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
The Ethics of AI: Can Machines Make Moral Decisions? As artificial intelligence moves from research labs into hospitals, courts, classrooms, cars, and the enterprise, its decisions increasingly carry moral weight. But can machines be moral agents, or are they merely tools reflecting our values and blind spots? This deep-dive bridges philosophy and engineering: we’ll map […]
The Ethics of AI: Can Machines Make Moral Decisions? Read More »
How AI Works: A Beginner’s Guide to Algorithms and Automation Artificial Intelligence (AI) sounds like magic until you peek under the hood. It’s not sorcery, it’s statistics, optimization, and automation running at scale. This beginner’s guide walks you from first principles to practical systems: what AI is, how algorithms learn from data, how automation deploys
How AI Works: A Beginner’s Guide to Algorithms and Automation Read More »
Building Simple AI Models with Python (Beginner Code Examples) A hands-on path to your first working ML pipeline: data loading, splits, baselines, feature engineering, training, evaluation, and saving a small model. Includes tabular classification, text classification, and a minimal API. Read first: These examples are educational. If you use models for trading or risk, apply
Building Simple AI Models with Python Read More »
Prompt Engineering for AI Productivity A practical guide to writing prompts that produce consistent, verifiable, and useful outputs , with templates for research, governance, on-chain risk, market briefings, and dev workflows. Integrates with our Prompt Libraries. TL;DR: Good prompts set role, goal, context, constraints, and output format. Add examples, require sources, and define evaluation rubrics.
Prompt Engineering for AI Productivity Read More »
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,
AI Tools for Crypto Market Analysis Read More »
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
How AI Is Used in Trading Bots Read More »
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
AI in Finance and Crypto Read More »
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
Computer Vision Basics Read More »
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.
Natural Language Processing (NLP) Explained Read More »
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
Basics of Neural Networks Read More »
