For interview preparation and learning
Table of Contents:
- LeetCode
- Leetcode Patterns
List of questions with patterns + tips - LeetCode Explore
- Leetcode Patterns
- Codewars
- HackerRank
- CodeAbbey
- CodeRun Инструмент для подготовки к очному собеседованию в Яндексе. Задачи очень похожи на те, что будут на интервью.
- Другие
- Algoprog
paid - Яндекс:
- Computer Science Center:
- Подготовься к алгоритмическому собеседованию за 30 недель
paid - Introduction To Algorithms by MIT
- Algorithms + Data Structures from CS50's Introduction to Computer Science
- Тренировки по алгоритмам от Яндекса:
- Algorithmic concepts By Afshine Amidi and Shervine Amidi
- NeetCode. A better way to prepare for coding interviews.
- The Algorithms. Open Source resource for learning Data Structures & Algorithms and their implementation in any Programming Language
- Алгоритмы и структуры данных простыми словами
- Алгоритмика
- Leetcode. Company-wise questions
- Code Abbey Problems
- Unlocking Algorithm Efficiency: A Comprehensive Guide to Time and Space Complexity
- Data Structures Reference
- An Executable Data Structures Cheat Sheet for Interviews
- Coding Interview Guide
- Algorithmic Thinking
- Algorithm Notes
- Coding Interview University
- Tech Interview Cheat Sheet
- Comprehensive Data Structure and Algorithm Study Guide
- Data Structures & Algorithms by Google
- Design and Analysis of Algorithms
- Algorithms for Competitive Programming
- Как проходят алгоритмические секции на собеседованиях в Яндекс
- How to effectively use LeetCode to prepare for interviews
- Grokking Algorithms. An illustrated guide for programmers and other curious people
- Elements of Programming Interviews in Python: The Insiders' Guide
- Cracking the Coding Interview: 189 Programming Questions and Solutions
- Problem Solving with Algorithms and Data Structures using Python by Brad Miller and David Ranum, Luther College
- Competitive Programmer's Handbook by Antti Laaksonen
- Competitive Programming by Steven Halim
- Мартин Р. Чистый код: создание, анализ и рефакторинг / Robert C. Martin. Clean Code: A Handbook of Agile Software Craftsmanship
- Стив Макконнелл. Совершенный код. Мастер-класс / Steve McConnell. Code Complete: A Practical Handbook of Software Construction
- Основы Python
- Python: основы и применение
- Программирование на Python
- Поколение Python:
- CS50’s Introduction to Programming with Python
- CS50’s Introduction to Artificial Intelligence with Python
- Python Tutorial for Beginners (with mini-projects)
- What the f*ck Python! Exploring and understanding Python through surprising snippets
- Comprehensive Python Cheatsheet
- Python Koans. An interactive tutorial for learning the Python programming language by making tests pass
- Full Speed Python. Learning Python using a practical approach
- The Hitchhiker’s Guide to Python!
- A collection of design patterns and idioms in Python
- 53 Python Interview Questions and Answers
- Python: вопросы на собеседовании:
- [Часть I. Junior](https://pythonist.ru/ python-voprosy-sobesedovaniya-chast-i-junior/)
- Часть II. Middle
- Часть III. Senior
- Интерактивный тренажер по SQL
- Пакет SQL курсов:
- PostgreSQL Tutorial for Beginners
- Оконные функции SQL
- SQL Tutorial
- The Ultimate SQL Guide
- Онлайн тренажер SQL Academy
- Ace the SQL Interview
- Practice SQL
- SQLBolt. Learn SQL with simple, interactive exercises.
