Python Programming Cheat Sheet: Essential Syntax and Libraries
Numbers and Math
- Arithmetic:
+,-,/,//(floor),%(remainder),**(exponent). - Shorthand:
a += 2(also-=,*=,//=). - Functions:
abs(-3.5),round(3.56, 1),max(3.56, 4.57).
Strings and Formatting
- Quotes:
'spam eggs',"doesn't". - Newline:
print('\n'). - F-Strings:
f'Price {cost:.2f}',f'{ratio:.2%}'.
Data Types
int(whole),float(decimal),str(text),bool(True/False).- Input:
input()(returns string),int(input())(converts to integer).
Decision Making
age = int(input('Enter your age: ')) if age >= 18: print(Computer Graphics Systems: Raster vs. Random Scan
Display Architectures in Computer Graphics
In computer graphics, architecture displays are fundamentally divided into two categories based on how the electron beam (or display processor) constructs and refreshes the image on the screen: Raster-Scan Systems and Random-Scan (Vector/Stroke/Calligraphic) Systems.
1. Raster-Scan System
A Raster-Scan System is the most common type of display graphics architecture, used in modern TVs, computer monitors, and smartphones.
How it Works
The electron beam sweeps
Read MoreБазовый шаблон интернет-магазина на Django
Главная Каталог {% if user.is_authenticated %} Кабинет Выйти {% else %} Вход Регистрация {% endif %} Админ
{% block content %}{% endblock %}
© 2026 Магазин | shop@mail.com{% if not user.is_authenticated %}
Вход
{% csrf_token %} ВойтиНет аккаунта? Зарегистрируйтесь
Регистрация
{% csrf_token %} Зарегистрироваться{% endif %}
Read MoreDeep Learning Architectures: CNNs, RNNs, and GANs
1. Pooling Layers in CNNs
A pooling layer is a down-sampling layer in a Convolutional Neural Network (CNN) usually placed after a convolutional layer. It reduces the spatial dimensions (width × height) of the input feature maps while retaining the most critical structural information.
Types of Pooling Layers
- Max Pooling: Extracts the maximum value from the region covered by the sliding filter. Purpose: Captures dominant features like sharp edges and bright pixels.
- Average Pooling: Computes the average
Fundamentals of AI: From Search to Expert Systems
UNIT I: Foundations of Artificial Intelligence
1. Introduction to Artificial Intelligence
Artificial Intelligence (AI) is a branch of computer science that focuses on creating machines capable of performing tasks that normally require human intelligence. These include learning, reasoning, problem-solving, decision-making, language understanding, and pattern recognition. AI simulates human thinking through algorithms and data processing, finding applications in robotics, virtual assistants, medical
Read MoreSupervised and Unsupervised Learning Model Reference
Supervised Classification
Logistic Regression (LR)
- Type: Binary Classification
- Scaling: Yes (StandardScaler)
- Outliers: Not robust
- Categorical Variables: No (encode first)
- Core Idea: Sigmoid function maps output to 0–1 probability; threshold ≥ 0.5 predicts class 1.
- Advantages: Fast, simple, interpretable, outputs probabilities.
- Disadvantages: Binary only, requires linear boundary, fails on non-linear data.
- Metrics: Accuracy, Precision, Recall, F1-Score, Confusion Matrix.
Decision Trees (DT)
- Type: Classification