| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import itertools | |
| %matplotlib inline | |
| lim = 20 | |
| n_points = 100000 | |
| t1 = np.linspace(-lim, lim, int(np.sqrt(n_points))) | |
| t2 = np.linspace(-lim, lim, int(np.sqrt(n_points))) |
| [[January 1st, 2019]] | |
| [[January 2nd, 2019]] | |
| [[January 3rd, 2019]] | |
| [[January 4th, 2019]] | |
| [[January 5th, 2019]] | |
| [[January 6th, 2019]] | |
| [[January 7th, 2019]] | |
| [[January 8th, 2019]] | |
| [[January 9th, 2019]] | |
| [[January 10th, 2019]] |
| <table style="width: 80%; margin:auto"> | |
| <tr> | |
| <td contenteditable style="width: 50%; border: 1px solid lightgrey; padding: 20px; vertical-align: top;"></td> | |
| <td contenteditable style="width: 50%; border: 1px solid lightgrey; padding: 20px; vertical-align: top;"></td> | |
| </tr> | |
| <tr> | |
| <td contenteditable style="width: 50%; border: 1px solid lightgrey; padding: 20px; vertical-align: top;"></td> | |
| <td contenteditable style="width: 50%; border: 1px solid lightgrey; padding: 20px; vertical-align: top;"></td> |
| expertly-sophisticated digital collectives | |
| translucent calculus | |
| alternative-influence freedom cages | |
| diversified narration | |
| hypothetically-indicative digital collectives | |
| optimised freedom cages | |
| institutional gimmickry | |
| k-selected freedom cages | |
| utopian late capitalism | |
| k-selected observance |
| fungibility | |
| tegmark | |
| smuggles | |
| cosmically | |
| bioethicist | |
| shackling | |
| inconceivably | |
| uncontroversial | |
| untestable | |
| paperclip |
| """ | |
| 1. For the fit and evaluate methods, make verbose=2 the default (rather than verbose=1). | |
| I ignore the checkmarks produced by verbose=1, and they seem to freeze the Jupyter notebook. Frustrating. | |
| 2. Save IMDB as pickle upon download, rather than npz. Loading it into memory then takes around 6 seconds rather than 60 seconds. | |
| """ | |
| ### Usual imports | |
| import pandas as pd |
| # Original | |
| model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3))) | |
| model.add(layers.MaxPooling2D((2, 2))) | |
| model.add(layers.Conv2D(64, (3, 3), activation='relu')) | |
| model.add(layers.MaxPooling2D((2, 2))) | |
| model.add(layers.Conv2D(128, (3, 3), activation='relu')) | |
| model.add(layers.MaxPooling2D((2, 2))) | |
| model.add(layers.Conv2D(128, (3, 3), activation='relu')) | |
| model.add(layers.MaxPooling2D((2, 2))) | |
| model.add(layers.Flatten()) |
Write a 50k-word novel in November.
| # -*- coding: utf-8 -*- | |
| # Natural Language Toolkit: Generating from a CFG | |
| # | |
| # Copyright (C) 2001-2017 NLTK Project | |
| # Author: Steven Bird <stevenbird1@gmail.com> | |
| # Peter Ljunglöf <peter.ljunglof@heatherleaf.se> | |
| # URL: <http://nltk.org/> | |
| # For license information, see LICENSE.TXT | |
| # | |
| from __future__ import print_function |
| S -> Company 'for' Market | Modifier 'version of' Company | 'Improve' Company 'with' Concept | 'Use' Concept 'to help' Market | 'Create a' Resource 'for' Noun | 'Improve the' Quality 'of' Company | 'Improve' Need 'with' Tool | 'Improve' Need 'for' Market | 'Help' Market Improve | |
| Tool -> Company | Resource | Concept | |
| Noun -> Company | Market | | |
| Company -> 'Uber' | 'Google' | 'Facebook' | 'Youtube' | |
| Market -> 'golfers' | 'programmers' | 'home cooks' | 'nomads' | 'writers' | 'researchers' | 'engineers' | 'minimalists' | |
| Modifier -> 'absurd' | 'kind' | 'space-age' | 'simpler' | |
| Concept -> 'context-free grammar' | 'deep learning' | 'physics simulation' | |
| Resource -> 'community' | 'book' | 'market' | 'toolkit' | 'training tool' | 'search engine' | 'knowledge base' | 'up-to-date list' | 'messaging system' | 'notification system' | 'newsletter' | 'club' | |
| Quality -> 'speed' | 'cost' | 'quality' |