, search for your desired stock/cryptocurrency Then, goto historical dataset and click on download

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Simple Weather Check base on Hefeng api, Work on raspberry Pi

Simple Weather Check base on Hefeng api, Work on raspberry Pi 简单基于和风天气的查询 可以被树莓派使用 Use git clone https://github.com/DDizzzy79/LCD1602HeWeatherPull.git Then cd LCD1602HeWeatherPullThen Use [email protected]:~ $ sudo apt-get install i2c-tools    [email protected]:~ $ sudo apt-get install python-smbus    [email protected]:~ $ sudo i2cdetect -y 1  TO find your i2c Ports, Ater that, config The LCD1602.py File , Change The i2c setting to the one you foundAlso, In order to set the ports of i2c between RaspberryPi , Use gpio readall to check   To […]

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An open-source project for long-tail classification

Supported Methods for Long-tailed Recognition: Reproduce Results Here we simply show part of results to prove that our implementation is reasonable. ImageNet-LT Method Backbone Reported Result Our Implementation CE ResNet-10 34.8 35.3 Decouple-cRT ResNet-10 41.8 41.8 Decouple-LWS ResNet-10 41.4 41.6 BalanceSoftmax ResNet-10 41.8 41.4 CE ResNet-50 41.6 43.2 LDAM-DRW* ResNet-50 48.8 51.2 Decouple-cRT ResNet-50 47.3 48.7 Decouple-LWS ResNet-50 47.7 49.3 CIFAR100-LT (Imbalance Ratio 100) ${dagger}$ means the reported results are copied from LADE    

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Free,Cross-platform,Single-file mass network protocol server simulator

Free,Cross-platform,Single-file mass network protocol server simulator Description FaPro is a Fake Protocol Server tool, Can easily start or stop multiple network services. The goal is to support as many protocols as possible, and support as many deep interactions as possible for each protocol. Demo Site Features Supported Running Modes: Local Machine Virtual Network Supported Protocols: DNS DCE/RPC EIP Elasticsearch FTP HTTP IEC 104 Memcached Modbus

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Play Video & Music on Telegram Group Video Chat

ʜᴇʟʟᴏ ❤️🇱🇰 Join us ᴠɪᴅᴇᴏ sᴛʀᴇᴀᴍ ɪs ᴀɴ ᴀᴅᴠᴀɴᴄᴇᴅ ᴛᴇʟᴇʀᴀᴍ ʙᴏᴛ ᴛʜᴀᴛ’s ᴀʟʟᴏᴡ ʏᴏᴜ ᴛᴏ ᴘʟᴀʏ ᴠɪᴅᴇᴏ & ᴍᴜsɪᴄ ᴏɴ ᴛᴇʟᴇɢʀᴀᴍ ɢʀᴏᴜᴘ ᴠɪᴅᴇᴏ ᴄʜᴀᴛ 🧪 ɢᴇᴛ sᴇssɪᴏɴ ɴᴀᴍᴇ ғʀᴏᴍ ʙᴇʟᴏᴡ: Pyrogram ᴘʀᴇᴠɪᴇᴡ ғᴇᴀᴛᴜʀᴇs ᴍᴜsɪᴄ & ᴠɪᴅᴇᴏ sᴛʀᴇᴀᴍ sᴜᴘᴘᴏʀᴛ ᴍᴜʟᴛɪᴄʜᴀᴛ sᴜᴘᴘᴏʀᴛ ᴘʟᴀʏʟɪsᴛ & ǫᴜᴇᴜᴇ sᴜᴘᴘᴏʀᴛ sᴋɪᴘ, ᴘᴀᴜsᴇ, ʀᴇsᴜᴍᴇ, sᴛᴏᴘ ғᴇᴀᴛᴜʀᴇ ᴍᴜsɪᴄ & ᴠɪᴅᴇᴏ ᴅᴏᴡɴʟᴏᴀᴅᴇʀ ғᴇᴀᴛᴜʀᴇ ɪɴʟɪɴᴇ sᴇᴀʀᴄʜ sᴜᴘᴘᴏʀᴛ ʏᴏᴜᴛᴜʙᴇ ᴅɪʀᴇᴄᴛ sᴇᴀʀᴄʜ sᴜᴘᴘᴏʀᴛ    

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Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling

Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas†, and Igor Mordatch† *equal contribution, †equal advising A link to our paper can be found on arXiv. Overview Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.Contains scripts to reproduce experiments. Instructions We provide code in two sub-directories: atari containing code for Atari experiments and gym containing code for OpenAI Gym experiments.See corresponding READMEs in each folder for instructions; scripts should be run […]

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A database-based CDN node supporting PostgreSQL and MongoDB backends

A database-based CDN node supporting PostgreSQL and MongoDB backends. Ubuntu host guide by Digital Ocean. A simple to use database-based deployable CDN node for hobbyist developers who wish to have their own CDN! Setup Clone this repo via this command: git clone https://github.com/justanotherbyte/imoog Go into the imoog/settings.py file and adjust your settings. Examples for both database drivers have been provided in the file. Install a production asgi server of your choice. The 2 I recommend are hypercorn and uvicorn. Installing […]

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The code for Deformable Neural Radiance Fields, a.k.a. Nerfies

This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies. This codebase contains a re-implementation of Nerfies using JAX, building on JaxNeRF. We have been careful to match implementation details and have reproduced the original results presented in the paper. Demo We provide an easy-to-get-started demo using Google Colab! These Colabs will allow you to train a basic version of our method using Cloud TPUs (or GPUs) on Google Colab. Note that due to limited compute resources available, these […]

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