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@HarshTrivedi
HarshTrivedi / pad_packed_demo.py
Last active July 5, 2024 22:47 — 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)
@digiter
digiter / FixCommandForMacKeyboard.md
Last active December 2, 2022 19:48
Map Command Key to Control For ubuntu + mac keyboard

For each machine running xmodmap will give you the mapping and keycode, take a look first then write the script. I use ubuntu and mac keyboard. The below maps both left and right control and super.

Create an .Xmodmap file in your Linux home directory, with the following contents, then execute xmodmap .Xmodmap

clear control
clear mod4

keycode 37 = Super_L
keycode 105 = Super_R
@yosemitebandit
yosemitebandit / 3_regularization.ipynb
Last active June 8, 2017 01:49
udacity neural network course -- assignment 3.4, 3-layer NN with regularization and dropout
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@baraldilorenzo
baraldilorenzo / readme.md
Created January 16, 2016 12:57
VGG-19 pre-trained model for Keras

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

@baraldilorenzo
baraldilorenzo / readme.md
Last active June 13, 2024 03:07
VGG-16 pre-trained model for Keras

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman