HARSHMETHWANI WEEK1 210410

w1/conda.md

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+# BasisOfLearning
+Week 1
+
+# Working with conda environments
+
+Firstly install anaconda from https://www.anaconda.com/products/distribution#windows
+
+Open a terminal window and type 
+#### conda --version
+Conda displays the number of the version that you have installed.
+
+Update conda to the current version. Type the following:
+#### conda update conda
+
+# Managing Environments
+
+Open conda command prompt-
+
+Create a new environment and install a package in it.
+
+We will create a new anaconda package say 'deeplearning'.
+Using the following command in conda command promp
+#### conda create --name deeplearning
+
+To use, or "activate" the new environment, type the following:
+#### conda activate deeplearning
+
+To install package to given environment we will type:
+
+#### conda install -n deeplearning python

w1/jupyter.md

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+# Jupyter Notebook Installation
+
+This page contains information and links about installing and using tools across the Jupyter ecosystem. Generally speaking, the documentation of each tool is the place to learn about the best-practices for how to install and use the tool.
+
+Use the following installation steps:
+
+1.Download Anaconda. We recommend downloading Anaconda’s latest Python 3 version (currently Python 3.9).
+
+2.Install the version of Anaconda which you downloaded, following the instructions on the download page.
+
+3.Congratulations, you have installed Jupyter Notebook. To run the notebook type in conda command prompt:
+
+#### jupyter notebook
+
+or directly use anaconda navigator to access it.
+
+# Creating a new Notebook
+
+A new notebook may be created at any time, either from the dashboard, or using the File → New menu option from
+within an active notebook. The new notebook is created within the same directory and will open in a new browser tab.
+It will also be reflected as a new entry in the notebook list on the dashboard.
+
+When you create a new notebook document, you will be presented with the notebook name, a menu bar, a toolbar
+and an empty code cell.
+
+# Structure of Notebook
+
+The notebook consists of a sequence of cells. A cell is a multiline text input field, and its contents can be executed by
+using Shift-Enter, or by clicking either the “Play” button the toolbar, or Cell, Run in the menu bar. The execution
+behavior of a cell is determined by the cell’s type. There are three types of cells: code cells, markdown cells, and raw
+cells. Every cell starts off being a code cell, but its type can be changed by using a drop-down on the toolbar (which
+will be “Code”, initially), or via keyboard shortcuts
+
+# Code Cells
+
+A code cell allows you to edit and write new code, with full syntax highlighting and tab completion. The programming
+language you use depends on the kernel, and the default kernel (IPython) runs Python code.
+When a code cell is executed, code that it contains is sent to the kernel associated with the notebook. The results that
+are returned from this computation are then displayed in the notebook as the cell’s output. The output is not limited to
+text, with many other possible forms of output are also possible, including matplotlib figures and HTML tables (as
+used, for example, in the pandas data analysis package). This is known as IPython’s rich display capability.

w1/numpy.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6437d32a",
+   "metadata": {},
+   "source": [
+    "NumPy is a Python library used for working with arrays.\n",
+    "\n",
+    "It also has functions for working in domain of linear algebra, fourier transform, and matrices.\n",
+    "\n",
+    "NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.\n",
+    "\n",
+    "NumPy stands for Numerical Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d74f7966",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "data=np.array([1,2,3,4])\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89c51eeb",
+   "metadata": {},
+   "source": [
+    "To create an ndarray, we can pass a list,\n",
+    "tuple or any array-like object into the array() method, and it will be converted into an ndarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "566350a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "#check dimension of array\n",
+    "data=np.array([[1,2,3],[1,2,3]])\n",
+    "print(data.ndim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "81827eac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5th element on 2nd row:  10\n"
+     ]
+    }
+   ],
+   "source": [
+    "#accessing array element\n",
+    "import numpy as np\n",
+    "\n",
+    "arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\n",
+    "\n",
+    "print('5th element on 2nd row: ', arr[1, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6969ec8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Slicing Arrays\n",
+    "import numpy as np\n",
+    "\n",
+    "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
+    "\n",
+    "print(arr[1:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cd5c2253",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "#changing data types\n",
+    "import numpy as np\n",
+    "\n",
+    "arr = np.array([1.1, 2.1, 3.1])\n",
+    "\n",
+    "newarr = arr.astype('i')\n",
+    "\n",
+    "print(newarr)\n",
+    "print(newarr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f589833c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[42  2  3  4  5]\n",
+      "[1 2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#array copy \n",
+    "#Make a copy, change the original array, and display both arrays:\n",
+    "import numpy as np\n",
+    "\n",
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "x = arr.copy()\n",
+    "arr[0] = 42\n",
+    "\n",
+    "print(arr)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ef77a16f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[42  2  3  4  5]\n",
+      "[42  2  3  4  5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#array view\n",
+    "#Make a view, change the original array, and display both arrays:\n",
+    "import numpy as np\n",
+    "\n",
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "x = arr.view()\n",
+    "arr[0] = 42\n",
+    "\n",
+    "print(arr)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe48d67f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}