- SQL Tutorial by w3schools
- PostgreSQL Exercises
- The Querynomicon. An Introduction to SQL for Wary Data Scientists
- Machine Learning Mastery by Jason Brownlee
paid - Машинное обучение для людей. Разбираемся простыми словами
- Анализ малых данных
- Kaggle Competitions
- The Illustrated Machine Learning
- MLU-EXPLAIN
- Open Machine Learning Course by Yury Kashnitsky
- Машинное обучение (курс лекций, К.В.Воронцов)
- Прикладные задачи анализа данных (курс лекций, А.Г.Дьяконов) video
- Алгоритмы Машинного обучения с нуля
- Stanford CS229: Machine Learning by Andrew Ng
- Kaggle Learn
- Google Machine Learning Courses
- End to End Machine Learning by Brandon Rohrer
- Машинное Обучение в Python: Большой Курс для Начинающих
paid
- Учебник по машинному обучению от ШАД
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Machine Learning Simplified: A gentle introduction to supervised learning by Andrew Wolf
- The Kaggle Book
- Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
- Clean Machine Learning Code
- Interpreting Machine Learning Models With SHAP: A Guide With Python Examples And Theory On Shapley Values
- Interpretable Machine Learning. A Guide for Making Black Box Models Explainable by Christoph Molnar
- Machine Learning Q and AI. Expand Your Machine Learning & AI Knowledge With 30 In-Depth Questions and Answers by Sebastian Raschka
- Reliable Machine Learning: Applying SRE Principles to ML in Production by Cathy Chen
- Machine Learning Refined: Foundations, Algorithms, and Applications
- Models Demystified. A Practical Guide from t-tests to Deep Learning by Michael Clark & Seth Berry
- Anthology of Modern Machine Learning
- Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
- ML Papers of The Week by DAIR.AI
- word2vec Parameter Learning Explained
- geographic Data Science with Python
- WTTE-RNN - Less hacky churn prediction
- Прикладной анализ данных в социальных науках
- Ансамбли в машинном обучении
- Reflecting on 18 years at Google
- Machine Learning for Imbalanced Data
- Валидация моделей машинного обучения
- Do Machine Learning Models Memorize or Generalize?
- Supervised Machine Learning for Science by Christoph Molnar & Timo Freiesleben
- Лекция по курсу ММО - 24.03.2021, Отбор признаков (Feature selection)
- Kaggle Tips for Feature Engineering and Selection | by Gilberto Titericz | Kaggle Days Meetup Madrid
- featurewiz is the best feature selection library for boosting your machine learning performance with minimal effort and maximum relevance using the famous MRMR algorithm
- Feature Ranking and Selection
- Feature Engineering A-Z
- CatBoost - An In-Depth Guide
- Введение в библиотеку Transformers и платформу Hugging Face
- Build a Telegram chatbot with any AI model under the hood
- The Illustrated Machine Learning
- ML Primer by Boris Tseytlin
- Decision Trees. The unreasonable power of nested decision rules
- Векторное представление товаров Prod2Vec: как мы улучшили матчинг и избавились от кучи эмбеддингов
- Some characteristics of best-in-class ML portfolio projects
- Как метод подмены задачи борется с несовершенством данных (и мира)
- Feature Selection — Exhaustive Overview by Danny Butvinik
- A highly anticipated Time Series Cross-validator is finally here
- Интерпретация моделей и диагностика сдвига данных: LIME, SHAP и Shapley Flow
- Мое первое серебро на Kaggle или как стабилизировать ML модель и подпрыгнуть на 700 мест вверх
- Soccer Analytics 2022 Review
- Эй-Яй, крипта, MLOps и командный пет-проджект by yorko
- Understanding UMAP
- A new perspective on Shapley values, part I: Intro to Shapley and SHAP
- A new perspective on Shapley values, part II: The Naïve Shapley method
- 10 первых ошибок в карьере ML-инженера
- Understanding the Bias-Variance Tradeoff by Seema Singh
- The “Bias-Variance Trade-Off” Explained Practically (In Python)
- Модельный риск: как увеличить эффективность работы ML моделей в большой компании
- Эффективные ансамбли
- Reflecting on 18 years at Google
- Как не перестать быть data driven из-за data driften, или Пару слов о дрейфе данных
- StatQuest with Josh Starmer
- A new perspective on Shapley values:
- Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning by Sebastian Raschka
- How to avoid machine learning pitfalls: a guide for academic researchers by Michael A. Lones
- Core Machine Learning Skills
