northanapon

import math
from typing import List

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

class TiedAutoEncoder(nn.Module):
    """
    Applies an tied-weight autoencoder to the incoming data.

HarshTrivedi

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)

arthurbeggs

#!/bin/bash

################################################################################
###                       OpenCV2 Installation Script                        ###
################################################################################
#            Source code at https://github.com/arthurbeggs/scripts             #
################################################################################
#                                                                              #
#     Feel free to copy and modify this file. Giving me credit for it is your  #
# choice, but please keep references to other people's work, which I don't     #

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

karpathy

"""
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)

karpathy

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

kylebgorman

import scipy.stats


def mcnemar_p(n1: int, n2: int) -> float:
    """Computes McNemar's test.

    Args:
      n1: the number of "wins" for the first condition.
      n2: the number of "wins" for the second condition.

jetsonhacks

For best results, you should read through the official NVIDIA documentation found on:

https://developer.nvidia.com/linux-tegra-rel-21

In particular, the Quick Start Guide.

For this process you will need:

• A host desktop or laptop computer running Ubuntu Linux 12.04 is officially recommended. In practice, this may be a virtual machine, I have used VirtualBox in the past. Also, I've successfully flashed from Ubuntu Linux 14.04. Your mileage may vary.
• Micro USB cable provided with the Jetson TK1 kit
• Jetson TK1 and power supply