Python from Beginner to Mastery — Chapter 13 Web Development
This chapter focuses on Python Web development, covering basic concepts such as web architecture, HTTP protocol, and the ecosystem. It compares frameworks like Flask (lightweight), Django (full-stack), and FastAPI (modern). The content includes Flask installation, basic application development, and deployment, as well as database operations (SQLite, SQLAlchemy), integration of frontend AJAX, design of RESTful APIs. Additionally, it addresses production deployment (Gunicorn, Nginx, Docker) and performance optimization (query optimization, caching, asynchronous processing, monitoring), helping readers master the entire web development process.
Read MorePython from Beginner to Mastery — Chapter 12: Data Science and Analysis
This chapter focuses on Python data science, covering the basics of data science, NumPy numerical computation, Pandas data processing, data visualization, statistical analysis, machine learning introduction, and project practice. It systematically learns core data science technologies, including data processing, visualization, statistical analysis, machine learning model construction and evaluation, to help master the full - process skills of data science.
Read MorePython from Beginner to Pro - Chapter 11 Network Programming
This chapter introduces Python network programming, covering the basics of network protocols (TCP/IP four - layer model, common protocols), Socket programming (implementation of TCP/UDP servers and clients), HTTP clients (usage of urllib/requests libraries), Web server development (http.server module), network data processing (serialization, compression, encryption), asynchronous network programming (asyncio), and network security (common threats and protections). It helps in building efficient and secure network applications.
Read MorePython from Beginner to Pro - Chapter 10: Advanced Python Features
This chapter delves into advanced Python features, including: list, dictionary, and set comprehensions, which are concise and efficient for quickly creating data structures; generators with lazy evaluation that save memory and support infinite sequences and pipeline processing; iterators providing a unified traversal interface; decorators for extending function functionality and achieving cross-cutting concern separation; closures enabling functional programming and state retention; and context managers ensuring proper resource management. These features embody Python's "Readability counts" philosophy and help in writing concise and efficient code.
Read MorePython from Beginner to Pro - Chapter 9: Modules and Packages
This chapter introduces Python's module and package system. A module is a file containing code, which can be categorized into types such as built-in and standard. It can be imported through various methods, with search paths and caching mechanisms in place. A package is a container for modules, including an __init__.py file, and can be structured hierarchically. There are both relative and absolute import methods. Third-party modules are managed using pip, and dependencies can be isolated through virtual environments. Commonly used third-party libraries like requests and numpy are also introduced, forming the foundation for building Python projects.
Read MorePython from Beginner to Pro - Chapter 8 Exception Handling
This chapter introduces Python exception handling, covering concepts such as exceptions (runtime errors in programs, divided into syntax errors and exceptions), hierarchy (based on BaseException), and common built-in exceptions. It focuses on explaining the try-except statement (basic syntax, catching specific/multiple/all exceptions), the finally clause (executed regardless of exceptions), and the else clause (executed when there are no exceptions). raise can be used to actively throw exceptions and create exception chains, and custom exception classes can be defined. It also adheres to best practices like EAFP, such as prioritizing specific exceptions, logging, and avoiding anti-patterns, to help write robust programs.
Read MorePython from Beginner to Mastery — Chapter 7: Object-Oriented Programming
This chapter introduces Python object-oriented programming, covering concepts such as classes and objects. A class is defined using the `class` keyword and can be instantiated. Attributes are divided into instance attributes and class attributes, while methods include instance methods, class methods, and static methods. The constructor `__init__` initializes objects, and the destructor `__del__` cleans up resources. Inheritance enables code reuse, and polymorphism reflects different implementations of the same interface. Encapsulation is achieved through access control and the `@property` decorator. Special methods customize object behavior, along with advanced features like class decorators and descriptors, which help in building efficient code.
Read MorePython from Beginner to Master — Chapter 6 File Operations
This chapter introduces Python file operations, including: file opening and closing using the `open()` function with different modes, emphasizing the automatic resource management via the `with` statement; file reading methods such as `read`, with chunked reading for large files; file writing methods such as `write`, with attention to the differences between modes; file pointer positioning using `tell` and `seek`; directory operations implemented through the `os` module and `pathlib`, involving path manipulation and traversal. Best practices such as following specified encodings and using the `with` statement are also covered.