- Discover machine learning, data science & robotics competitions
- The Little Book of Deep Learning by François Fleuret
- Deep Learning with Python by François Chollet
paid - Multimodal Deep Learning
- Dive into Deep Learning
I prefer going through this book using Amazon SageMaker - Understanding Deep Learning by Simon J.D. Prince
- What are embeddings by Vicki Boykis
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger
- Deep Learning Specialization
paid - MIT 6.S191 Introduction to Deep Learning
- Full Stack Deep Learning - Course 2022
- 11-785 Introduction to Deep Learning from Carnegie Mellon University
- Neuromatch Academy: Deep Learning
- Efficient Deep Learning Systems by Yandex School of Data Analysis
- Short Courses by DeepLearning.AI
- TinyML and Efficient Deep Learning Computing
- Practical Deep Learning
- Practical Deep Learning for Coders part 2: Deep Learning Foundations to Stable Diffusion
- PyTorch for Deep Learning & Machine Learning (video) + Learn PyTorch for Deep Learning: Zero to Mastery book (site)
- Deep Learning Fundamentals by Sebastian Raschka and Lightning AI
- Future of AI is Foundation Models & Self-Supervised Learning
- Artificial Intelligence for Beginners
- 11-785 Introduction to Deep Learning + 11785 Spring 2024 Lectures
- Stanford CS 230 ― Deep Learning
cheatsheet - CS 330: Deep Multi-Task and Meta Learning
- Deep Learning with Catalyst
- Practical DL
- Deep Learning from the Foundations by fast.ai
- PyTorch Tutorials - Complete Beginner Course
- Школа глубокого обучения
- Neural Networks: Zero to Hero by Andrej Karpathy
- Introduction to Deep Learning by Sebastian Raschka
- Коллекция ручных задачек о нейросетях
- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks by Sebastian Raschka
- Deep Learning Tuning Playbook by Google
- A Step by Step Backpropagation Example
- PyTorch Fundamentals by Microsoft
- labml.ai Annotated PyTorch Paper Implementations
- Grokking PyTorch
- The Incredible PyTorch
- AI Fundamentals. Concepts, Definitions, Terms
- A Guide to Production Level Deep Learning
- A Gentle Introduction to torch.autograd
- WHAT IS TORCH.NN REALLY? by Jeremy Howard, fast.ai
- О «раздутом пузыре» нейросетей
- Cтатьи от команды DeepSchool
- Полезные материалы про PyTorch
- Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra adn Thomas Wolf
- Speech and Language Processing by Dan Jurafsky and James H. Martin
- Transformers for Natural Language Processing by Denis Rothman
- Нейронные сети и обработка текста
- Stanford CS224N: NLP with Deep Learning + Videos + Notes
- NLP Course | For You by Lena Voita + YSDA Natural Language Processing course
- Hugging Face course
- Natural Language Processing course by Valentin Malykh
- Stanford LSA 311: Computational Lexical Semantics by Dan Jurafsky
- Stanford CS224U: Natural Language Understanding
- Введение в обработку естественного языка
- Stanford CS 224V Conversational Virtual Assistants with Deep Learning
- Learn to Love Working with Vector Embeddings by Pinecone
- CS11-711 Advanced Natural Language Processing (at Carnegie Mellon University's Language Technology Institute) + Video + Assignments
- Linguistics for Language Technology
- Recommendations for Getting Started with NLP by Elvis
- Чат по NLP
- 100 вопросов про NLP
- The 1950-2024 Text Embeddings Evolution Poster
- awesome-nlp. A curated list of resources dedicated to Natural Language Processing
- Stanford Webinar - GPT-3 & Beyond
- Transformer Recipe by Elvis Saravia
- Transformer, explained in detail by Igor Kotenkov
- The Practical Guides for Large Language Models
- State of GPT by Andrej Karpathy
- LLM University by Cohere
- CS324 - Large Language Models
- Generative AI exists because of the transformer. This is how it writes
- Training & Fine-Tuning LLMs for Production
- LLMOps: Building Real-World Applications With Large Language Models
- A Survey of Large Language Models
- Transformers Tutorials
- Ruformers/Руформеры
- Схема энкодера архитектуры Трансформер
- Large Language Model Course
- Insights from Finetuning LLMs with Low-Rank Adaptation by Sebastian Raschka
- LLM Bootcamp - Spring 2023
- ChatGPT Course – Use The OpenAI API to Code 5 Projects
- Self-Attention & Transformers (CS 224n: Natural Language Processing with Deep Learning)
- Build a Large Language Model (From Scratch) by Sebastian Raschka
- Hands-on LLMs Course
