ckrapu

{
    "https://www.nvidia.com/": "June 2, 7 p.m. Taiwan Time.\nAI\nJoin NVIDIA Founder and CEO Jensen Huang and SAP CEO Christian Klein on June 4 as they discuss the many ways AI can help transform your business.\nLearn how to manage AI inference at scale through real-world case studies and best practices for ensuring data security, compliance, and innovation.\nDon\u2019t miss the opening keynote when NVIDIA Founder and CEO Jensen Huang joins Snowflake CEO Sridhar Ramaswamy to discuss what the future holds in the era of AI.\nImage Courtesy of Foxconn\nImage courtesy of Accuray\nNVIDIA AI models and microservices advance edge computing, route mapping, and app performance.\n*In-person registration is required.\nLatest AI performance gains and features for RTX AI PCs unveiled at Microsoft Build.\nImage courtesy of: JIYUE\nThe Dawn of a New Industrial Revolution\nBring Out the Best in Your Business With Jensen Huang at SAP Sapphire\nWebinar: Deploying Generative AI in Production\nJensen Huang Talks the Future of 

ckrapu

'''
Script for scraping text documents from NVIDIA's website.
Before running, make sure you've installed the required packages using 
`pip install trafilatura bs4 requests`
'''

import requests
from bs4 import BeautifulSoup
import trafilatura
import json

ckrapu

INSERT INTO USER_SANDBOX.CHRIS_KRAPU.DOMGEO_DAILY_TEST_FOR_SALE (
    RDC_VISITOR_ID
    , MEMBER_ID
    , DOMINANT_GEO
    , MEAN_DIST_KM
    , DG_VIEWS
    , SUM_VIEWS_ACROSS_TOP
    , DG_VIEWS_PCT
    , TOP_VIEWED_GEOS
    , SPATIAL_DISPERSION_INDEX

ckrapu

import pymc as pm
import numpy as np

with pm.Model() as model:

  x = pm.Normal('x', shape=2)
  x_2d = pm.Normal('x_2d', shape=(3,4))

  # Takes variable of shape (2,) and extends it to shape (3,)
  y = pm.Deterministic('y',pm.math.concatenate([x, [0]], axis=0))

ckrapu

{"1": "Corn", "2": "Cotton", "3": "Rice", "4": "Sorghum", "5": "Soybeans", "6": "Sunflower", "10": "Peanuts", "11": "Tobacco", "12": "Sweet Corn", "13": "Pop or Orn Corn", "14": "Mint", "21": "Barley", "22": "Durum Wheat", "23": "Spring Wheat", "24": "Winter Wheat", "25": "Other Small Grains", "26": "Dbl Crop WinWht/Soybeans", "27": "Rye", "28": "Oats", "29": "Millet", "30": "Speltz", "31": "Canola", "32": "Flaxseed", "33": "Safflower", "34": "Rape Seed", "35": "Mustard", "36": "Alfalfa", "37": "Other Hay/Non Alfalfa", "38": "Camelina", "39": "Buckwheat", "41": "Sugarbeets", "42": "Dry Beans", "43": "Potatoes", "44": "Other Crops", "45": "Sugarcane", "46": "Sweet Potatoes", "47": "Misc Vegs & Fruits", "48": "Watermelons", "49": "Onions", "50": "Cucumbers", "51": "Chick Peas", "52": "Lentils", "53": "Peas", "54": "Tomatoes", "55": "Caneberries", "56": "Hops", "57": "Herbs", "58": "Clover/Wildflowers", "59": "Sod/Grass Seed", "60": "Switchgrass", "61": "Fallow/Idle Cropland", "62": "Pasture/Grass", "63": "Fores

ckrapu

import pymc as pm
import numpy as np
import pandas as pd
import arviz as az
import matplotlib.pyplot as plt
import xarray as xr
import aesara

trainSize,testSize = 1000,400
group_num_all = 7

ckrapu

log.ts:301  INFO [remote-connection][ExtensionHost][c4c80…][reconnect] received socket timeout event (unacknowledgedMsgCount: 9776, timeSinceOldestUnacknowledgedMsg: 20002, timeSinceLastReceivedSomeData: 20003).
11:43:34.029 log.ts:301  INFO [remote-connection][ExtensionHost][c4c80…][reconnect] starting reconnecting loop. You can get more information with the trace log level.
11:43:34.029 log.ts:301  INFO [remote-connection][ExtensionHost][c4c80…][reconnect] resolving connection...
11:43:34.030 log.ts:301  INFO [remote-connection][ExtensionHost][c4c80…][reconnect] connecting to 127.0.0.1:44341...
11:43:34.030 log.ts:289 TRACE [remote-connection][ExtensionHost][c4c80…][reconnect][127.0.0.1:44341] 1/6. invoking socketFactory.connect().
11:43:40.202 log.ts:289 TRACE [remote-connection][ExtensionHost][c4c80…][reconnect][127.0.0.1:44341] 2/6. socketFactory.connect() was successful.
11:43:40.203 log.ts:289 TRACE [remote-connection][ExtensionHost][c4c80…][reconnect][127.0.0.1:44341] 3/6. sending AuthRequest control 

ckrapu

with pm.Model() as bnn:
        x_data = pm.Data("x_data", x_input)
        y_data = pm.Data("y_data", y_input)

        #weights and bias prior
        w_1 = pm.Normal("w_1", 0, sigma=1, shape=(layer_in, layer_nodes[0]))
        b_1 = pm.Normal("b_1", 0, sigma=3, shape=1)
        #w_2 = pm.Normal("w_2", 0, sigma=1, shape=(layer_nodes[0], layer_nodes[1]))
        #b_2 = pm.Normal("b_2", 0, sigma=3, shape=1)
        w_out = pm.Normal("w_out", 0, sigma=1, shape=(layer_nodes[1], ))

ckrapu

import numpy as np
import pymc3 as pm
import theano.tensor as tt

def frobenius_norm(X):
    return tt.sum(tt.nlinalg.trace(A@A.T))**0.5

# Create simulated data via forward evolution of system
K        = 3
T        = 10

ckrapu

class Sampling(layers.Layer):
    """Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""

    def call(self, inputs):
        z_mean, z_log_var = inputs
        batch = tf.shape(z_mean)[0]
        dim = tf.shape(z_mean)[1]
        epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
        return z_mean + tf.exp(0.5 * z_log_var) * epsilon