Install FFmpeg with homebrew. You'll need to install it with a couple flags for webm and the AAC audio codec.
brew install ffmpeg --with-libvpx --with-libvorbis --with-fdk-aac --with-opus| # use ImageMagick convert | |
| # the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf | |
| convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf |
| def mirror_padding(images, filter_size): | |
| """ | |
| Mirror padding is used to apply a 2D convolution avoiding the border | |
| effects that one normally gets with zero padding. | |
| We assume that the filter has an odd size. | |
| To obtain a filtered tensor with the same output size, substitute | |
| a ``conv2d(images, filters, mode="half")`` with | |
| ``conv2d(mirror_padding(images, filters.shape), filters, mode="valid")``. |
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
| #!/usr/bin/env bash | |
| # | |
| # gh-dl-release! It works! | |
| # | |
| # This script downloads an asset from latest or specific Github release of a | |
| # private repo. Feel free to extract more of the variables into command line | |
| # parameters. | |
| # | |
| # PREREQUISITES | |
| # |
People
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π :heart_eyes: |
π :kissing_heart: |
π :kissing_closed_eyes: |
π³ :flushed: |
π :relieved: |
π :satisfied: |
π :grin: |
π :wink: |
π :stuck_out_tongue_winking_eye: |
π :stuck_out_tongue_closed_eyes: |
π :grinning: |
π :kissing: |
π :kissing_smiling_eyes: |
π :stuck_out_tongue: |
| from sklearn import linear_model | |
| from scipy import stats | |
| import numpy as np | |
| class LinearRegression(linear_model.LinearRegression): | |
| """ | |
| LinearRegression class after sklearn's, but calculate t-statistics | |
| and p-values for model coefficients (betas). | |
| Additional attributes available after .fit() |
Backstory: I decided to crowdsource static site generator recommendations, so the following are actual real world suggested-to-me results. I then took those and sorted them by language/server and, just for a decent relative metric, their Github Watcher count. If you want a heap of other projects (including other languages like Haskell and Python) Nanoc has the mother of all site generator lists. If you recommend another one, by all means add a comment.