- LLaMA-Factory. Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)
- Open LLMs. A list of open LLMs available for commercial use
- Overview of Large Language Models
- Mastering RAG: How To Architect An Enterprise RAG System
- Исчерпывающий гайд по опенсорсным языковым моделям
- Open-Source AI Cookbook
- Building LLM applications for production
- LLM Visualization
- The Illustrated Transformer by Jay Alammar
- Learn to Train and Deploy a Real-Time Financial Advisor
- Explainpaper
- ChatPDGF
- Reimagine Research
- Discover scientific knowledge and stay connected to the world of science
- Get scientific answers by asking millions of research papers
- Prompt Engineering Guide + Prompt Engineering Guide
- Prompt injection with Gandalf
- Prompt Engineering
- Prompt Engineering Guide
- Awesome ChatGPT Prompts
- Advanced Prompt Engineering
- Prompt engineering Guide by Open.ai
- Prompt Of The Year: 2023
- Train and Fine-Tune Sentence Transformers Models
- Working With Text Data using Sklearn + Text feature extraction using Sklearn
- minbpe. Minimal, clean code for the (byte-level) Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization
- Мультиклассификация экстремально коротких текстов классическими методами машинного обучения
- Рейтинг русскоязычных энкодеров предложений
- Как определять пользовательские намерения, о которых мы узнали 5 минут назад
- Самая большая BERT-подобная модель на русском, которая поместится на ваш компьютер
- ChatGPT как инструмент для поиска: решаем основную проблему
- GPT in 60 Lines of NumPy
- What Is ChatGPT Doing … and Why Does It Work?
- From GPT-3 to ChatGPT: Training Language Models on Instructions and Human Feedback
- Кто такие LLM-агенты и что они умеют?
- Word2Vec, Mikolov et al., Efficient Estimation of Word Representations in Vector Space
- FastText, Bojanowski et al., Enriching Word Vectors with Subword Information
- Attention, Bahdanau et al., Neural Machine Translation by Jointly Learning to Align and Translate
- Transformers, Vaswani et al., Attention Is All You Need
- BERT, Devlin et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- GPT-2, Radford et al., Language Models are Unsupervised Multitask Learners
- GPT-3, Brown et al, Language Models are Few-Shot Learners
- LaBSE, Feng et al., Language-agnostic BERT Sentence Embedding
- CLIP, Radford et al., Learning Transferable Visual Models From Natural Language Supervision
- RoPE, Su et al., RoFormer: Enhanced Transformer with Rotary Position Embedding
- LoRA, Hu et al., LoRA: Low-Rank Adaptation of Large Language Models
- InstructGPT, Ouyang et al., Training language models to follow instructions with human feedback
- Scaling laws, Hoffmann et al., Training Compute-Optimal Large Language Models
- FlashAttention, Dao et al., FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
- NLLB, NLLB team, No Language Left Behind: Scaling Human-Centered Machine Translation
- Q8, Dettmers et al., LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
- Self-instruct, Wang et al., Self-Instruct: Aligning Language Models with Self-Generated Instructions
- Alpaca, Taori et al., Alpaca: A Strong, Replicable Instruction-Following Model
- LLaMA, Touvron, et al., LLaMA: Open and Efficient Foundation Language Models
- Нейронные сети и компьютерное зрение
- CS231n: Deep Learning for Computer Vision + Videos
- EECS 442: Computer Vision + Videos
- Foundations of Computer Vision by Antonio Torralba, Phillip Isola and William T. Freeman
- К. Фальк. Рекомендательные системы на практике / Practical Recommender Systems by Kim Falk
- Personalized Machine Learning
- Авито. Рекомендации
- Recommenders. Best Practices on Recommendation Systems
- Рекомендательные системы
- Рекомендательные системы: идеи, подходы, задачи
- Временные ряды
- Topic 9. Time Series Analysis with Python
- Прогнозирование временных рядов
- Time Series
- Forecasting time series with gradient boosting: Skforecast, XGBoost, LightGBM, Scikit-learn and CatBoost by Joaquín Amat Rodrigo, Javier Escobar Ortiz
- ARIMA and SARIMAX models with Python by Joaquín Amat Rodrigo, Javier Escobar Ortiz
- Груздев А.В., Рафферти Г. Прогнозирование временных рядов с помощью Prophet, sktime, ETNA и Greykite
- Перрен Ж.Ж. Spark в действии / Spark in Action by Jean-Georges Perrin
- Learning Spark
- Data Analysis with Python and PySpark