shagunsodhani

Deep Convolutional Generative Adversarial Nets

Introduction

• The paper presents Deep Convolutional Generative Adversarial Nets (DCGAN) - a topologically constrained variant of conditional GAN.
• Link to the paper

Benefits

• Stable to train

augbog

Free O'Reilly books and convenient script to just download them.

Thanks /u/FallenAege/ and /u/ShPavel/ from this Reddit post

How to use:

1. Take the download.sh file and put it into a directory where you want the files to be saved.
2. cd into the directory and make sure that it has executable permissions (chmod +x download.sh should do it)
3. Run ./download.sh and wee there it goes. Also if you do not want all the files, just simply comment the ones you do not want.

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

tomysmile

type below:

brew update
brew install redis

To have launchd start redis now and restart at login:

brew services start redis

carlos-aguayo

import gym
import pandas as pd
import numpy as np
import random

# https://gym.openai.com/envs/CartPole-v0
# Carlos Aguayo - [email protected]


class QLearner(object):

colah

Understanding LSTMs

• Japanese: qiita
• Russian: Alex Sosn
• Chinese: jianshu, csdn, csdn, Yangyu Chen

beradrian

# set a proxy
set HTTP_PROXY=
set HTTPS_PROXY=%HTTP_PROXY%
npm config set proxy %HTTP_PROXY%
npm config set https.proxy %HTTPS_PROXY%
npm config set https-proxy %HTTPS_PROXY%
git config --global http.proxy %HTTP_PROXY%
git config --global https.proxy %HTTPS_PROXY%

# unset proxy

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):

craffel

import matplotlib.pyplot as plt

def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])
    

otobrglez

# Simple Recommendation Engine in Ruby
# Visit: http://otobrglez.opalab.com
# Author: Oto Brglez <[email protected]>

class Book < Struct.new(:title)

  def words
    @words ||= self.title.gsub(/[a-zA-Z]{3,}/).map(&:downcase).uniq.sort
  end