Ely-S

import * as tf from '@tensorflow/tfjs';
import { Tensor3D, Tensor2D, Tensor1D, Tensor4D, Tensor } from '@tensorflow/tfjs';


function get1dGaussianKernel(sigma, size): Tensor1D {
    // Generate a 1d gaussian distribution across a range
    var x = tf.range(Math.floor(-size / 2) + 1, Math.floor(size / 2) + 1)
    x = tf.pow(x, 2)
    x = tf.exp(x.div(-2.0 * (sigma * sigma))) as Tensor1D
    x = x.div(tf.sum(x))

ThabetAmer

#
# Based on https://hub.docker.com/_/sonarqube
#

version: "3.7"

services:

  sonarqube:
    container_name: sonarqube

blzq

def gaussian_blur(img, kernel_size=11, sigma=5):
    def gauss_kernel(channels, kernel_size, sigma):
        ax = tf.range(-kernel_size // 2 + 1.0, kernel_size // 2 + 1.0)
        xx, yy = tf.meshgrid(ax, ax)
        kernel = tf.exp(-(xx ** 2 + yy ** 2) / (2.0 * sigma ** 2))
        kernel = kernel / tf.reduce_sum(kernel)
        kernel = tf.tile(kernel[..., tf.newaxis], [1, 1, channels])
        return kernel

    gaussian_kernel = gauss_kernel(tf.shape(img)[-1], kernel_size, sigma)

evancz

A vision for data interchange in Elm

How do you send information between clients and servers? What format should that information be in? What happens when the server changes the format, but the client has not been updated yet? What happens when the server changes the format, but the database cannot be updated?

These are difficult questions. It is not just about picking a format, but rather picking a format that can evolve as your application evolves.

Literature Review

By now there are many approaches to communicating between client and server. These approaches tend to be known within specific companies and language communities, but the techniques do not cross borders. I will outline JSON, ProtoBuf, and GraphQL here so we can learn from them all.

DaniSancas

Neo4j Tutorial

Fundamentals

Store any kind of data using the following graph concepts:

• Node: Graph data records
• Relationship: Connect nodes (has direction and a type)
• Property: Stores data in key-value pair in nodes and relationships
• Label: Groups nodes and relationships (optional)

stewartpark

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np 

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])

model = Sequential()
model.add(Dense(8, input_dim=2))