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Category: "Machine Learning"

Books

  1. Anatomy of Deep Learning Principles
    Writing a Deep Learning Library from Scratch
    hwdong

    Talking about theory without implementation or only programming without principle explanations is difficult for people to understand the principles of deep learning. This book uses theoretical explanations and code implementations to explain the principles of deep learning and how to write a deep learning library from scratch.

  2. Statistics with Rust
    50+ Statistical Techniques Put into Action
    GitforGits | Asian Publishing House

    "Statistics with Rust" is your comprehensive resource to unlock Rust's true potential in modern statistical methods.

  3. Learning Pandas 2.0
    A Comprehensive Guide to Data Manipulation and Analysis for Data Scientists and Machine Learning Professionals
    GitforGits | Asian Publishing House

    Using Pandas 2.0, setting you on the path to becoming a data analysis powerhouse

  4. Get SH*T Done with Prompt Engineering and LangChain
    Build AI Applications with ChatGPT in Python
    Venelin Valkov

    Will AI replace you, or will you use AI to 10x yourself? Discover the secrets of Prompt Engineering and LangChain to build chatbots, summarization tools, and more. Get ready to revolutionize the way you approach AI development with this comprehensive guide.

  5. AI Domination: Chat GPT-3.5 Guide
    S.L. Jackson

    Discover: the ultimate Chat GPT-3.5 guide that will leave you wondering if your AI assistant has taken over! With witty humor and helpful tips.AI Domination: GPT-3.5 Guide is a must-read for anyone wanting to unlock the full potential of Chat GPT. Get your copy today and join the AI revolution!

  6. 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.

  7. 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!

  8. 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။

  9. 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.

  10. Feature Engineering & Selection for Explainable Models
    A Second Course for Data Scientists (Revised Edition)
    Md Azimul Haque
    No Description Available
  11. 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.

  12. Crowdsourcing and Simulation with Mobile Agents and the JavaScript Agent Machine
    Stefan Bosse

    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.

  13. 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!

  14. Little Book : Introduction to Non-Linear Model and Insights using R (nls)
    Somil
    No Description Available
  15. 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.