Describe environmental problem in your community that you can address (1).pdf
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Describe environmental problem in your community that you can address as an indivudual .please focus on this 5 aspects: 1)Describe and context of the environmental problem 2)The location and extent of the environmental problem 3)Who/what is causing the environmental problem 4)how you are associated or connected to the environmental problem 5)Who/what is being affected by this environmental problem.
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{
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"colab": {
"provenance": []
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"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",
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"func = -5 * X"
],
"metadata": {
"id": "Vq4OWcCNjJFj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
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"plt.legend()\n",
"plt.grid('True', color='y')\n",
"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": [
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" return w * x"
],
"metadata": {
"id": "T-vtFS9Gjig2"
},
"execution_count": null,
"outputs": []
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{
"cell_type": "code",
"source": [
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"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",
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vote Data e. a group of voters in the community who voted, randomly selected Variable f. the
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{
"nbformat": 4,
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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 .
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.
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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 ,
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did not file a tax return or pay his $12.800 in taxes for several years..
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