New LLM Toolkit:For Developers Building LLM Applications
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for AI Students, Developers, and Researchers
Tools and Learning Resources to Pioneer the Future of AI.
Popular Guides
How To Build A Large Language Model
Acquire the engineering skills to construct, train, and optimize sophisticated large language models.
Approx. 80 hours
Programming and Deep Learning
Advanced Transformer Architecture
Master the theory, mathematics, and implementation of advanced Transformer architectures for modern LLMs.
Approx. 30 hours
Deep Learning & Python Proficiency
LangChain for Production-Ready LLM Applications
Develop and operationalize complex, scalable LLM applications using advanced LangChain features and best practices.
Approx. 32 hours
Python & Basic LangChain
Python Programming Fundamentals
Acquire the core Python skills needed to write clear, functional code and begin your programming path.
Approx. 20 hours
No prior programming experience.
Practical Quantization for Large Language Models
Implement LLM quantization techniques (PTQ, QAT, GPTQ, GGUF) to reduce model size and improve inference speed.
Approx. 15 hours
LLM Fundamentals & Python
Getting Started with PyTorch
Build and train fundamental deep learning models using PyTorch's core features like tensors, autograd, and neural network modules.
Approx. 18 hours
Basic Python & ML knowledge
Advanced PyTorch
Build, optimize, and deploy complex deep learning models using PyTorch's advanced capabilities.
Approx. 36 hours
Intermediate PyTorch & DL concepts
Python for LLM Workflows: Tooling and Best Practices
Build and manage LLM applications using Python, LangChain, LlamaIndex, and essential development practices.
Approx. 18 hours
Intermediate Python skills
Mastering Gradient Boosting Algorithms
Effectively implement, tune, and interpret advanced gradient boosting models for sophisticated machine learning applications.
Approx. 28 hours
Python & ML Fundamentals
Time Series Analysis and Forecasting
Analyze time-dependent data and build statistical forecasting models like ARIMA and SARIMA.
Approx. 15 hours
Basic Python and Pandas
RLHF: Reinforcement Learning from Human Feedback
Apply Reinforcement Learning from Human Feedback (RLHF) principles and techniques to align large language models.
Approx. 24 hours
Advanced ML & DL knowledge
Advanced Reinforcement Learning Techniques
Implement and apply advanced reinforcement learning algorithms to solve complex sequential decision-making challenges.
Approx. 70 hours
Python, ML & RL Fundamentals
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MASTERCLASS
HOW TO BUILD A
LARGE LANGUAGE MODEL
30 Chapters, 700+ Pages of In-Depth Content
Guide to understanding and building state-of-the-art language models
Prerequisites: Strong foundations in programming and deep learning
Read NowRecent Articles & Insights
Oct 21, 2025
Step-by-step guide to walk you through the exact endpoints and response specs OpenAI expects, including the undocumented OIDC location and token exchange.
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The Model Context Protocol (MCP) promises to unify AI tools, saving you money on subscriptions. So why has it failed to gain traction? We'll look at the technical and market hurdles holding it back.
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How to stop saying 'AI' for everything. This guide gives you 30 specific machine learning terms that you can use to demonstrate proficiency.
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Learn what the Model Context Protocol (MCP) is and follow our step-by-step guide to connect Claude to an external MCP server, giving it access to live data and powerful tools.
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Learn to build a secure and scalable Model Context Protocol (MCP) server using the fastapi_mcp library. This step-by-step guide covers setup, authentication, and integration with AI tools like Claude, turning your APIs into a powerful toolkit for large language models.
Sep 18, 2025
Before you spend a fortune fine-tuning an LLM, discover faster, cheaper, and often more effective methods: prompt engineering and RAG. Learn why fine-tuning should be your last resort.
Sep 18, 2025
Learn which gradient boosting model to choose for speed, accuracy, and handling categorical data, with code examples and diagrams to guide you.
Sep 13, 2025
This complete guide details how to build a sophisticated course recommendation engine using LLMs, vector embeddings, and advanced semantic search. Learn data enrichment, prompt engineering, vector database implementation, and the logic for creating truly personalized learning paths.