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网络:Machine Learning with Python 俄罗斯22 957 技术与应用4 788

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日期	订阅者增长	提及	频道
03 六月	+21
02 六月	+39
01 六月	+40

频道帖子

Repost from Machine Learning with Python

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12	Learning AI doesn’t need another random tutorial rabbit hole. 🚫🐇 AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. 📚🤖 It helps you make your own learning path by collecting the materials the author used while learning AI, with quick-start recommendations up front and sections you can scan by resource type. 🗺️✨ Key features: 🌟 • TL;DR starting path – points to one book, one LLM video, and the Hugging Face Agents Course 📖🎥 • Books section – lists AI/ML/DL books with short notes on where each one helps 📚 • Courses and videos – collects practical lectures, tutorials, and talks from sources like MIT, NVIDIA, Hugging Face, Karpathy, and 3Blue1Brown 🎓 • Tools and libraries map – groups frameworks, platforms, visualization tools, and Python libraries for builders 🛠️ • Broader study material – includes models, model hubs, articles, papers, datasets, and AI notes 📄 Free public GitHub repo. 🆓 https://github.com/ArturoNereu/AI-Study-Group #AI #MachineLearning #DeepLearning #GitHub #StudyGroup #TechLearning ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A	849
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17	Data validation with Pydantic! 🐍✨ In the early stages of development, data validation usually doesn't cause problems. In many Python projects, validation initially looks simple: if not isinstance(age, int): raise ValueError("age must be an int") But then come email, JSON from APIs, query parameters, nested objects, configs, nullable fields, and type conversion. At some point, the code turns into a set of if/else and manual checks. For such tasks, Pydantic is often used. Installation: pip install pydantic pip install "pydantic[email]" Create a model: from pydantic import BaseModel class User(BaseModel): name: str age: int Now the data is validated automatically: user = User( name="Alex", age="30" ) print(user.age) print(type(user.age)) The result: 30 <class 'int'> Pydantic will automatically convert the string "30" to an int. If you pass an incorrect value, you'll get a ValidationError: User( name="Alex", age="test" ) This is especially convenient when working with APIs, JSON, query parameters, and incoming data from outside. A common production case is checking email: from pydantic import BaseModel, EmailStr class User(BaseModel): email: EmailStr User(email="alex@test.com") If the email is invalid, Pydantic will throw a ValidationError. You can set default values: from pydantic import BaseModel class Config(BaseModel): host: str = "localhost" port: int = 5432 And allow None: from pydantic import BaseModel class User(BaseModel): nickname: str \| None = None This field becomes optional. A practical example is processing an API response: from pydantic import BaseModel class Product(BaseModel): id: int title: str price: float data = { "id": "1", "title": "Keyboard", "price": "99.5" } product = Product(**data) print(product) The types will be automatically converted. For nested model structures, you can combine: from pydantic import BaseModel class Address(BaseModel): city: str zip_code: str class User(BaseModel): name: str address: Address user = User( name="Alex", address={ "city": "Berlin", "zip_code": "10115" } ) print(user) The nested object will also be validated. Serialization in Pydantic v2: print(user.model_dump()) print(user.model_dump_json()) Pydantic is actively used in FastAPI, ETL, microservices, data pipelines, and API clients. For working with environment variables in Pydantic v2, a separate package is usually used: pip install pydantic-settings It's important to understand: Pydantic is not an ORM and does not replace business logic. Its task is to validate data, convert types, and describe schemas. 🔥 Pydantic significantly reduces the amount of manual data validation and makes processing incoming structures more predictable. #Python #Pydantic #DataValidation #FastAPI #Coding #DevOps ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A	790
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19	Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚 This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊 It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖 Free public repository on GitHub. 💻✨ https://github.com/dair-ai/Mathematics-for-ML #MachineLearning #Mathematics #DataScience #Learning #GitHub #AI	758
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