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Source channel @githubtrending · Post #14845 · Jun 20

#jupyter_notebook#ai#artificial_intelligence#chatgpt#deep_learning#from_scratch#gpt#language_model#large_language_models#llm#machine_learning#python#pytorch#transformer You can learn how to build your own large language model (LLM) like GPT from scratch with clear, step-by-step guidance, including coding, training, and fine-tuning, all explained with examples and diagrams. This approach mirrors how big models like ChatGPT are made but is designed to run on a regular laptop without special hardware. You also get access to code for loading pretrained models and fine-tuning them for tasks like text classification or instruction following. This helps you deeply understand how LLMs work inside and lets you create your own functional AI assistant, gaining practical skills in AI development[1][2][3][4]. https://github.com/rasbt/LLMs-from-scratch

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#jupyter_notebook #ai #artificial_intelligence #chatgpt #deep_learning #from_scratch #gpt #language_model #large_language_models #llm #machine_learning #python #pytorch #transformer

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@githubtrending · Post #15565 · 03/16/2026, 11:30 AM

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#python#ai#deepagents#langchain#langgraph Deep Agents is a ready-to-use AI agent framework that comes with built-in planning, file management, and task delegation tools. It breaks down complex tasks into manageable steps, maintains context across conversations, and can spawn specialized sub-agents to handle focused work independently. You benefit from getting a working agent immediately without building from scratch, while retaining full customization options for your specific needs. The framework handles context management automatically, making it ideal for multi-step projects that traditional agents struggle with. https://github.com/langchain-ai/deepagents

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#python #ai #deepagents #langchain #langgraph