rocknrollnerd

import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams
from lasagne.updates import adam
from lasagne.utils import collect_shared_vars

from sklearn.datasets import fetch_mldata
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing

ndronen

#!/usr/bin/env python
"""
A quick, partial implementation of ENet (https://arxiv.org/abs/1606.02147) using PyTorch.
The original Torch ENet implementation can process a 480x360 image in ~12 ms (on a P2 AWS
instance).  TensorFlow takes ~35 ms.  The PyTorch implementation takes ~25 ms, an improvement
over TensorFlow, but worse than the original Torch.
"""

from __future__ import absolute_import

rtqichen

## Weight norm is now added to pytorch as a pre-hook, so use that instead :)

import torch
import torch.nn as nn
from torch.nn import Parameter
from functools import wraps

class WeightNorm(nn.Module):
    append_g = '_g'
    append_v = '_v'

oeway

import numpy as np
import scipy
import scipy.ndimage
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage.interpolation import map_coordinates
import collections
from PIL import Image
import numbers

__author__ = "Wei OUYANG"

DrustZ

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter

def debug(debug_open, x, layername):
    if debug_open:
        print x.size(), 'after', layername

class PVANet(nn.Module):

gpantalos

"""
Laplace approximation of a Beta distribution.
"""
import matplotlib.pyplot as plt
import torch

x = torch.linspace(0, 1, 200)
p = torch.distributions.Beta(2, 5)

mlelarge

nilsleh

import numpy as np
import torch
import torch.nn as nn
from functorch import vmap, jacrev, make_functional_with_buffers

batch_size = 2
in_channels = 5
out_channels = 20
feature_shape = 8
feature = torch.rand(batch_size, in_channels, feature_shape, feature_shape)

codelahoma

(DEFUNCT - NO LONGER WORKS) YouTube Video Summarizer (yt_summarize.py)

This gist contains a Python script that generates a transcript or summary of a YouTube video. It fetches video information, transcribes the audio using the Whisper ASR model, and generates a summary using the OpenAI language model.

Features

• Fetch YouTube video information (title, description, channel title, etc.)
• Transcribe video audio
• Generate a summary of the video transcript
• Save output as a markdown file