VikingPenguinYT

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.contrib.distributions import Bernoulli


class VariationalDense:
    """Variational Dense Layer Class"""
    def __init__(self, n_in, n_out, model_prob, model_lam):
        self.model_prob = model_prob

thomwolf

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

nikitakit

"""
Beam decoder for tensorflow

Sample usage:

```
from tf_beam_decoder import  beam_decoder

decoded_sparse, decoded_logprobs = beam_decoder(
    cell=cell,

mattbasta

# -*- coding: utf-8 -*-
"""
    codegen
    ~~~~~~~

    Extension to ast that allow ast -> python code generation.

    :copyright: Copyright 2008 by Armin Ronacher.
    :license: BSD.
"""

teamdandelion

7
2
1
0
4
1
4
9
5
9

application2000

These commands are based on a askubuntu answer http://askubuntu.com/a/581497
To install gcc-6 (gcc-6.1.1), I had to do more stuff as shown below.
USE THOSE COMMANDS AT YOUR OWN RISK. I SHALL NOT BE RESPONSIBLE FOR ANYTHING.
ABSOLUTELY NO WARRANTY.

If you are still reading let's carry on with the code.

sudo apt-get update && \
sudo apt-get install build-essential software-properties-common -y && \
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \

offchan42

ATTENTION PLEASE

This gist has been moved to https://github.com/offchan42/machine-learning-curriculum

Please see that repository instead because you can make pull requests there and later updates will be pushed there too.

===

Machine Learning Curriculum

InnovArul

import torch, torch.nn as nn, torch.nn.functional as F
import numpy as np
import torch.optim as optim

# tied autoencoder using off the shelf nn modules
class TiedAutoEncoderOffTheShelf(nn.Module):
    def __init__(self, inp, out, weight):
        super().__init__()
        self.encoder = nn.Linear(inp, out, bias=False)
        self.decoder = nn.Linear(out, inp, bias=False)

stevenringo

Sessions

Code	Title	Duration	Link
Keynote	Andy Jassy Keynote Announcement Recap	0:01	https://www.youtube.com/watch?v=TZCxKAM2GtQ
Keynote	AWS re:Invent 2016 Keynote: Andy Jassy	2:22	https://www.youtube.com/watch?v=8RrbUyw9uSg
Keynote	AWS re:Invent 2016 Keynote: Werner Vogels	2:16	https://www.youtube.com/watch?v=ZDScBNahsL4
Keynote	[Tuesday Night Live with Jame

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm