OCHIISHI Koichiro rakuishi

自動で設定されるフォルダ名を変更する
- https://pc-karuma.net/windows10-rename-user-folder/
設定 → 地域の言語 → 言語の追加 → 日本語 → オプション → 英語キーボード（101/102）
設定 → システム → ディスプレイ → 夜間モード（4:00/3:45）
Ctrl2cap https://technet.microsoft.com/ja-jp/sysinternals/bb897578.aspx

	Month	International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60
	1949-01	112
	1949-02	118
	1949-03	132
	1949-04	129
	1949-05	121
	1949-06	135
	1949-07	148
	1949-08	148
	1949-09	136

Install the Flutter

https://flutter.dev/docs/get-started/install/macos

$ cd ~/Projects
$ unzip ~/Downloads/flutter_macos_v1.9.1+hotfix.2-stable.zip
$ export PATH="$PATH:/Users/rakuishi/Projects/flutter/bin"
$ source ~/.bash_profile
$ flutter precache

	# 確認
	$ node -v
	# homebrew をインストールしていない場合
	$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
	$ brew install nodebrew
	$ nodebrew -v
	$ nodebrew setup
	$ nodebrew install-binary v8.9.4
	$ nodebrew use v8.9.4
	$ echo 'export PATH=$PATH:/Users/rakuishi/.nodebrew/current/bin' >> ~/.bashrc

	# https://medium.com/crypto-currently/lets-build-the-tiniest-blockchain-e70965a248b
	import hashlib as hasher
	import datetime as date

	class Block:
	def __init__(self, index, timestamp, data, previous_hash):
	self.index = index
	self.timestamp = timestamp
	self.data = data
	self.previous_hash = previous_hash

	def homework(train_X, train_y, test_X):
	import keras
	from keras.models import Sequential
	from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Input, Activation, Dropout
	from keras.layers.normalization import BatchNormalization
	from keras import optimizers
	from keras.preprocessing.image import ImageDataGenerator

	gcn_whitening = ImageDataGenerator(samplewise_center=True, samplewise_std_normalization=True)
	gcn_whitening.fit(train_X)

	# -- coding: utf-8 --
	# https://rakuishi.com/archives/getting-started-with-keras/

	from keras.datasets import mnist
	from keras.utils import to_categorical
	from keras.models import Sequential
	from keras.layers import Dense, Activation

	(x_train, y_train), (x_test, y_test) = mnist.load_data()
	x_train.shape, y_train.shape, x_test.shape, y_test.shape

	# 0.1176554
	def homework(train_X, train_Y, tokenizer_en, tokenizer_ja):
	import numpy as np
	from keras.models import Model
	from keras.layers import Input, Embedding, Dense, LSTM

	emb_dim = 256
	hid_dim = 256

	en_vocab_size = len(tokenizer_en.word_index) + 1