jforge

import cdk = require('@aws-cdk/core');
import greengrass = require('@aws-cdk/aws-greengrass');
import lambda = require('@aws-cdk/aws-lambda');

import { CfnCustomResource } from '@aws-cdk/aws-cloudformation';
import { PolicyStatement, Role, ServicePrincipal, CompositePrincipal } from '@aws-cdk/aws-iam';

import { group_deployment_reset_code } from '../lib/code/group_deployment_reset';
import { thing_vendor_code } from '../lib/code/thing_vendor';

alvations

What the BookCorpus?

So in the midst of all these Sesame Streets characters and robots transforming automobile era of "contextualize" language models, there is this "Toronto Book Corpus" that points to this kinda recently influential paper:

Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. "Aligning books and movies: Towards story-like visual explanations by watching movies and reading books." In Proceedings of the IEEE international conference on computer vision, pp. 19-27.

Why do I even care, there's no translations there?

Some might know my personal pet peeve on collecting translation datasets but this BookCorpus has no translations, so why do I even care about it?

charlesroper

How to setup a practically free CDN using Backblaze B2 and Cloudflare

⚠️ Note 2023-01-21
Some things have changed since I originally wrote this in 2016. I have updated a few minor details, and the advice is still broadly the same, but there are some new Cloudflare features you can (and should) take advantage of. In particular, pay attention to Trevor Stevens' comment here from 22 January 2022, and Matt Stenson's useful caching advice. In addition, Backblaze, with whom Cloudflare are a Bandwidth Alliance partner, have published their own guide detailing how to use Cloudflare's Web Workers to cache content from B2 private buckets. That is worth reading,

soumith

import torch
import torch.nn as nn
import torch.nn.parallel

class DCGAN_D(nn.Container):
    def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0):
        super(DCGAN_D, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

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

blole

#!/usr/bin/env python2
import os
import gym
import sys
import random
import itertools
from time import time
from copy import copy
from math import sqrt, log

shagunsodhani

Fractional Max-Pooling (FMP)

Introduction

• Link to Paper
• Spatial pooling layers are building blocks for Convolutional Neural Networks (CNNs).
• Input to pooling operation is a N_in x N_in matrix and output is a smaller matrix N_out x N_out.
• Pooling operation divides N_in x N_in square into N²_out pooling regions P_{i, j}.
• P_{i, j} ⊂ {1, 2, . . . , N_in} ∀ (i, j) ∈ {1, . . . , N_out}²

Mistobaan

# from https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
def tf_confusion_metrics(model, actual_classes, session, feed_dict):
  predictions = tf.argmax(model, 1)
  actuals = tf.argmax(actual_classes, 1)

  ones_like_actuals = tf.ones_like(actuals)
  zeros_like_actuals = tf.zeros_like(actuals)
  ones_like_predictions = tf.ones_like(predictions)
  zeros_like_predictions = tf.zeros_like(predictions)

nikitakit

This gist has been superceded by Meta Graph functionality that has since been added to tensorflow core.

See: https://www.tensorflow.org/versions/master/api_docs/python/state_ops.html#exporting-and-importing-meta-graphs

The code remains posted for archival purposes only.

rossant