 about everything and celebrating events like IPA Day less if they want to survive.
Skiing and snowboarding are a couple of things most Parkites love and enjoy, but hauling ski equipment to and from different places in the country can be a hassle. How do you package it? How much does it cost to mail it? Can you take it on a plane?
All of these questions can create a panic for someone trying to get their equipment to Park City or any other ski and snowboarding area. As far as airlines go, there is no possibility of carrying on ski equipment on any flight. However, if packaged correctly, ski equipment can be checked in.
If a passenger is part of a "preferred membership" program, most initial bag fees will be waived. Otherwise, charges apply and vary depending on the airline. One ski bag and one ski boot bag count as one piece of luggage on American Airlines, Delta Airlines, JetBlue Airlines, Southwest Airlines and United Airlines.
Flying American means the first checked bag is $25 while the second is an additional $35. The same goes for Delta and United.
Southwest Airlines does not charge for the first two checked bags unless they are over 50 pounds. Excess size charges will not apply to ski equipment, but excess weight charges may.
JetBlue does not charge for the first checked bag, but the second will cost $40, $50 if it exceeds 50 pounds. Skis and snowboards are not subject to standard size requirements but must follow weight and other guidelines.
Aside from flying with ski equipment, it can also be mailed out to wherever you are going beforehand. Several different postal services will ship to and from areas like New York and Chicago assuming the skis or snowboard are about 65 inches long and weigh approximately 15 pounds, including packaging.
UPS will ship skis to or from New York for $88.28, and it will take five business days. To or from Chicago, it will cost $69.36 and take four days. UPS will also charge $16 for packaging if not provided with a case for the equipment.
The United States Postal Service will charge $34.58 to ship equipment to or from New York, but with $500 insurance, the total comes out to $41.98.
FedEx does not have a weight limit but charges $69.57 to ship equipment to or from New York in five business days. To ship to or from Chicago, the cost is $45.63 and takes only two business days.
The United States Post Office on Main Street’s standard shipping rate to or from New York and Chicago is $32, but priority shipping will cost $72. They charge the least, but a clerk at the post office said that while standard shipping is usually about six business days, the holidays will cause a delay in delivery. If skis are mailed out today, Dec. 14, the package may not make it there even by Christmas.
According to a press release from the USPS, post offices across Utah are preparing for what they said is the most hectic day for mailing out packages. "An avalanche of 2.1 million cards and packages are expected to be mailed by Utahns on Monday, Dec. 16, the busiest mailing day of the year."
They said they are projecting 16 percent more packages shipped this holiday season compared to the last, and "more than triple the number of cards and letter on Dec. 16 compared to an average day."
To fly with skis or to mail them out? That is the question. Above all, preparation is required to travel with ski equipment. To mail them out might not be wise at the busiest time of the year, but at other times, it can be a cheaper option that can make traveling a lot easier.VANCOUVER, BRITISH COLUMBIA--(Marketwired - May 8, 2017) - GMV Minerals Inc. (the "Company" or "GMV") (TSX VENTURE:GMV) is pleased to announce that it has received an Interpretive Report summarizing several geophysical surveys completed at the Mexican Hat Gold project in southeast Arizona.
Ian Klassen, President of the Company says "Zonge International's interpretation demonstrates that the known mineralized domains are characterized by both magnetic lows and more conductive zones, as expected for mineralization that is associated with hematite and carbonate in fractured rocks. We are extremely pleased that this mineralization has been traced to depths of at least 500 meters below surface. Other similar geophysical responses can be found in broad areas to the north that have no bedrock exposure and have not yet been drilled. These responses represent terrific exploration targets that require additional follow-up work. Confirmation of shallow cover over the bedrock was expected and supports GMV's low cost exploration and conceptual open pit extraction model. Additional geophysical surveys are being planned to extend the survey over an even broader area."<jupyter_start><jupyter_text>Hyperparameter tuning using HyperDrive<jupyter_code>#Import required libraries
from azureml.core import Workspace, Experiment, Model
from azureml.data.dataset_factory import TabularDatasetFactory
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
from azureml.train.hyperdrive.run import PrimaryMetricGoal
from azureml.train.hyperdrive.policy import BanditPolicy, MedianStoppingPolicy
from azureml.train.hyperdrive.sampling import RandomParameterSampling
from azureml.train.hyperdrive.runconfig import HyperDriveConfig
from azureml.train.hyperdrive.parameter_expressions import uniform, choice
from azureml.widgets import RunDetails
from azureml.train.automl import AutoMLConfig
from azureml.core.dataset import Dataset
from azureml.core.environment import Environment
from azureml.automl.core.shared import constants
import azureml.core
from azureml.core.workspace import Workspace
from azureml.core.dataset import Dataset
from azureml.core.conda_dependencies import CondaDependencies
from azureml.core.model import Model
import numpy as np
import pandas as pd
import os
import shutil
import json
import joblib
import requests
#find the workspace and create an environment with the name "hp"
ws = Workspace.from_config()
exp = Experiment(workspace=ws, name="hp")

print('Workspace name:'+ ws.name, 
      'Azure region:'+ ws.location, 
      'Subscription id:'+ ws.subscription_id, 
      'Resource group:'+ ws.resource_group, sep = '\n')
#start logging on the experiment created
run = exp.start_logging()
# Name the cpu cluster
cpu_cluster_name = "hpcluster"

# Verify whether the cluster already exists
try:
    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
    print('Found existing cluster')

#create a new cluster with a "vm_size='STANDARD_D12_V2' and at most 4 nodes   
except ComputeTargetException:  
    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D12_V2', max_nodes=4)
    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)
    print("New cluster is created")
from azureml.train.hyperdrive.parameter_expressions import randint
from azureml.train.sklearn import SKLearn

#define a new environment from the conda_dependencies.yml file
myenv=Environment.from_conda_specification(name="myenv",
                                                  file_path="./conda_dependencies.yml")

#The early termination policy is defined
#Here the slack factor taken is '0.1' and delay evaluation as 5
policy = BanditPolicy(slack_factor = 0.1, evaluation_interval=2, delay_evaluation=5)

#The RandomParameterSampler was used since it allows for more efficient results
ps = RandomParameterSampling(
    {
        'C':uniform(0,1),
        "max_iter":randint(150)
    })

if "training" not in os.listdir():
    os.mkdir("./training")

#train the scikit-learn model using the train.py script on the cpu cluster already defined
est = SKLearn(source_directory='.',  
              compute_target=cpu_cluster,
              entry_script='train.py', 
              vm_size = 'STANDARD_D12_V2', 
            )

#the model hyperparameters are configured using the estimated model. the hyperparameter sampler, and the termination policy
#The goal is to maximize the 'accuracy' of the model
hyperdrive_config = HyperDriveConfig(estimator=est,
                                     hyperparameter_sampling=ps,
                                     policy=policy,
                                     primary_metric_name='Accuracy',
                                     primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,
                                     max_total_runs=10,
                                     max_concurrent_runs