| """ | |
| DyNet implementation of a sequence labeler (POS taggger). | |
| This is a translation of this tagger in PyTorch: https://gist.github.com/hal3/8c170c4400576eb8d0a8bd94ab231232 | |
| Basic architecture: | |
| - take words | |
| - run though bidirectional GRU | |
| - predict labels one word at a time (left to right), using a recurrent neural network "decoder" | |
| The decoder updates hidden state based on: | |
| - most recent word |
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FFmpeg is one of the most powerful tools for video transcoding and manipulation, but it's fairly complex and confusing to use. That's why I decided to create this cheat sheet which shows some of the most often used commands.
Let's start with some basics:
ffmpeg calls the FFmpeg application in the command line window, could also be the full path to the FFmpeg binary or .exe file
| PostgreSQL Data Types | AWS DMS Data Types | Redshift Data Types | |
|---|---|---|---|
| INTEGER | INT4 | INT4 | |
| SMALLINT | INT2 | INT2 | |
| BIGINT | INT8 | INT8 | |
| NUMERIC (p,s) | If precision is 39 or greater, then use STRING. | If the scale is => 0 and =< 37 then: NUMERIC (p,s) If the scale is => 38 and =< 127 then: VARCHAR (Length) | |
| DECIMAL(P,S) | If precision is 39 or greater, then use STRING. | If the scale is => 0 and =< 37 then: NUMERIC (p,s) If the scale is => 38 and =< 127 then: VARCHAR (Length) | |
| REAL | REAL4 | FLOAT4 | |
| DOUBLE | REAL8 | FLOAT8 | |
| SMALLSERIAL | INT2 | INT2 | |
| SERIAL | INT4 | INT4 |
| import pandas as pd | |
| import numpy as np | |
| #setting up a comparable dataframe | |
| df = pd.DataFrame(np.random.randint(20,100,size=(50, 4)), columns=['A','B','C','D']) | |
| #these two columns become a multi-column index | |
| df['year_idx'] = np.random.randint(2000,2004,50) | |
| df['id_idx'] = np.random.randint(10000,19999,50) | |
| df.drop_duplicates(subset=['year_idx','id_idx'],inplace=True) |
| """ 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 |
| """ | |
| 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) |
| """ | |
| Fast non-maximum suppression numpy port | |
| from: | |
| http://quantombone.blogspot.com/2011/08/blazing-fast-nmsm-from-exemplar-svm.html | |
| """ | |
| import numpy as np |
| """ | |
| Use a Counter to find the most common words in "The Wonderful Wizard of Oz" by | |
| L. Frank Baum. | |
| Available in (mostly) plain text at: | |
| https://archive.org/stream/wonderfulwizardo00baumiala/wonderfulwizardo00baumiala_djvu.txt | |
| Note: This code also counts the words in the header, so it's not a *realistic* | |
| applicaton, but more of a demonstration of python's Counter. |
| """ | |
| This module implements the Lowess function for nonparametric regression. | |
| Functions: | |
| lowess Fit a smooth nonparametric regression curve to a scatterplot. | |
| For more information, see | |
| William S. Cleveland: "Robust locally weighted regression and smoothing | |
| scatterplots", Journal of the American Statistical Association, December 1979, |
