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macOS app for corpus ingestion, semantic search, and concept universe visualization powered by local LLMs via LM Studio. Architecture: - Go daemon (17MB single binary, zero dependencies) - chi router, pure-Go SQLite, tiktoken tokenizer - 6-stage pipeline: scan → extract → chunk → embed → annotate → conceptualize - Brute-force cosine vector search in memory - 89 tests across 8 packages - SwiftUI app (macOS 15+) - Multi-workspace management with auto-start daemons - Live pipeline progress, search, concept browser - WebGPU 3D universe renderer with Canvas2D fallback - Custom crystal app icon
27 lines
3.3 KiB
Text
27 lines
3.3 KiB
Text
Introduction to Machine Learning
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Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
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The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
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Supervised Learning
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Supervised learning is where you have input variables and an output variable and you use an algorithm to learn the mapping function from the input to the output. The goal is to approximate the mapping function so well that when you have new input data, you can predict the output variables for that data. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process.
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Common algorithms include linear regression, logistic regression, support vector machines, random forests, and neural networks. Each has different strengths and is suited to different types of problems.
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Unsupervised Learning
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Unsupervised learning is where you only have input data and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.
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These are called unsupervised learning because there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data. Common techniques include clustering (k-means, hierarchical clustering) and dimensionality reduction (PCA, t-SNE).
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Reinforcement Learning
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Reinforcement learning is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. The agent learns from the consequences of its actions, rather than from being explicitly taught. It selects its actions on basis of its past experiences and also by new choices.
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Deep Learning
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Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and transformers have been applied to fields including natural language processing, speech recognition, computer vision, and recommendation systems.
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The transformer architecture, introduced in 2017 by Vaswani et al. in the paper "Attention Is All You Need", has become the foundation for modern large language models like GPT, BERT, and their successors. Transformers use self-attention mechanisms to process sequential data, allowing them to capture long-range dependencies more effectively than previous architectures like RNNs and LSTMs.
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