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@HarshTrivedi
HarshTrivedi / pad_packed_demo.py
Last active April 17, 2025 01:26 — forked from Tushar-N/pad_packed_demo.py
Minimal tutorial on packing (pack_padded_sequence) and unpacking (pad_packed_sequence) sequences in pytorch.
import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
# Step 1: Construct Vocabulary
# Step 2: Load indexed data (list of instances, where each instance is list of character indices)
@aparrish
aparrish / understanding-word-vectors.ipynb
Last active April 9, 2025 14:04
Understanding word vectors: A tutorial for "Reading and Writing Electronic Text," a class I teach at ITP. (Python 2.7) Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/
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@zshaheen
zshaheen / travis_to_conda.md
Last active May 28, 2021 18:15
How to Setup Automatic Uploads to Anaconda from Travis CI in 15 minutes

How to Setup Automatic Uploads to Anaconda from Travis CI in 15 minutes

TL;DR: Edit .travis.yaml to install Anaconda and to run conda_upload.sh after testing. Edit meta.yaml to take in the environmental variables $VERSION and $CONDA_BLD_PATH. Create conda_upload.sh which sets the needed environmental variables, builds the tar archive, and uploads it to Anaconda. Finally edit some stuff on your Anaconda and Travis CI account so they can talk.

Intro

The following steps will detail how to automatically trigger Anaconda builds and uploads from Travis CI. This will only upload successful builds in the master branch and if there are multiple commits in a single day, it'll only keep the latest one. Both of these settings can easily be changed.

Edit .travis.yaml

First, edit .travis.yml so that it installs Anaconda.

install:
@moeseth
moeseth / waveform.py
Created October 12, 2015 08:19
Create Soundcloud style waveform from Audio in Python
from pydub import AudioSegment
from matplotlib import pyplot as plot
from PIL import Image, ImageDraw
import numpy as np
import os
src = "./test.mp3"
audio = AudioSegment.from_file(src)
data = np.fromstring(audio._data, np.int16)