05satyam/ai-ml

AI, ML, 딥러닝 및 LLMs — 콘텐츠 & 쿡북

AI/ML, Deep Learning, Generative AI, RAG, Agent, Fine-tuning 및 현대 툴링을위한 노트북, 데모 및 리소스의 큐레이터, 핸드 온 라이브러리.

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☸ PyPI 패키지

InstructVault에 대한 의견

• PyPI: https://pypi.org/project/instructvault/
• GitHub: https://github.com/05satyam/instruct_vault

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Non-Convex 최적화 벤치 마크 기능

• PyPI: https://pypi.org/project/nonconvexoptimzationfunclib/
• GitHub: [T3]

** 표준 non-convex benchmark 함수의 파이썬 라이브러리 ** 증발 및 비교 최적화 알고리즘.

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ML 인터뷰 핸드북

• GitHub: [T4]

** classical ML의 기본 사항**: 설명, 파생, 선택, 디버그, 고위 SWE / MLE 인터뷰에서 그들을 방어.

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의 표 내용

1. 사명 및 범위
2. 경로 선택
3. 기초 (AI/ML 핵심)
4. LangChain (모든 노트북)
5. LlamaIndex (모든 노트북)
6. RAG 시스템 (모든 Variants)
7. 대리인 & 오케스트라
8. 도표 & 다탈
9. MCP (모형 콘텍스트 프로토콜)
10. ·.

AI, ML, Deep Learning & LLMs — Content & Cookbooks

A curated, hands-on library of notebooks, demos, and resources for AI/ML, Deep Learning, Generative AI, RAG, agents, fine-tuning, and modern tooling.

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📦 PyPI Packages

InstructVault

• PyPI: https://pypi.org/project/instructvault/
• GitHub: https://github.com/05satyam/instruct_vault

pip install instructvault

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Non-Convex Optimization Benchmark Functions

• PyPI: https://pypi.org/project/nonconvexoptimzationfunclib/
• GitHub: https://github.com/05satyam/nonconvexoptimizationfunclib

A Python library of standard non-convex benchmark functions for evaluating and comparing optimization algorithms.

pip install nonconvexoptimzationfunclib

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📘 ML Interview Handbook

• GitHub: https://github.com/05satyam/ml-interview-prep

Companion handbook for classical ML fundamentals: explain, derive, choose, debug, and defend them in senior SWE / MLE interviews.

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📌 Table of Contents

1. Mission & Scope
2. Pick your path
3. Foundations (AI/ML Core)
4. LangChain (All Notebooks)
5. LlamaIndex (All Notebooks)
6. RAG Systems (All Variants)
7. Agents & Orchestration
8. Graph & Multimodal
9. MCP (Model Context Protocol)
10. Observability
11. Interview Experiences (includes GenAI question banks)