Read MorePython from Beginner to Master — Chapter 5 Functions
This chapter provides an in - depth explanation of Python functions, covering function definition and invocation, such as defining functions with `def` and using docstrings for description. For parameter passing, it includes positional, keyword, and default parameters, as well as variable parameters `*args` and `**kwargs`. Regarding return values and scopes, it discusses single/multiple return values, local and global scopes, and the use of the `global` and `nonlocal` keywords. Recursive functions require understanding both the base and recursive cases. Lambda expressions are introduced for concisely defining anonymous functions. Additionally, built - in functions such as mathematical operations and type conversion are covered. Functions are a core part of programming, and mastering their usage is conducive to writing high - quality code.
Read MorePython from Beginner to Mastery — Chapter 4: Data Structures
This chapter introduces 5 built-in data structures in Python. Strings can be defined using multi - quotes and have a rich set of operation methods. Lists are ordered and mutable, supporting operations such as adding, deleting, modifying, querying, sorting, and reversing. Tuples are immutable and ordered, and can be used as keys in dictionaries. Dictionaries are key - value pair mappings, unordered and mutable. Sets store unique elements and support mathematical operations. When choosing a data structure, factors such as data properties, operations, and performance should be considered to write efficient and elegant code.
Read MorePython from Beginner to Mastery — Chapter 3 Control Flow
This chapter mainly introduces Python control flow, including conditional statements and loop statements. Conditional statements include single, double, and multi-branch structures, which can be combined with boolean, comparison, and logical operations, and have usage scenarios such as nesting and the ternary operator (e.g., leap year judgment). For loop statements, there are while and for loops: while loops have an else clause, and for loops can iterate over sequences (often combined with range). break is used to exit a loop, continue skips the current iteration, and pass is used for placeholder purposes. Additionally, the chapter covers applications such as nested loop pattern printing and data processing, while emphasizing performance optimization and the effective use of built-in functions to enhance efficiency.
Read MorePython from Beginner to Mastery - Chapter 2: Python Basic Syntax
This chapter introduces Python basic syntax, including: variable-related aspects such as naming rules (identifier composition, PEP 8 specifications, etc.), assignment references, and scopes; data types are divided into basic types (numbers, strings, etc.) and composite types (lists, tuples, etc.), supporting dynamic typing and type conversion; operators include arithmetic, comparison, logical, etc.; input/output functions include the usage and formatting of print and input; comments include single-line, multi-line, and docstrings, following PEP 8 code specifications, which form the foundation for subsequent learning.
Read MorePython from Beginner to Pro - Chapter 1: Python Environment Setup
This chapter mainly introduces Python environment setup and basic usage. First, it explains the Python installation and configuration methods for different operating systems (Windows, macOS, Linux); then it covers the writing of the first Python program (e.g., "Hello World"), running methods, and structure analysis; next, it introduces the Python interactive environment (including IPython and Jupyter Notebook); finally, it elaborates on the use of the pip package management tool, including installing, uninstalling, and upgrading packages, using requirements.txt files, configuring domestic mirror sources, and virtual environments.
Read MoreDeploying Baidu Wenxin 4.5 Open-Source Large Model on AiStudio for Android Call
In the previous article "Deploying theERNIE 4.5 Open-Source Model for Android Device Calls", the blogger introduced how to deploy the ERNIE 4.5 open-source large language model on one's own server. However, for students without GPU servers, this approach is out of reach. Therefore, this article will introduce how to leverage the computing power on AiStudio for free to deploy the ERNIE 4.5 open-source large model for personal use.
Read MoreDeploying Baidu Wenxin 4.5 Open-Source Model for Android Device Calls
In the previous article "Usage and Deployment of the ERNIE 4.5 Open-Source Large Model", we introduced how to use FastDeploy to deploy the ERNIE 4.5 open-source large model and briefly called its interface. This article will describe how Android can call this deployed interface and implement conversations.
