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dhavalsaysdhavalsays
committedAug 3, 2020
matrix math
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‎DeepLearningML/4_matrix_math/4_matrix_math.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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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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"<h3 style='color:blue'>Calculate profit/loss from revenue and expenses</h3>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"revenue = np.array([[180,200,220],[24,36,40],[12,18,20]])\n",
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"expenses = np.array([[80,90,100],[10,16,20],[8,10,10]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[100, 110, 120],\n",
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" [ 14, 20, 20],\n",
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" [ 4, 8, 10]])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"profit = revenue - expenses\n",
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"profit"
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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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"<h3 style='color:blue'>Calculate total sales from units and price per unit using matrix multiplication</h3>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"price_per_unit = np.array([1000,400,1200])\n",
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"units = np.array([[30,40,50],[5,10,15],[2,5,7]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[30000, 16000, 60000],\n",
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" [ 5000, 4000, 18000],\n",
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" [ 2000, 2000, 8400]])"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"price_per_unit*units"
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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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"In above case numpy is using broadcasting so it expands price_per_unit array from 1 row, 3 columns to 3 row and 3 columns. Correct way to do matrix multiplication is to use dot product as shown below"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([34400, 50000, 64400])"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.dot(price_per_unit,units)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎DeepLearningML/4_matrix_math/4_matrix_math.md

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#### Exercise: Matrix Math
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1. Below is some indian companies revenues in US dollars. Using numpy can you convert this into Indian rupees? 1 USD = 75 INR
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![](revenue_usd.jpg)
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2. Divine flowers is a flower shop that sells different type of flowers. Below is the table showing how many flowers of each type they sold in different months. Also given are the prices of one flower each. Using this find out their total sales in every month.
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![](flowers.jpg)
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[Click here for solution of 1 and 2](https://github.com/codebasics/py/tree/master/DeepLearningML/4_matrix_math/4_matrix_math_exercise_solution.ipynb)
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3. Here is some matrix exercise from mathisfun.com. Please click on a link below and do the exercise.
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[Click me for matrix exercise](https://www.mathopolis.com/questions/q.html?id=714&t=mif&qs=714_715_716_717_2394_2395_2397_2396_8473_8474_8475_8476&site=1&ref=2f616c67656272612f6d61747269782d6d756c7469706c79696e672e68746d6c&title=486f7720746f204d756c7469706c79204d61747269636573)
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‎DeepLearningML/4_matrix_math/4_matrix_math_exercise_solution.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import numpy as np"
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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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"<h3 style='color:blue'>Solution 1: Convert USD revenues to INR</h3>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[15000, 16500, 18750],\n",
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" [ 5100, 5925, 7875],\n",
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" [ 8250, 10500, 13500],\n",
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" [ 6000, 6375, 6750]])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"revenues_usd = np.array([[200,220,250],[68,79,105],[110,140,180],[80,85,90]])\n",
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"revenues_inr = 75*revenues_usd\n",
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"revenues_inr"
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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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"<h3 style='color:blue'>Solution 2: Calculate total flowers sale every month for divine flowers shop</h3>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"units_sold = np.array([[50,60,25],[10,13,5],[40,70,52]])\n",
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"price_per_unit = np.array([20,30,15])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1900, 2640, 1430])"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"total_sales_amount = np.dot(price_per_unit,units_sold)\n",
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"total_sales_amount"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎DeepLearningML/4_matrix_math/flowers.jpg

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