Категории

Dot product in NumPy - 16

Опубликовано: 31 Май 2026
на канале: IT Guys
5,955
97

Hello everyone! Today we will be learning about the dot product in NumPy. This topic will also cover when can we use NumPy's transposing method, here is the link for the video if you would like to watch it:
   • Manipulating and comparing arrays with Num...  


Also, if you are stuck on this topic(believe me, it is a hard topic to understand), check out these resources at https://github.com/sush3011/MachineLe... or you can also check out these two websites:
http://matrixmultiplication.xyz/ - this will represent the waterfall technique that you can use whilst visualizing the process of the dot product
https://numpy.org/doc/stable/referenc... -The official documentation!

play_arrow
6,992
216

6П45С переделка

6П45С переделка
play_arrow
98
2

Xiaomi Redmi 9C FRP bypass

Xiaomi Redmi 9C FRP bypass
play_arrow
12,940
225

курил только легалку

курил только легалку
play_arrow
19,271
108

Caligula (1979) - The Death Of Nerva (Best scene, goes hard AF)

Caligula (1979) - The Death Of Nerva (Best scene, goes hard AF)
play_arrow
191
24

Dracula giapponesi e vecchi troppo giovani - Il Buono, il Brutto e il Boh... #1

Dracula giapponesi e vecchi troppo giovani - Il Buono, il Brutto e il Boh... #1
play_arrow
236
13

Morbitus - Blood Warp

Morbitus - Blood Warp
play_arrow
3
0

Пташков Дмитрий И Иванов Вальс

Пташков Дмитрий И Иванов Вальс
play_arrow
18
0

What is Music Licensing?

What is Music Licensing?
Похожие видео
play_arrow
Evaluating a machine learning model - Scikit-Learn evaluation functions - 51

Evaluating a machine learning model - Scikit-Learn evaluation functions - 51
play_arrow
Evaluiating a machine leaning model - FINALLY using the scoring Paramter - 50

Evaluiating a machine leaning model - FINALLY using the scoring Paramter - 50
play_arrow
Regression model evaluation metrics - Part 2(Mean squared error) - 49

Regression model evaluation metrics - Part 2(Mean squared error) - 49
play_arrow
Regression model evaluation metrics - Part 2(Mean absolute error) - 48

Regression model evaluation metrics - Part 2(Mean absolute error) - 48
play_arrow
Regression model evaluation metrics - Part 1(R^2) - 47

Regression model evaluation metrics - Part 1(R^2) - 47
play_arrow
Evaluating a classification model with evaluation metrics - Part 4(Classsification Report) - 46

Evaluating a classification model with evaluation metrics - Part 4(Classsification Report) - 46
play_arrow
Evaluating a classification model with evaluation metrics - Part 3(Confusion Matrix) - 45

Evaluating a classification model with evaluation metrics - Part 3(Confusion Matrix) - 45
play_arrow
Evaluating a Classification model with evaluation metrics - Part 2(Area under ROC curve) - 44

Evaluating a Classification model with evaluation metrics - Part 2(Area under ROC curve) - 44
play_arrow
Evaluating a Classification model with evaluating metrics - Part 1(Accuracy) - 43

Evaluating a Classification model with evaluating metrics - Part 1(Accuracy) - 43
play_arrow
Evaluating a machine learning model - Part 2 - 42

Evaluating a machine learning model - Part 2 - 42
play_arrow
Evaluating a machine learning model - Part 1 - 41

Evaluating a machine learning model - Part 1 - 41
play_arrow
Making predictions with a machine learning model - Part 2 - 40

Making predictions with a machine learning model - Part 2 - 40
play_arrow
Making predictions with machine learning models - Part 1 - 39

Making predictions with machine learning models - Part 1 - 39
play_arrow
Fitting a machine learning model - 38

Fitting a machine learning model - 38
play_arrow
Choosing the right machine-learning model with scikit-learn - Part 2 - 37

Choosing the right machine-learning model with scikit-learn - Part 2 - 37
play_arrow
Choosing the right Machine Learning Model with Scikit-Learn - Part 1 - 36

Choosing the right Machine Learning Model with Scikit-Learn - Part 1 - 36
play_arrow
Getting our data ready - Filling in missing values - Part 2 - 35

Getting our data ready - Filling in missing values - Part 2 - 35
play_arrow
Getting our data ready - Filling in missing values - Part 1 - 34

Getting our data ready - Filling in missing values - Part 1 - 34
play_arrow
Getting our data ready - Converting non-numerical values into numerical values - 33

Getting our data ready - Converting non-numerical values into numerical values - 33
play_arrow
Getting our data ready - Splitting our data - 32

Getting our data ready - Splitting our data - 32
play_arrow
A typical Scikit-Learn workflow - 31

A typical Scikit-Learn workflow - 31
play_arrow
Introduction to Scikit-Learn - 30

Introduction to Scikit-Learn - 30
play_arrow
Matplotlib project - 29

Matplotlib project - 29
play_arrow
Customising and styling Matplotlib plots - 28

Customising and styling Matplotlib plots - 28