alexklibisz

<p>Test</p>

alexklibisz

FROM ruby:2.2.4

RUN apt-get update -y
RUN apt-get install ruby-full -y
RUN apt-get install git -y
RUN gem install dpl
RUN gem install aws-sdk
RUN gem install aws-sdk-v1

RUN mkdir /usr/src/app

alexklibisz

Preface and Problem

There is a project that I've spent the last two to three months working on that uses Firebase. The project includes a web app and iOS application and focuses heavily on real-time user interaction. We've really enjoyed working with Firebase and the Firebase web and iOS SDKs. It makes this real-time programming much simpler than rolling our own data-syncing solution for the server and multiple client language.

It will soon (in the next two weeks) be time to release the project, and we have no effective way in place to back up our data. Firebase offers a "private backups" feature for the "Bonfire" plan, but we obviously don't want to pay the $150 / month until we absolutely have to. Until we reach a point where we will use the Bonfire plan, we are forced to roll our own solution.

The Goal

Must-haves:

alexklibisz

Alex Klibisz

alexklibisz

I found the concept of confidence intervals tricky to grasp in my statistics class this semester.

I found a particularly good introduction here: https://www.youtube.com/watch?v=vWFdiGg7f6k,

and a good example here: https://www.youtube.com/watch?v=8sR1iTQAbrY

Meaning of a confidence interval:

• You want to estimate some value (either a proportion or a mean) for an entire population.
• So you want the true value for the universe.

alexklibisz

const restaurants = [
  {
    "name": "Copper Cellar",
    "food_type": "Burgers",
    "neighborhood": "UT Campus"
  },
  {
    "name": "Soccer Taco",
    "food_type": "Mexican",
    "neighborhood": "Market Square"

alexklibisz

function matchFilter(allItems, query, threshold) {
  // Create an array of properties that are defined in the query.
  // For the example, it will be [food_type, neighborhood]
  const properties = Object.keys(query)
    .filter(key => query[key].trim().length > 0);

  // Create a comparison string for the query item.
  // For the example, it will be "Mxicanmarketsquare"
  const queryComp = properties.map(p => query[p]).join();

alexklibisz

input: "data"
input_shape{dim: 1 dim: 1 dim: 128 dim: 128 dim: 64}

layer {
  name: "conv_in128_chan16"
  type: "Convolution"
  bottom: "data"
  top: "conv_in128_chan16"
  param {
    lr_mult: 1.0

alexklibisz

def weighted_log_loss(yt, yp):
    '''Log loss that weights false positives or false negatives more.
    Punish the false negatives if you care about making sure all the neurons
    are found and don't mind some false positives. Vice versa for punishing
    the false positives. Concept taken from the UNet paper where they
    weighted boundary errors to get cleaner boundaries.'''

    emphasis = 'fn'
    assert emphasis in ['fn', 'fp']
    m = 2

alexklibisz

name: 'phseg_v5'
force_backward: true
layers {                 top: 'data' top: 'label'  name: 'loaddata'       type: HDF5_DATA  hdf5_data_param { source: 'aug_deformed_phseg_v5.txt' batch_size: 1 } include: { phase: TRAIN }}

layers { bottom: 'data'              top: 'd0b'   name: 'conv_d0a-b'     type: CONVOLUTION  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0  convolution_param { num_output: 64 pad: 0 kernel_size: 3 engine: CAFFE weight_filler { type: 'xavier' }} }
layers { bottom: 'd0b'               top: 'd0b'   name: 'relu_d0b'       type: RELU }
layers { bottom: 'd0b'               top: 'd0c'   name: 'conv_d0b-c'     type: CONVOLUTION  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0  convolution_param { num_output: 64 pad: 0 kernel_size: 3 engine: CAFFE weight_filler { type: 'xavier' }} }
layers { bottom: 'd0c'               top: 'd0c'   name: 'relu_d0c'       type: RELU }

layers { bottom: 'd0c'               top: 'd1a'   name: 'pool_d0c-1a'    type: POOLING  pooling_param { pool: MA