gowrishankarin

absl-py @ file:///home/conda/feedstock_root/build_artifacts/absl-py_1705494584803/work
aiobotocore @ file:///home/conda/feedstock_root/build_artifacts/aiobotocore_1709590248257/work
aiofiles==23.2.1
aiohttp @ file:///Users/runner/miniforge3/conda-bld/aiohttp_1707669852329/work
aioitertools @ file:///home/conda/feedstock_root/build_artifacts/aioitertools_1663521246073/work
aiosignal @ file:///home/conda/feedstock_root/build_artifacts/aiosignal_1667935791922/work
alabaster @ file:///home/conda/feedstock_root/build_artifacts/alabaster_1704848697227/work
alembic==1.13.1
altair @ file:///home/conda/feedstock_root/build_artifacts/altair_1701735522225/work
annotated-types @ file:///home/conda/feedstock_root/build_artifacts/annotated-types_1696634205638/work

gowrishankarin

def build_encoder_with_sampling_layer(shape=(6)):

    encoder_inputs = keras.layers.Input(shape=shape, name="input_layer") 
    x = keras.layers.Dense(5, activation="relu", name="h1")(encoder_inputs)
    x = keras.layers.Dense(5, activation="relu", name="h2")(x)
    x = keras.layers.Dense(4, activation="relu", name="h3")(x)

    z_mean = keras.layers.Dense(latent_dim, name="z_mean")(x)
    z_log_var = keras.layers.Dense(latent_dim, name="z_log_var")(x)
    z = Sampling()([z_mean, z_log_var])

gowrishankarin

<script
  type="text/javascript"
  async
  src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/MathJax.js?config=TeX-MML-AM_CHTML"
></script>
<script type="text/x-mathjax-config">
  MathJax.Hub.Config({
    tex2jax: {
      inlineMath: [ ['$','$'], ["\\(","\\)"] ],
      processEscapes: true

gowrishankarin

scores = ["81", "58", "1*", "16", "30*", "40", "91", "51", "60*", 
          "12", "9", "16*", "57", "28*", "15", "16", "56", "40"]
notouts = [
    1 if a_score.find("*") != -1 else 0 for a_score in scores 
]
outs = (len(scores) - sum(notouts))
total_score = sum([
     int(score.split("*")[0]) if is_out else int(score) for score, is_out in zip(scores, notouts)
])
print(f'Jaddu\'s Average in the last {len(scores)} innings is {total_score/outs}')

gowrishankarin

1. **Don't panic!**
2. If your computer is hanged over login screen, press Crtl+Alt+f2
3. It will ask for your login username and password.
4. Connect ethernet cable (LAN cable) from your wifi router or modem to your computer
4. Type "dhclient eth0" and press enter
5. For testing networking, try "ping www.google.com" if there's any failure then please check your lan connection,router or modem (
Connecting to WiFi is a tedious process so prefer LAN connection)
6. Type "reboot" (without quotes) and press enter.
7. Now keep pressing "Esc" or "Shift" key.
8. Enter into latest kernel recovery mode.

gowrishankarin

# https://www.kaggle.com/c/grupo-bimbo-inventory-demand/discussion/23863#136641

library(data.table)
library(xgboost)

train = fread('_data/train.csv', select=c("Semana", 'Cliente_ID', 'Producto_ID',
    'Agencia_ID', 'Ruta_SAK', 'Demanda_uni_equil'))
test = fread('_data/test.csv', select=c("Semana", 'id', 'Cliente_ID', 'Producto_ID',
    'Agencia_ID', 'Ruta_SAK'))

gowrishankarin

library((neuralnet)
library(NeuralNetTools)
CAPITAL_LETTER <- c(rep(0, 7), 1)
SMALL_LETTER <- c(0, rep(1, 7))
binaryData <- data.frame(
    expand.grid(c(0, 1), c(0, 1), c(0, 1)), CAPITAL_LETTER, SMALL_LETTER
)
colnames(binaryData) <- c("X_Points", "Y_Points", "Velocity", "CAPITAL_LETTER", "SMALL_LETTER")
mod <- neuralnet(
    CAPITAL_LETTER + SMALL_LETTER ~ X_Points + Y_Points + Velocity, 

gowrishankarin

@media screen and (min-width: 1px) and (max-width: 400px) {
    /*
    This seems like a good place for code... Make sure you change the breakpoints!
    */
    body {
        background: #ff0000;
    }
}

@media screen and (min-width: 401px) and (max-width: 599px) {

gowrishankarin

function startAMQPReceiver() {
    amqp.connect('amqp://' + CustomConfig.CC.aims.domain).then(function(conn) {
        process.once('SIGNT', function() {
            conn.close();
        });

        return conn.createChannel().then(function(ch) {
            var ok = ch.assertQueue('heartspots', {
                durable: true
            });

gowrishankarin

const fs = require('fs');
const http = require('http');
const crypto = require('crypto');

const image_location = 'some-image-in-current-folder.jpg';

const config = {
	recognizeIm: {
		clientId: '',
		apiKey: '',