Author: Deep Learner

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Micro-climate predictions: Enabling hyper-local decisions for agriculture and renewables

It is springtime in Eastern Washington, USA, and the temperature is slightly above freezing. A farmer is preparing to fertilize his fields of wheat and lentils as winter runoff and frost are nearly finished. The plants are susceptible to fertilizer at freezing temperatures, so the farmer checks forecasts from the local weather station, which is about 50 miles away. The three-day outlook shows temperatures above freezing. The farmer rents equipment and starts fertilizing the farm. But at night, the temperature […]

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Code

Understanding the Complexity of Metaclasses and their Practical Applications

Metaprogramming is a collection of programming techniques which focus on ability of programs to introspect themselves, understand their own code and modify themselves. Such approach to programming gives programmers a lot of power and flexibility. Without metaprogramming techniques, we probably wouldn’t have modern programming frameworks, or those frameworks would be way less expressive.  This article is an excerpt from the book, Expert Python Programming, Fourth Edition by Michal Jaworski and Tarek Ziade – A book that expresses many years of professional experience in building all kinds of applications […]

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AI-Jobs

The Top Skills for a Career in Datascience in 2021

Datascience is exploding in popularity due to how it’s tethered to the future of technology, supply-demand for high paying jobs and being on the bleeding edge of corporate culture, startups and innovation! Students from South and East Asia especially can fast track lucrative technology careers with data science even as tech startups are exploding in those areas with increased foreign funding. Think carefully. Would you consider becoming a Data Scientist? According to Coursera: A data scientist might do the following […]

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Command-line Tools

Natural language computational chemistry command line interface

Install Must have Open-AI Codex key: export OPENAI_API_KEY= then nlcc key bindings ctrl-w copy to clipboard (Note, you may need to install xsel on linux) ctrl-q help ctrl-o reset context ctrl-z execute python code ctrl-t adjust temperature ctrl-u status ctrl-c quit ctrl-x write to file (same output as copy to clipboard) ctrl-l load from file ctrl-n update number of code responses ctrl-down Enter multiline. escape-enter to enter prompt or ctrl-down to leave GitHub https://github.com/whitead/nlcc    

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Graph

Adaptive Graph Convolution for Point Cloud Analysis

This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph Convolution (AdaptConv) is a point cloud convolution operator presented in our ICCV2021 paper. If you find our work useful in your research, please cite our paper. Installation Classification classification.md Part Segmentation Indoor Segmentation coming soon GitHub https://github.com/hrzhou2/AdaptConv-master    

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Deep Learning

A deep stable learning method for out-of-distribution generalization

StableNet is a deep stable learning method for out-of-distribution generalization. This is the official repo for CVPR21 paper “Deep Stable Learning for Out-Of-Distribution Generalization” and the arXiv version can be found at https://arxiv.org/abs/2104.07876. Introduction Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Therefore, eliminating the impact of distribution shifts between training and testing data is crucial for building performance-promising deep models. Conventional methods assume […]

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Asyncio

Revolt.py: An async library to interact with the revolt.chat api

An async library to interact with the https://revolt.chat api. This library will be focused on making bots and i will not implement anything only for user accounts. Support server: https://app.revolt.chat/invite/FDXER6hr Documentation is here Example More examples in the examples folder import revolt import asyncio import aiohttp class Client(revolt.Client): async def on_message(self, message: revolt.Message): if message.content == “hello”: await message.channel.send(“hi how are you”) async def main():    

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