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Add table of contents
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‎codes/01-array-basics.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Dependencies\n"
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"**Table of contents**<a id='toc0_'></a> \n",
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"- [Dependencies](#toc1_) \n",
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"- [NumPy Basics](#toc2_) \n",
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" - [0D Array (Known as Scalar)](#toc2_1_) \n",
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" - [1D Array (Known as Vector)](#toc2_2_) \n",
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" - [2D Array (known as Matrix)](#toc2_3_) \n",
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" - [3D Array](#toc2_4_) \n",
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" - [4D Array](#toc2_5_) \n",
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"\n",
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"<!-- vscode-jupyter-toc-config\n",
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"\tnumbering=false\n",
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"\tanchor=true\n",
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"\tflat=false\n",
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"\tminLevel=1\n",
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"\tmaxLevel=6\n",
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"\t/vscode-jupyter-toc-config -->\n",
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"<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# <a id='toc1_'></a>[Dependencies](#toc0_)\n"
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]
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{
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"cell_type": "markdown",
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"# NumPy Basics\n",
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"# <a id='toc2_'></a>[NumPy Basics](#toc0_)\n",
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"\n",
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"- **NumPy** is a powerful library for numerical computing in Python, optimized for high-performance array operations.\n",
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"- **Arrays** in NumPy provide efficient storage and are the primary data structure, supporting fast mathematical computations over large datasets.\n",
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"cell_type": "markdown",
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"## 0D array (Known as scalar)\n"
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"## <a id='toc2_1_'></a>[0D Array (Known as Scalar)](#toc0_)\n"
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]
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{
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"cell_type": "markdown",
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"## 1D array (Known as vector)\n"
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"## <a id='toc2_2_'></a>[1D Array (Known as Vector)](#toc0_)\n"
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]
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"cell_type": "markdown",
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"## 2D array (known as matrix)\n"
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"## <a id='toc2_3_'></a>[2D Array (known as Matrix)](#toc0_)\n"
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]
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},
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"cell_type": "markdown",
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"## 3D array\n"
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"## <a id='toc2_4_'></a>[3D Array](#toc0_)\n"
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]
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},
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"## 4D array\n"
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"## <a id='toc2_5_'></a>[4D Array](#toc0_)\n"
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{

‎codes/02-arithmetic-operations.ipynb‎

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"cell_type": "markdown",
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"# Dependencies\n"
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"**Table of contents**<a id='toc0_'></a> \n",
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"- [Dependencies](#toc1_) \n",
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"- [NumPy - Arithmetic Operations](#toc2_) \n",
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" - [Array & Scalar](#toc2_1_) \n",
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" - [Array & Array](#toc2_2_) \n",
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" - [Broadcasting](#toc2_3_) \n",
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"\n",
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"<!-- vscode-jupyter-toc-config\n",
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"\tnumbering=false\n",
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"\tanchor=true\n",
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"\tflat=false\n",
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"\tminLevel=1\n",
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"\tmaxLevel=6\n",
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"\t/vscode-jupyter-toc-config -->\n",
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"<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# <a id='toc1_'></a>[Dependencies](#toc0_)\n"
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"# NumPy - Arithmetic Operations\n",
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"# <a id='toc2_'></a>[NumPy - Arithmetic Operations](#toc0_)\n",
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"\n",
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"- **Scalar and Array Operations**:\n",
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" - Demonstrates adding, subtracting, multiplying, dividing, and performing other arithmetic operations between a NumPy array and a scalar.\n",
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"## Array & Scalar\n"
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"## <a id='toc2_1_'></a>[Array & Scalar](#toc0_)\n"
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"## Array & Array\n"
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"## <a id='toc2_2_'></a>[Array & Array](#toc0_)\n"
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"## Broadcasting\n",
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"## <a id='toc2_3_'></a>[Broadcasting](#toc0_)\n",
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"\n",
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"- It allows arrays of different shapes to be used together in different operations.\n",
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"- The **smaller** array is “broadcasted” across the **larger** array so that they have compatible shapes.\n"
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‎codes/03-comparative-operations.ipynb‎

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"cell_type": "markdown",
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"# Dependencies\n"
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"**Table of contents**<a id='toc0_'></a> \n",
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"- [Dependencies](#toc1_) \n",
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"- [NumPy - Comparative Operations](#toc2_) \n",
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" - [Array & Scalar](#toc2_1_) \n",
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" - [Array & Array](#toc2_2_) \n",
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" - [Broadcasting](#toc2_3_) \n",
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"\n",
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"<!-- vscode-jupyter-toc-config\n",
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"\tnumbering=false\n",
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"\tanchor=true\n",
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"\tflat=false\n",
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"\tminLevel=1\n",
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"\tmaxLevel=6\n",
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"\t/vscode-jupyter-toc-config -->\n",
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"<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# <a id='toc1_'></a>[Dependencies](#toc0_)\n"
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"# NumPy - Comparative Operations\n",
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"# <a id='toc2_'></a>[NumPy - Comparative Operations](#toc0_)\n",
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"\n",
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"- **Scalar and Array Operations**:\n",
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" - Compares arrays with scalar values using operators like `==`, `!=`, `<`, `>`, `<=`, and `>=`..\n",
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"## Array & Scalar\n"
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"## <a id='toc2_1_'></a>[Array & Scalar](#toc0_)\n"
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"## <a id='toc2_2_'></a>[Array & Array](#toc0_)\n"
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"## Broadcasting\n",
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"## <a id='toc2_3_'></a>[Broadcasting](#toc0_)\n",
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"\n",
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"- It allows arrays of different shapes to be used together in different operations.\n",
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"- The **smaller** array is “broadcasted” across the **larger** array so that they have compatible shapes.\n"

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