Explore the evolution of machine learning from its inception, shaping in the mid-20th century with Alan Turing's ideas. Learn about early use cases, including checkers, speech recognition, and OCR systems. Discover how machine learning works through data collection, model training, and practical examples like email spam detection. Uncover the significant impact of machine learning on our daily lives.
Explore the essence of Decision Trees and Naive Bayes Classifiers. Discover their history, workings, and applications in machine learning. Uncover the simplicity of decision trees and efficiency of Naive Bayes.
Unveil the world of Neural Networks in this article. Learn how they emulate the human brain's processing, their historical journey, and explore a simple example of their classification prowess.
Learn to build a spam detection system using binary classification. Dive into C# models, ML.NET framework, and .NET Core.
Explore the power of Support Vector Machines (SVM) in AI and ML. Originating in the 1960s, SVMs excel in classification and regression tasks, making them crucial in diverse applications.
Learn about types, differences, and practical usage. Choose, train, evaluate, and deploy models effectively. Explore tools, data preprocessing, and experimentation for successful implementation.
Unlock the essence of machine learning and artificial intelligence with embeddings—a transformative tool converting high-dimensional data into a more manageable space, vital for NLP, images, and audio processing.
Explore diverse machine learning model formats and their file extensions. From ML.NET's .zip to ONNX's cross-platform versatility, delve into characteristics, use cases, and examples.
Explore the diverse AI models – ML.NET, BERT, and GPT – in this article comparing their strengths and applications.
Discover the transformative impact of ONNX in machine learning. This article delves into ONNX models, their history, and the role of ONNX Runtime, revolutionizing model sharing and interoperability.
Dive into "The Essence of Embeddings in AI." Discover how embeddings, like skilled librarians, distill complex data. Explore the intersection of AI embeddings and memes, unveiling intriguing implications and insights.
Learn to create, train, and deploy a spam detection AI model using ML.NET.
Explore Retrieval-Augmented Generation (RAG) in AI language models—combining language generation with knowledge retrieval. Learn how RAG works and its applications in customer service, education, healthcare, and news.
Explore the limitations of Large Language Models (LLMs) in AI, focusing on outdated information and the absence of data source attribution. Compare LLMs with Retrieval-Augmented Generation (RAG), emphasizing RAG's advantages in addressing these challenges. Discover the potential of RAG for real-time data retrieval and enhanced reliability in AI-driven solutions.
Discover Ollama, an advanced AI framework for running large language models locally. Simplify deployment with CLI REPL and REST API. Explore versatile models like Llama 2 and Starling.