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Showing 20 open source projects for "interview"
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Daily Interview
Datawhale members have compiled a book covering machine learning
daily-interview is an open-source educational repository designed to help software engineers prepare for technical interviews through daily practice questions and curated learning materials. The project collects a wide range of interview questions related to algorithms, data structures, system design, and core computer science topics commonly tested by technology companies. -
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AIGC-Interview-Book
AIGC algorithm engineer interview secrets
...In addition to technical concepts, the repository also contains interview preparation materials such as practice questions, hiring insights, and career advice for AI engineers. The materials are organized so readers can study fundamental topics as well as advanced research areas that frequently appear in technical interviews. -
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llm_interview_note
Mainly record the knowledge and interview questions
llm_interview_note is an open-source knowledge repository designed to help engineers prepare for interviews and deepen their understanding of large language models (LLMs). The project compiles structured notes, conceptual explanations, and curated interview questions related to modern NLP and generative AI systems. It covers fundamental topics such as the historical evolution of language models, tokenization methods, word embeddings, and the architectural foundations of transformer-based models. The repository also explores practical engineering concerns including distributed training strategies, dataset construction, model parameters, and scaling techniques used in large-scale machine learning systems. ... -
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Data-Science-Interview-Questions-Answers
Curated list of data science interview questions and answers
Data-Science-Interview-Questions-Answers is a curated educational repository designed to help data science candidates prepare for technical interviews by organizing a large bank of questions and answers in one place. It began as a daily interview question initiative and was later consolidated into GitHub so learners could review the material more easily and revisit it over time. -
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
Cracking the Data Science Interview is an open educational repository that collects study materials, resources, and reference links for preparing for data science interviews. The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. -
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Deep Learning Interviews book
Hundreds of fully solved job interview questions
...The repository organizes problems across topics such as neural networks, optimization, probabilistic models, and mathematical foundations of machine learning. Each question is accompanied by detailed solutions that explain the reasoning behind the answers and the theoretical concepts involved. In addition to interview preparation, the material also serves as a condensed overview of many core topics taught in graduate-level machine learning programs. -
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Data Science Interviews
Data science interview questions and answers
...Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. In addition to theoretical questions, the repository also includes practical interview topics related to coding challenges, SQL queries, and algorithmic thinking. -
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AI-Job-Notes
AI algorithm position job search strategy
...The repository’s structure encourages progressive preparation—from fundamentals to mock interviews and post-interview retrospectives. It’s designed to reduce uncertainty and decision fatigue during the often lengthy job-hunt cycle. -
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System Design Academy
If you want to become good at AI & system design, join this newsletter
...Fundamental topics include APIs, caching, consistency, databases, DNS, Docker, Kafka, Kubernetes, load balancing, microservices, and service discovery. Separate guides cover interview frameworks and design exercises for products such as ChatGPT, Spotify, WhatsApp, and YouTube. The repository also collects AI agent concepts and selected software architecture white papers. -
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AgentGuide
AI Agent Development Guide, LangGraph in Action, Advanced RAG
...The guide covers topics such as agent frameworks, retrieval-augmented generation systems, multi-agent collaboration, memory management, and tool usage. It also includes practical projects, interview preparation materials, and curated research papers related to AI agents and LLM engineering. The project is designed not only for learning but also for career preparation, helping developers understand how to build portfolio projects and prepare for AI engineering roles. -
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LearnLLM.AI
Sharing knowledge about big models that everyone can understand
...The repository aims to provide intuitive explanations and practical examples so readers can understand both the theoretical and applied aspects of large language models. In addition to technical explanations, it includes curated interview questions and discussion topics that help readers prepare for industry interviews related to machine learning and generative AI. -
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Local-NotebookLM
Googles NotebookLM but local
Local-NotebookLM is a local AI tool for turning PDF documents into generated audio content. It works like a self-hosted alternative to NotebookLM-style document-to-audio workflows. The system extracts and processes PDF text, sends the content through an LLM, and converts the result into speech with configurable voices. Users can generate podcasts, summaries, interviews, lectures, debates, tutorials, news reports, executive briefs, and other formats. It supports multiple LLM providers,... -
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AI Copywriter
An AI copywriter that uses real copywriting skills + real marketing
AI Copywriter is a portable Markdown-based agent skill for producing persuasive marketing copy that still sounds human. It combines conversion-focused writing with a humanization process based on 33 recognizable patterns of machine-generated prose. Before drafting, the skill asks about the ideal customer, product category, and real story behind the message. It evaluates audience emotion, simplifies the explanation, and challenges vague inputs instead of hiding them behind generic language.... -
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ML-NLP
This project is a common knowledge point and code implementation
ML-NLP is a large open-source repository that collects theoretical knowledge, practical explanations, and code examples related to machine learning, deep learning, and natural language processing. The project is designed primarily as a learning resource for algorithm engineers and students preparing for technical interviews in machine learning or NLP roles. It compiles important concepts that frequently appear in machine learning discussions, including neural network architectures, training... -
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Anthropic's Original Performance
Anthropic's original performance take-home, now open for you to try
Anthropic's Original Performance repository contains the publicly released version of a performance challenge originally used by Anthropic as part of their technical interview process, offering developers the opportunity to optimize and benchmark low-level code against simulated models. The project sets up a baseline performance problem where participants work to reduce simulated “clock cycles” required to run a given workload, effectively challenging them to engineer faster code under constraints. This take-home includes starter code, tests, and tools to debug performance, aiming to measure how effectively one can apply algorithmic improvements and optimizations. ... -
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AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
AI-Engineer-Headquarters is a comprehensive educational repository designed to help developers become advanced AI engineers through a structured learning path and practical system-building exercises. The project serves as a curated collection of resources, methodologies, and tools covering topics across the entire artificial intelligence development lifecycle. Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real... -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is... -
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Deep-Learning-Interview-Book
Interview guide for machine learning, mathematics, and deep learning
Deep-Learning-Interview-Book collects structured notes, Q&A, and concept summaries tailored to deep-learning interviews, turning scattered study into a coherent playbook. It spans the core math (linear algebra, probability, optimization) and the practitioner topics candidates actually face, like CNNs, RNNs/Transformers, attention, regularization, and training tricks. -
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BitTiger
Lifelong Learning University from Silicon Valley
BitTiger is an extensive open educational repository that functions as a self-guided curriculum covering a wide range of topics in computer science, artificial intelligence, blockchain, system design, and technical interview preparation. Rather than being a traditional software application, it is structured as a knowledge base composed of curated learning materials, tutorials, and practical case studies designed to simulate real-world problem solving. The project reflects the philosophy of a “lifelong learning university,” aiming to help learners bridge the gap between academic knowledge and industry skills by exploring applied concepts across multiple domains. ... -
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Deep Learning cheatsheets
VIP cheatsheets for Stanford's CS 230 Deep Learning
...It was created to help students and practitioners quickly review important formulas, workflows, and implementation concepts without navigating large textbooks or lecture archives. The repository combines concise theoretical explanations with practical training advice, making it valuable for both interview preparation and hands-on model development. Its materials are widely used within the machine learning community because of their accessibility, clarity, and high information density.
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