tzmartin

1. Copy m3u8 link

2. Run command

echo "Enter m3u8 link:";read link;echo "Enter output filename:";read filename;ffmpeg -i "$link" -bsf:a aac_adtstoasc -vcodec copy -c copy -crf 50 $filename.mp4

culurciello

-- Xception model
-- a Torch7 implementation of: https://arxiv.org/abs/1610.02357
-- E. Culurciello, October 2016

require 'nn'
local nClasses = 1000

function nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW, kH)
   local block = nn.Sequential()
   block:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(nInputPlane), kW,kH, 1,1, 1,1))

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')

gunjanpatel

Revert the full commit

Sometimes you may want to undo a whole commit with all changes. Instead of going through all the changes manually, you can simply tell git to revert a commit, which does not even have to be the last one. Reverting a commit means to create a new commit that undoes all changes that were made in the bad commit. Just like above, the bad commit remains there, but it no longer affects the the current master and any future commits on top of it.

git revert {commit_id}

About History Rewriting

Delete the last commit

Deleting the last commit is the easiest case. Let's say we have a remote origin with branch master that currently points to commit dd61ab32. We want to remove the top commit. Translated to git terminology, we want to force the master branch of the origin remote repository to the parent of dd61ab32:

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

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

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

karpathy

""" 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

fchollet

'''Functional Keras is a more functional replacement for the Graph API.
'''

###################
# 2 LSTM branches #
###################
a = Input(input_shape=(10, 32))  # output is a TF/TH placeholder, augmented with Keras attributes
b = Input(input_shape=(10, 32))
encoded_a = LSTM(32)(a)  # output is a TF/TH tensor
encoded_b = LSTM(32)(b)

myungsub

Vision & Language

• Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images

  • Mateusz Malinowski, Marcus Rohrbach, Mario Fritz

• Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books

  • Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler

• Learning Query and Image Similarities With Ranking Canonical Correlation Analysis

• Wah Ngo