steven2358

FFmpeg cheat sheet

A list of useful commands for the FFmpeg command line tool.

Download FFmpeg: https://www.ffmpeg.org/download.html

Full documentation: https://www.ffmpeg.org/ffmpeg.html

Basic conversion

satyajitvg

"""
simple character rnn from Karpathy's blog
"""

import numpy as np

def random_init(num_rows, num_cols):
    return np.random.rand(num_rows, num_cols)*0.01

def zero_init(num_rows, num_cols):

fchollet

import keras
import numpy as np

timesteps = 60
input_dim = 64
samples = 10000
batch_size = 128
output_dim = 64

# Test data.

robertpainsi

Commit Message Guidelines

Short (72 chars or less) summary

More detailed explanatory text. Wrap it to 72 characters. The blank
line separating the summary from the body is critical (unless you omit
the body entirely).

Write your commit message in the imperative: "Fix bug" and not "Fixed
bug" or "Fixes bug." This convention matches up with commit messages

Nikolay-Lysenko

import numpy as np


def xgb_quantile_eval(preds, dmatrix, quantile=0.2):
    """
    Customized evaluational metric that equals
    to quantile regression loss (also known as
    pinball loss).

    Quantile regression is regression that

fchollet

from keras.models import Sequential
from keras.layers import Dense

x, y = ...
x_val, y_val = ...

# 1-dimensional MSE linear regression in Keras
model = Sequential()
model.add(Dense(1, input_dim=x.shape[1]))
model.compile(optimizer='rmsprop', loss='mse')

aabadie

"""Example of usage of Joblib with Amazon S3."""

import s3io
import joblib
import numpy as np

big_obj = [np.ones((500, 500)), np.random.random((1000, 1000))]

# Customize the following values with yours
bucket = "my-bucket"

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

kastnerkyle

raise ValueError("DEPRECATED/FROZEN - see https://github.com/kastnerkyle/tools for the latest")

# License: BSD 3-clause
# Authors: Kyle Kastner
# Harvest, Cheaptrick, D4C, WORLD routines based on MATLAB code from M. Morise
# http://ml.cs.yamanashi.ac.jp/world/english/
# MGC code based on r9y9 (Ryuichi Yamamoto) MelGeneralizedCepstrums.jl
# Pieces also adapted from SPTK
from __future__ import division
import numpy as np

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)