Diabetics are prone to diabetic nephropathy because a- diabetics are.pdf
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Diabetics are prone to diabetic nephropathy because a. diabetics are prone to kidney stones b. capillaries like the glomerulus are damaged by high blood glucose c. diabetics urinate a lot and so damage their kidneys d. diabetics eat a high fat diet and this causes kidney disease.
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"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",
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"# 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": [
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"plt.show()"
],
"metadata": {
"id": "EFDhIKURjPbB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
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],
"metadata": {
"id": "c2KRcFl5jRuy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
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"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",
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"\n",
"\n",
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Stock on 31 December 2011 was valued at $12500 ii. Accrued salaries amounted to $500 . iii.
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{
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"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 .
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Detail view on the Product planning process and its 5 steps.
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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
Determine the amount of the late filing and late payment penalties that apply for the following
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
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Use image map
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Ordered and unordered list
Header, Footer and nav layout with proper style/CSS
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Link bookmarks to jump to different paragraphs (at least two)
A link to College website
CSS: RGB and hex color, margin, padding
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