Notes Python

This project is a collection of educational notes and tutorials focused on Python programming, scientific computing, and data analysis. It serves as a reference for learning language basics, advanced techniques, and object-oriented design.

The materials include implementation guides for building linear, logistic, and convolutional neural networks using symbolic graph frameworks. It also provides instruction on manipulating and visualizing structured data frames and performing complex mathematical operations through numerical libraries.

The repository includes a system for converting interactive notebook files into static HTML pages to facilitate the distribution and reading of the technical content.

Features

Python Programming Tutorials - A comprehensive collection of tutorials and notes covering Python language basics and advanced techniques.

Neural Network Implementation Guides - Provides practical guides for translating mathematical concepts into neural network code using symbolic graph frameworks.

Data Analysis and Visualization - Guides the use of structured data frames and plotting libraries to analyze and visualize complex information.

Data Analysis Workflows - Provides a workflow for manipulating and visualizing structured data frames to uncover insights.

Tabular Data Frames - Instructional guides on using tabular data frames for efficient dataset slicing and statistical analysis.

Tabular Data Structures - Covers the organization of complex datasets into row-and-column formats for efficient analysis.

Python Data Analysis Tutorials - Offers instructional materials for manipulating and analyzing structured data using scientific Python libraries.

Scientific Computing - Teaches the use of numerical libraries to perform complex mathematical operations and handle large datasets.

Vectorized Array Operations - Demonstrates how to perform vectorized array operations to bypass slow high-level language loops.

Numerical Computing References - Provides an instructional guide for performing high-performance mathematical operations on multi-dimensional arrays.

Class-Based Logic Organization - Teaches how to organize software state and logic using class-based modeling and inheritance.

Object Oriented Design - Provides comprehensive tutorials on object-oriented design principles and patterns for structuring software systems.

Neural Network Implementations - Instructional material on implementing neural network architectures using symbolic graph frameworks.

Static Site Generation - Ships a script to generate static documentation pages from interactive notebook source files.

Notebook to HTML Converters - Includes a system for converting interactive notebook files into static HTML pages for distribution.

Neural Network Implementations - Provides implementation guides for building linear, logistic, and convolutional neural networks.

Graph-Based Computational Execution - Teaches the construction of mathematical operations as directed graphs to enable automatic differentiation.

lijin-THUnotes-python

Features

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