Flashcards in Matrix Math Deck (41):

1

## A single value is known as

### scalar

2

## scalar

### a value with 0 dimensions

3

## Lists of values are known as

### vectors

4

## Types of vectors

### row and column

5

## Dimensions of vectors

### just one: length

6

## What is a matrix

### a 2 dimensional grid of values

7

## What is a tensor?

### Any n-dimensional collection of values

8

## Locations in matrices are known as

### indices

9

## Indices nomenclature

### Like a 1 layer nested array

10

## Numpy is

###
a C library in python.

Does lots of math operations in Python and is designed to work with matrices.

11

## Normal convention for naming numpy

###
import numpy as np

12

## Most common way to with number in NumPy is through

### ndarray objects

13

## ndarray objects are

###
similar to Python lists, but can have any number of dimensions

Does fast math operations

14

## To declare an ndarray

### x = nd.array(5)

15

## To get shape of ndarray

### nd.shape

16

## To reshape an nd array like one that is (4,)

### (4,).reshape(1,4)

17

##
Why do some people use

x = v[:, None]

### Adds extra dimension

18

## Elementwise operations

### Like iterating through and running an operation

19

## Requirements for adding two matrices

###
Have to be the same shape

20

## When describing the shape of a matrix how does one describe it?

### rows x columns

21

## You can only safely run a transpose to multiply if

### The data is arranged as rows

22

## To get the min, max, mean of a matrix

###
np.min(array)

np.max(array)

mp.mean(array)

23

## How to calculate error in a logistic regression?

### It the number of errors

24

## What method does one use to minimize the error?

### Gradient descent

25

## Basic parts of a neural network

### Input data, processing, output

26

## Individual nodes are called

### perceptrons

27

## What are weights?

### A higher weight means the neural network considers that input more important than other inputs, and lower weight means that the data is considered less important.

28

## W vs w

### W when it represents a matrix of weights or a w when it represents an individual weight

29

## How is an output signal determined?

### feeding the linear combination into an activation function

30

## What are two ways to go from an AND perceptron to an OR perceptron?

###
Increase the weights

Decrease the magnitude of the bias

31

## AND perceptron

### Both must be true to accept

32

## OR perceptron

### One must be true

33

## NOT perceptron

### A specific one must be true

34

## XOR perceptron

### outputs 0 if the inputs are the same and 1 if the inputs are different

35

## Gradient is

### term for rate of change or slope

36

## To calculate rate of change

### derivative of a function f(x) gives you another function fâ'(x) that returns the slope of f(x) at point x

37

## Local minima

### where the error is low, but not the lowest

38

## SSE is

### measure of networks performance. Low means good predictions.

39

## np.dot is the same as

### Multiplying two matrices and then getting the sum

40

## sigmoid(x)

### 1/(1+np.exp(-x))

41