Machine Learning with Python

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Want any LLM to answer from your own documents? Most RAG setups quietly give weak, vague answers, and the model is almost never the real problem. Three small fixes decide whether it works, and the exact tools to use in 2026 are very specific. Full, concrete guide in one post.

On Reddit, someone collected all the resources they used to prepare for interviews on algorithms, system design, and machine coding. In the end, they made it to a Google interview. 1. Algorithms and Patterns Before grinding on problems, it's worth understanding the patterns. • All LeetCode Articles on Coding Patterns Summarized https://leetcode.com/discuss/interview-question/5366542/all-leetcode-articles-on-coding-patterns-summarized-in-one-page • Solved All Two Pointers Problems in 100 Days https://leetcode.com/discuss/study-guide/1688903/Solved-all-two-pointers-problems-in-100-days • Tree Question Pattern 2023 — Tree Study Guide https://leetcode.com/discuss/study-guide/2879240/tree-question-pattern-2023-tree-study-guide • Important and Useful Links from All Over LeetCode https://leetcode.com/discuss/general-discussion/665604/Important-and-Useful-links-from-all-over-the-LeetCode • Coding Interview Preparation Problems for Beginners https://leetcode.com/discuss/interview-question/448284/Coding-Interview-preparation-problems-for-beginners 2. Preparation for Companies • Google, Meta, Apple, Amazon Senior SDE Preparation https://prachub.com/?sort=hot&company=Meta%2CGoogle%2CTikTok%2CAmazon • A Study Guide for Passing the Google Interview https://prachub.com/interview-guide The author also made a small tracker for preparation: • company-specific questions • Todo / Solved / Revision statuses • automatic repetition scheduling • AI assistant with hints instead of ready-made solutions https://prachub.com/questions 3. System Design (HLD) Instead of random articles — structured collections: • Arch 25 — the most common systems and patterns • Arch 50 — infrastructure, data, and fault tolerance • Arch 75 — more complex scenarios and company-specific specialization • Arch All — a full bank of 103 HLD tasks • Core Concepts — 33 breakdowns of distributed systems 4. Machine Coding / LLD Many underestimate this part until their first interview failure. • MaCo 30 — the most common tasks • MaCo 60 — an extended collection • MaCo All — a full set of 103 tasks • Design Patterns — 31 design patterns

🔖 Cheat sheets on Transformers and LLMs from Stanford We found an excellent set of materials from the Stanford CME-295 course on large language models. They cover tokenization, the self-attention mechanism, prompting, fine-tuning, LLM-as-a-judge, RAG, AI agents, and reasoning models. ⛓️ Download here https://github.com/afshinea/stanford-cme-295-transformers-large-language-models

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