gumi Inc. が Erlang & Elixir Fest 2018 の会場で配布しているクイズを掲載しています。
gumimaze.md 本記事について gumi Inc. が Erlang & Elixir Fest 2018 の会場で配布しているクイズを掲載しています。 Story あなたはプログラマーとして、とあるゲーム開発プロジェクトに配属された。 プロジェクトに配属され、はじめに与えられた業務は、次のような ASCII アートで記述された迷路を入力として受け取り、点数を出力するコマンドを作成することだった。 この業務には前任者がいたようだが、現在は連絡がとれない。幸いなことにコードは残されているようだ。 Input maze.txt: WWWWWWWWWW WSWW 1 W W WWW W W 1 W W 1WWWW W W W 1 W W 1 W W W WWW1WWW W W 1 1GW WWWWWWWWWW 記号の意味 記号 意味 S スタート地点 G ゴール地点 空白スペース 通路
GitHub - coells/100days: 100 days of algorithms
This repository contains notebooks with live code to accompany 100 days of algorithms challenge. I set the challenge for myself to implement algorithm by algorithm, day by day, until the number reaches 100. If you are interested, here's the intro to the series and all the articles sorted by date from the latest. The challenge was quite fun and rough, as well. Do not expect the implementations to b
GitHub - DT42/BerryNet: Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of d
GitHub - BilalReffas/EmojiIntelligence: Neural Network built in Apple Playground using Swift
I used this challenge to learn more about neural networks and machine learning. A neural network consists of layers, and each layer has neurons. My network has three layers: an input layer, a hidden layer, and an output layer. The input to my network has 64 binary numbers. These inputs are connected to the neurons in the hidden layer. The hidden layer performs some computation and passes the resul
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deep-learning-book/README.md at master · rasbt/deep-learning-book
GitHub - rasbt/deep-learning-book: Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
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