Describe the various reasons for evaluating the effectiveness of train.pdf
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Describe the various reasons for evaluating the effectiveness of training programs. Name and describe the different ways that learning occurs in a company. Identify strengths and weaknesses of on-the-job training (OJT) as a training method. What are some ways to structure OJT to enhance its effectiveness?.
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{
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"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
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"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "V0RhNGmBjFWt"
},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn"
]
},
{
"cell_type": "code",
"source": [
"# Creating a function f(X) with a slope of -5\n",
"X = torch.arange(-5, 5, 0.2).view(-1, 1)\n",
"func = -5 * X"
],
"metadata": {
"id": "Vq4OWcCNjJFj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Plot the line in red with grids\n",
"plt.plot(X.numpy(), func.numpy(), 'r', label='func')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend()\n",
"plt.grid('True', color='y')\n",
"plt.show()"
],
"metadata": {
"id": "EFDhIKURjPbB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Adding Gaussian noise to the function f(X) and saving it in Y\n",
"Y = func + 1.7 * torch.randn(X.size())"
],
"metadata": {
"id": "c2KRcFl5jRuy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Plot and visualizing the data points in blue\n",
"plt.plot(X.numpy(), Y.numpy(), 'b+', label='Y')\n",
"plt.plot(X.numpy(), func.numpy(), 'r', label='func')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend()\n",
"plt.grid('True', color='y')\n",
"plt.show()"
],
"metadata": {
"id": "ahd47p24jgrB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# defining the function for forward pass for prediction\n",
"def forward(x):\n",
" return w * x"
],
"metadata": {
"id": "T-vtFS9Gjig2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# evaluating data points with Mean Square Error.\n",
"def criterion(y_pred, y):\n",
" return torch.mean( (y_pred - y) ** 2 )"
],
"metadata": {
"id": "DpPaj2Vvjv5A"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"w = torch.tensor(-10.0, requires_grad=True)"
],
"metadata": {
"id": "UMXfOHC0jx9G"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"step_size = 0.1\n",
"loss_list = []\n",
"iter = 20"
],
"metadata": {
"id": "Ml6UCAqujzvm"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for i in range (iter):\n",
" # making predictions with forward pass\n",
" Y_pred = forward(X)\n",
"\n",
"\n",
" # calculating the loss between original and predicted data points\n",
" loss = criterion(Y_pred, Y)\n",
"\n",
"\n",
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types of employees in the hospital. But, an employee must belong to only one type where an
employee cannot do to the job of an employee with different job type. - DOCTOR - is an
EMPLOYEE in the hospital. A doctor has a LicenseNo attribute. - NURSE - is an EMPLOYEE
in the hospital. CareLevel needs to be recorded for each nurse. - CARE_UNIT - is a care center
or station within the hospital. Each CARE_UNIT has a UnitID (identifier), and a UnitName. -
ROOM - is a room in a certain care unit. A ROOM has a RoomNo (identifier), RoomType, and
RoomPrice. - BED - is a hospital bed that may be assigned to a patient that will be admitted to
the hospital. A BED cannot exist without being assigned to a ROOM, so that a BED depends on
the ROOM. A BED has a BedNo (identifier), and a BedType. - PATIENT - a person who is
either admitted to the hospital, or not. With special consideration to the COVID-19 pandemic,
the hospital has only two patient classifications as COVID patients, or NON_COVID patients. A
patient must belong to only one classification. Each PATIENT has the following attributes:
PatientID (identifier), PName (which is composed of FName, and LName), BirthDate, Gender,
and ContactNo. - COVID - is a patient diagnosed with COVID-19 illness. For each COVID
patient, the Status, ExistingCondition (can have many existing conditions), and TravelHistory is
recorded. - NON_COVID - is a patient diagnosed with other illnesses which is not COVID-19.
Each NON_COVID patient has an IllnessType attribute. - COVID_TEST - is a laboratory test
given only to COVID patients. Each COVID_TEST has a TestID (identifier), and a Description.
Your team also noted the following information about relationships among entities: An
EMPLOYEE supervises many employees, or may not supervise at all. But an EMPLOYEE must
be supervised by exactly one employee. A DOCTOR must treat as a minimum one PATIENT;
while, a PATIENT is treated by one or more DOCTORS. Every time a doctor treats a patient, the
TreatDate, TreatDetails (which is composed of Medicine and Dosage), TotalCharges,
InsurancePaid, and AmountDue (which is computed by subtracting the Insurance Paid from the
Total Charges) are recorded. .