Read MoreUsage and Deployment of ERNIE 4.5 Open-Source Large Model
The ERNIE 4.5 series open-source models consist of a total of 10 models, covering Mixture-of-Experts (MoE) models with activation parameter scales of 47B and 3B (with the largest model having a total parameter count of 424B), as well as dense parameter models with 0.3B parameters. Below, we will introduce how to quickly use ERNIE 4.5 models for inference and deploy the interface for client-side calls on platforms such as Android and WeChat Mini Programs. Note that only text-type models are accepted here; in reality, ERNIE 4.5 also has multimodal models.
Read MoreDeploying Custom Gesture Recognition Models with MediaPipe on Android
This project implements a high-performance real-time gesture recognition Android application based on the Google MediaPipe and Android CameraX technology stacks. It adopts MediaPipe's latest Gesture Recognition API, supporting the recognition of various gesture types, including common gestures such as thumb-up, victory sign, and open palm. Additionally, it features real-time hand key point detection and drawing functionality.
Read MoreCustom Gesture Recognition Training Model with MediaPipe
MediaPipe is an open-source framework developed by Google for building perception pipelines to process time-series data such as video and audio. Among its components, MediaPipe Hands is a high-performance hand key-point detection solution capable of real-time hand key-point detection on mobile devices.
Read MoreA Tool Website Developed with Python
This article introduces a feature-rich tool website developed using Python. It includes various tools such as document tools, PDF tools, image tools, audio tools, video tools, voice tools, and programming tools, which are commonly used in work or study.
Read MoreQuickly Deploy a DeepSeek-R1 Service from Scratch
Here are the simplest commands to introduce how to deploy the DeepSeek-R1 service. Anaconda is assumed to be already installed, and the vllm framework is used, making it easy to deploy even in China.
Read MoreRapid Training of Cat and Dog Sound Classification Model
This paper introduces how to quickly perform sound classification training and inference using PyTorch and the macls library. First, create a Python 3.11 virtual environment via Anaconda and install the PyTorch 2.5.1 GPU version along with the macls library. Next, prepare the dataset, with provided download links or support for custom formats. The training part can be completed with just three lines of code for model training, optimization, and saving. The inference phase loads the pre-trained model for prediction. The framework supports multiple sound classification models, facilitating different scenario requirements.
Read MoreQuick Deployment of Speech Recognition Framework Using MASR V3
This framework appears to be very comprehensive and user-friendly, covering multiple stages from data preparation to model training and inference. To help readers better understand and utilize this framework, I will provide detailed explanations for each part along with some sample code. ### 1. Environment Setup First, you need to install the necessary dependency packages. Assuming you have already created and activated a virtual environment: ```sh pip install paddlepaddle==2.4.0 -i https://mirror.baidu.com/pypi/ ```
Read MoreQuick Deployment of Speech Recognition Framework Using PPASR V3
This detailed introduction demonstrates the process of developing and deploying speech recognition tasks using the PaddleSpeech framework. Below are some supplements and suggestions to the information you provided: 1. **Installation Environment**: Ensure your environment has installed the necessary dependencies, including libraries such as PaddlePaddle and PaddleSpeech. These libraries can be installed via the pip command. 2. **Data Preprocessing**: - You may need to perform preprocessing steps on the raw audio, such as sample rate adjustment and noise removal.
Read MoreText Endpoint Detection Based on Large Language Models
This paper introduces a method to detect text endpoints using large language models (LLMs) to improve Voice Activity Detection (VAD) in voice conversations. By training a fine-tuned model to predict whether a sentence is complete, the user's intent can be more accurately judged. The specific steps include: 1. **Principle and Data Preparation**: Leverage the text generation capabilities of large language models to fine-tune based on predefined datasets and specific formats. 2. **Fine-tuning the Model**: Use the LLaMA-Factory tool for training, selecting appropriate prompt templates and optimized data formats. 3.
Read MoreSpeaker Log Implementation Based on PyTorch (Speaker Separation)
This article introduces the speaker diarization feature of the VoiceprintRecognition_Pytorch framework implemented based on PyTorch, which supports various advanced models and data preprocessing methods. By executing the `infer_speaker_diarization.py` script or using the GUI interface program, audio can be speaker-separated and results displayed. The output includes the start and end times of each speaker and their identity information (registration is required first). Additionally, the article provides solutions for Chinese names in the Ubuntu system... （注：原文末尾“解决中文名”表述不完整，已保留原文未尽部分的省略格式，完整内容需参考原文后续章节）
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