Learn Artificial Intelligence from the basics to intermediate level. Token Tool Hub offers practical guides on machine learning, neural networks, and AI’s role in blockchain and crypto
AI Tools for Crypto Market Analysis Turn raw feeds, prices, on-chain events, and news; into decisions. We map the toolchain: data sources, feature engineering, sentiment/RAG, anomaly detection, dashboards, alerting, and the prompts that keep your analysis rigorous. Heads-up: These approaches are educational. If you apply them to trading or risk, use proper evaluation, human oversight, […]
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How AI Is Used in Trading Bots From simple rule-following scripts to learning systems that adapt to markets, this chapter shows where AI actually helps: signals, execution, risk, and monitoring. It also covers backtesting pitfalls, on-chain realities, and the guardrails professional teams use. Important: Educational content only; this is not financial advice. Live trading is
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AI in Finance & Crypto Risk & fraud models, time-series forecasting, portfolio thinking, sentiment & news pipelines, on-chain analytics, DeFi risk, governance summarization, and the guardrails you need to avoid costly mistakes. Important: This chapter is educational and not financial advice. AI can inform decisions but should not be used as an autopilot for trading
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Computer Vision Basics How machines “see”: pixels and tensors, convolutional neural networks, essential tasks (classification, detection, segmentation, keypoints), evaluation, data strategy, deployment on web/mobile/edge, and pitfalls to avoid. TL;DR: Computer Vision (CV) converts images and video into structure and decisions. Modern CV uses convolutional neural networks (and increasingly transformers) trained on labeled images, then fine-tuned
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Natural Language Processing (NLP) Explained Tokens, embeddings, classic methods, transformers, evaluation, safety, and how to build language features that users actually trust. TL;DR: NLP turns raw text into structure and meaning. Early pipelines used bag-of-words and n-grams with linear models; modern systems lean on transformers and large language models (LLMs) that understand context via attention.
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Basics of Neural Networks From the intuition of artificial “neurons” to training with backpropagation, regularization, and modern architectures. this chapter demystifies neural nets and shows when they help (and when simpler models win). TL;DR: A neural network stacks layers of simple units that apply weights, add biases, and pass values through nonlinear activations. By composing
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Introduction to Machine Learning (Supervised vs Unsupervised) A practical, hands-on tour of machine learning: what it is, a short history, how models learn, the difference between supervised and unsupervised learning, how to evaluate systems, and how to ship something useful without drowning in jargon. TL;DR: Machine Learning (ML) learns patterns from data to make predictions
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AI Ethics & Risks A practical playbook for responsible AI: fairness, privacy, security, safety, human oversight, and continuous monitoring, so you ship useful systems that people can trust. TL;DR: Responsible AI is a lifecycle: define acceptable behavior, govern data, evaluate for fairness and quality, secure the model and pipeline, deploy with guardrails and human oversight,
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Everyday Examples of AI From phones and feeds to banking, logistics, and on-chain analytics, here’s how AI actually shows up day-to-day, and what’s happening behind the scenes. TL;DR: AI quietly powers recognition (who/what), recommendation (what next), routing (how to get there), risk (is this safe), and generation (summaries, images, code). It blends classic ML on
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AI vs Machine Learning vs Deep Learning Understand how these terms relate, when each approach is most useful, and how modern products combine rules, classic ML, deep learning, and human oversight. TL;DR: AI is the broad goal of intelligent behavior. Machine Learning (ML) achieves AI by learning from data. Deep Learning (DL) is a powerful
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