Determine whether the following are characteristics-examples of compet.pdf
Determine whether the following are characteristics/examples of competitive and/or non-
competitive enzyme inhibitors. Reduce enzyme activity A. Both competitive and non-
competitive inhibitors Bind to the enzyme active site B. Competitive inhibitor Used in feedback
inhibition C. Noncompetitive inhibition Alter the shape of the active site by binding elsewhere
on the enzyme.
Determine what the key terms refer to in the following study- A study.pdf
Determine what the key terms refer to in the following study. A study was conducted in a local
community to analyze which voters would be likely to vote and how on an upcoming bond.
Statistic a. voted, didn't vote, voted Sample b. all eligible voters in the community Parameter c.
the attendance of a single voter Population d. the number of eligible voters who actually plan to
vote Data e. a group of voters in the community who voted, randomly selected Variable f. the
number of eligible voters in the study who actually plan to vote.
Determine the value of current assets- Determine the value of current.pdf
Determine the value of current assets?
Determine the value of current assets from the information below: Additional Information: i.
Stock on 31 December 2011 was valued at $12500 ii. Accrued salaries amounted to $500 . iii.
Prepaid insurance amounted to $125 . iv. Office Equipment is to be depreciated at 20% per
annum. v. Provision for doubtful debts at 5% of the debtors A / c is to be created..
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include training, testing and evaluation in your code.
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "V0RhNGmBjFWt"
},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn"
]
},
{
"cell_type": "code",
"source": [
"# Creating a function f(X) with a slope of -5\n",
"X = torch.arange(-5, 5, 0.2).view(-1, 1)\n",
"func = -5 * X"
],
"metadata": {
"id": "Vq4OWcCNjJFj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Plot the line in red with grids\n",
"plt.plot(X.numpy(), func.numpy(), 'r', label='func')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend()\n",
"plt.grid('True', color='y')\n",
"plt.show()"
],
"metadata": {
"id": "EFDhIKURjPbB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Adding Gaussian noise to the function f(X) and saving it in Y\n",
"Y = func + 1.7 * torch.randn(X.size())"
],
"metadata": {
"id": "c2KRcFl5jRuy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Plot and visualizing the data points in blue\n",
"plt.plot(X.numpy(), Y.numpy(), 'b+', label='Y')\n",
"plt.plot(X.numpy(), func.numpy(), 'r', label='func')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend()\n",
"plt.grid('True', color='y')\n",
"plt.show()"
],
"metadata": {
"id": "ahd47p24jgrB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# defining the function for forward pass for prediction\n",
"def forward(x):\n",
" return w * x"
],
"metadata": {
"id": "T-vtFS9Gjig2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# evaluating data points with Mean Square Error.\n",
"def criterion(y_pred, y):\n",
" return torch.mean( (y_pred - y) ** 2 )"
],
"metadata": {
"id": "DpPaj2Vvjv5A"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"w = torch.tensor(-10.0, requires_grad=True)"
],
"metadata": {
"id": "UMXfOHC0jx9G"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"step_size = 0.1\n",
"loss_list = []\n",
"iter = 20"
],
"metadata": {
"id": "Ml6UCAqujzvm"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for i in range (iter):\n",
" # making predictions with forward pass\n",
" Y_pred = forward(X)\n",
"\n",
"\n",
" # calculating the loss between original and predicted data points\n",
" loss = criterion(Y_pred, Y)\n",
"\n",
"\n",
" # storing the calculated loss in a list\n",
" loss_list.append(loss.item())\n",
"\n",
"\n",
" # backward pass for .
Detail view on the Product planning process and its 5 steps- 1- Identi.pdf
Detail view on the Product planning process and its 5 steps.
1. Identify opportunities
2. Evaluate and prioritize projects
3. Allocate resources and plan timing
4. Complete pre-project planning
5. Reflect on the results and the process.
An detail answer with atleast 1000 words..
Determine the amount of the late filing and late payment penalties tha.pdf
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taxpayers. Required: o. Jolene filed her tax retum by its original due date but did not pay the $2 ,
300 in taxes she owed with the return until one and a half months later. b. Oscar filed his tax
return and paid his $3.300 tax llability five months late. c. Wilfred, attempting to evade his taxes,
did not file a tax return or pay his $12.800 in taxes for several years..
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9
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Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
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Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
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Chapter 5
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Chapter 6
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2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
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