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Category: "Machine Learning"

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  1. The McGinty Equation
    Unifying Quantum Field Theory and Fractal Theory to Understand Subatomic Particle Behavior
    Chris McGinty

    Beyond practical applications, the McGinty Equation underscores the beauty and elegance of physics, demonstrating how theoretical concepts can be used to solve complex problems and uncover new truths about the universe. Its potential applications extend beyond quantum mechanics and into other fields, such as biology, finance, and computer science.

  2. Interpreting Machine Learning Models With SHAP
    A Guide With Python Examples And Theory On Shapley Values
    Christoph Molnar

    Master machine learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your machine learning applications.

  3. OpenAI GPT For Python Developers
    The art and science of building AI-powered apps with GPT-4, Whisper, Weaviate, and beyond
    Aymen El Amri

    Explore the fascinating world of Artificial Intelligence and solve real-world problems! In this practical guide, you will build intelligent real-world applications using GPT-4, Embeddings, Whisper, Weaviate, and more tools from the OpenAI ecosystem. You don't need to be a data scientist or machine learning engineer to follow this guide!

  4. Supervised Machine Learning မိတ်ဆက်
    Regression and Classification
    myothida (ဒေါက်တာမျိုးသီတာ)

    Machine Learning ၏ အဓိပ္ပာယ်ကို လူအများစု အလွယ်တကူ နားလည်နိုင်ရန် အဓိပ္ပာယ် ဖွင့်ဆိုရမည်ဆိုပါက ကွန်ပြူတာ (သို့မဟုတ်) စက် တစ်ခုခုကို လူ့ကိုယ်စား (သို့မဟုတ်) လူကဲ့သို့ ပြုမူဆောင်ရွက်နိုင်စေရန် သင်ကြားပေးခြင်းဟု ယေဘုယျ ဖွင့်ဆို နိုင်ပါသည်။ဥပမာ ပေးရမည် ဆိုပါက ၂၀၂၂ ခုနှစ် နို၀င်ဘာလတွင် OpenAI မှ စတင် ထုတ်လိုက်သည့် ChatGPT ဖြစ်သည်။

  5. Introduction to Supervised Machine Learning
    Regression and Classification
    myothida

    A newly released AI invention, ChatGPT is able to answer questions and even write code for developers. Does this information make you feel that understanding Machine Learning might be challenging for you? This book provides a comprehensive and easy-to-follow introduction to the fundamental concepts of machine learning methods.

  6. Data Cleaning: The Ultimate Practical Guide
    From Dirty Data to Clean Data
    Lee Baker

    Data Cleaning: The Ultimate Practical Guide is a guide to understanding what dirty data is, and how it gets into your dataset.This book will help you prevent most types of dirty data getting into your dataset, and clean out quickly and efficiently the remaining errors, so you can have clean, fit-for-purpose and analysis-ready data.

  7. Programming of distributed and Web crowdsourcing applications using mobile agents and the JavaScript Agent Machine can be so easy! Less than 100 lines code are required to create a multi-agent system. Only basic JavaScript knowledge is required.

  8. Aprendizaje Profundo con PyTorch Paso a Paso - Volumen I: Fundamentos
    Una Guía para Principiantes
    Daniel Voigt Godoy and Jesús Martínez-Blanco

    ¿Estás buscando un libro con el que puedas aprender sobre aprendizaje profundo y PyTorch sin tener que pasar horas descifrando texto y código críptico? ¿Un libro técnico que sea también legible y entretenido? ¡Aquí lo tienes!

  9. No Description Available
  10. Serverless 101 - Essential Patterns for Data Scientist
    Hands-on guideline on deploying applications using serverless.com for data scientists
    Konrad Semsch

    Get to know how to deploy small applications and machine learning solutions using the serverless.com framework.

  11. Zefs Guide to Deep Learning Flashcards is a set of digital flashcards that accompany the book Zefs Guide to Deep Learning. Anyone wanting to improve their knowledge of the key concepts in machine learning and deep learning will benefit from studying with these flashcards, whether to land that dream AI job or ace their machine learning exams.

  12. Machine Learning in Python for Process Systems Engineering
    Achieve Operational Excellence Using Process Data
    Ankur Kumar and Jesus Flores Cerrillo

    This book provides a guided tour along the wide range of ML methods that have proven useful in process industry. Step-by-step instructions, supported with real process datasets, show how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, soft sensing, and process control. Also available at Google Play.

  13. Zefs Guide to Deep Learning is a short guide to the most important concepts in deep learning, the technique at the center of the current artificial intelligence revolution. It will give you a strong understanding of the core ideas and most important methods and applications. All in around only 150 pages!

  14. The Hitchhiker's Guide to Responsible Machine Learning
    The introduction to Interpretable and Responsible Machine Learning and eXplainable Artificial Intelligence with code examples for R
    Przemysław Biecek

    Selected modern machine learning techniques and the intuition behind them. Methods are supplemented by code snippets with examples in R language. The process is shown through a comic book describing the adventures of two characters, Beta and Bit.  See the flipbook version at https://betaandbit.github.io/RML/

  15. All statistical foundations you need to understand and use machine learning! It includes R/Pyhton software and Shiny dashboards to illustrate numerically the most important concepts.