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View gist:19be89d7744b99704e7ad251acf252b1
2/17 I learned that SQL is a relational database (much like a spreadsheet). I also learned that documents llike this are useful to prove knowledge, as well as helping concretize knowledge and have a place of reference for things in case I forget something.
View output_log.txt
Log uploaded on Sunday, February 17, 2019, 1:15:16 PM
Loaded mods:
RuntimeGC: RuntimeGC(20.0.15.1)
HugsLib[ov:6.1.1]: 0Harmony(1.2.0.1), HugsLib(av:1.0.0.0,fv:6.1.1)
[FSF] Complex Jobs: (no assemblies)
Color Coded Mood Bar: 0Harmony(1.2.0.1), ColoredMoodBar(1.0.0.0)
Colonist Bar KF 1.0: 0Harmony(1.2.0.1), 0KillfaceTools(1.0.0.0), ColonistBarKF(1.0.0.0)
Blueprints: Blueprints(av:2.0.0.0,fv:2.2.39)
Additional Joy Objects Classic: AdditionalJoyObjects(1.0.0.0)
[1.0] DE Surgeries: (no assemblies)
View dog_breed_simple_cnn_train.py
from keras.callbacks import ModelCheckpoint
### specify the number of epochs that you would like to use to train the model.
epochs = 5
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5',
verbose=1, save_best_only=True)
model.fit(train_tensors, train_targets,
validation_data=(valid_tensors, valid_targets),
View forever.js
const { spawn } = require("child_process");
const { join } = require("path");
const chalk = require("chalk");
const watermark = (name, pid, message) => {
const iso = new Date().toISOString();
const mark = chalk.bold(`[${iso}|${name}#${pid}] `);
const lines = ("" + message)
.split(/\n/g)
View app.js
import React, {Component} from 'react';
import {Platform, StyleSheet, Text,TextInput, View, Button, Linking} from 'react-native';
type Props = {};
export default class App extends Component<Props> {
constructor(props) {
super(props);
this.state = {
noHP: ''
};
View procedural-terrain-rendering.markdown
View procedural-terrain-rendering.markdown
View procedural-terrain-rendering.markdown
View PY0101EN-1-2-Strings.ipynb
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View dog_breed_simple_cnn.py
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
model = Sequential()
### TODO: Define your architecture.
# image is 224×224x3 pixels
model.add(Conv2D(filters=16, kernel_size=(2,2), input_shape=(224,224,3)))
model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same', data_format=None))