ToolDix AI Learning
A working library for people who want more than a list of links: structured public courses, original study notes, primary readings, video analysis prompts, and small build exercises. Start with the work you want to do, not the tool you happen to see first.
Curated learning library
Every card opens an in-site guide with author and source context, a practical first step, and discussion. The original material remains with its publisher.
96 resources
DeepLearning.AI · Andrew Ng
A non-technical orientation to what generative AI can do, where it fails, and how teams can use it responsibly.
Microsoft
A structured, code-forward curriculum covering generative AI concepts and application building.
Google for Developers
Interactive modules on models, data, embeddings, neural networks, and production considerations.
Hugging Face
A practical path through Transformers, tokenizers, datasets, fine-tuning, and modern LLM workflows.
fast.ai · Jeremy Howard and Rachel Thomas
A practical video course for applying deep learning to vision, language, tabular data, and deployment.
Stephen Wolfram Writings · Stephen Wolfram
A visual, long-form attempt to explain token prediction, neural networks, training, and why fluent output is not the same as grounded correctness.
arXiv · Ashish Vaswani et al.
The paper that introduced the Transformer architecture used in many modern language and multimodal systems.
OpenAI
Focused examples for building, evaluating, and operating AI-assisted applications.
GitHub Docs
A first-party documentation directory for using Copilot in the editor, GitHub, code review, and agent workflows.
DAIR.AI
A browsable reference for prompt techniques, evaluation ideas, reliability patterns, and risks.
Anthropic Documentation
A primary documentation hub for designing prompts, defining success criteria, and improving outputs with deliberate techniques.
OpenAI Developer Documentation
A developer-oriented guide for shaping model behavior through clear instructions, context, and evaluation-driven iteration.
Hugging Face
An applied route through agent concepts, tool use, and agent frameworks.
OpenAI Agents SDK
A first-party quickstart for defining an agent, adding tools, coordinating handoffs, and observing a run.
Stability AI
Provider documentation for turning prompts and images into generated visual outputs through an API.
Magenta
An open-source research project exploring machine learning as a creative tool for music and art.
Google Cloud Documentation
A first-party guide for producing speech audio from text or SSML through a cloud text-to-speech workflow.
Meshy
Documentation for generating and preparing 3D assets from text or image inputs.
ComfyUI
A public set of example workflows for learning how nodes, models, and outputs connect.
ComfyUI
The official documentation hub for installation, a first generation, interface concepts, tutorials, nodes, and workflow development.
UNESCO
A policy-facing framework for thinking about AI literacy, human agency, ethics, and learning outcomes.
TeachAI
Practical guidance for educators on AI use, policy, and classroom decision-making.
NVIDIA Deep Learning Institute
A visual introduction to generative AI concepts and practical starting points from NVIDIA's learning platform.
Google for Developers
A practical guide to deciding whether a real problem is suitable for machine learning before choosing a model or collecting data.
Andrej Karpathy · Andrej Karpathy
A creator-led programming series that builds neural-network concepts from small code examples upward.
Learn Prompting
A structured public course covering prompt basics, reliability patterns, and prompt-injection awareness.
Google AI for Developers
First-party guidance for structuring instructions, examples, and context when building model interactions.
Anthropic
A collection of task patterns that can be treated as starting hypotheses and tested against a real workflow.
OpenAI
A framework and documentation set for composing agents, tools, handoffs, guardrails, and traces.
Anthropic Engineering
An engineering perspective on choosing simple workflows before adding autonomous loops, tools, and coordination.
LangChain
Courses focused on stateful agent workflows, human intervention, memory, and deployment concerns.
Hugging Face
A hands-on introduction to diffusion models, image generation pipelines, and the implementation ideas behind them.
Adobe
Short official tutorials for ideation, image generation, editing, and production handoff in a design workflow.
Stability AI
A primary starting point for understanding Stability's image-generation platform, API workflow, and operational constraints.
Runway
Official tutorials for planning, generating, iterating, and exporting AI-assisted video work.
Luma AI
A help and learning hub for using AI video and visual-generation workflows in short production cycles.
Google Cloud
Documentation for expressing subject, action, camera, lighting, and audio intent in AI video prompts.
Magenta / Google Research
A research and open-source introduction to combining interpretable digital signal processing elements with neural networks for audio synthesis and transformation.
Suno
Official help material for turning creative intent into generated music and understanding account-level features.
ElevenLabs
Primary documentation for text-to-speech, voice controls, and implementation choices.
Microsoft Azure
Quickstarts and architecture guidance for speech recognition, synthesis, and voice-enabled applications.
HeyGen
Official guidance for avatar and translated-video workflows, including setup and output management.
Tripo
A developer reference for text-to-3D and image-to-3D asset generation workflows.
Blender Foundation
An open benchmark project that helps creators understand the hardware and rendering context around 3D production.
ComfyUI
The source repository, installation notes, release history, and issue discussions for the core node-based workflow tool.
ComfyUI-Manager
An open-source manager project for discovering and managing workflow extensions and their installation state.
OpenAI
Official documentation for delegating coding tasks, providing context, reviewing changes, and using Codex within an engineering workflow.
Anthropic
Official material for using a coding agent in a local repository while keeping task boundaries, permissions, and verification explicit.
Aider
Documentation for an open-source pair-programming tool designed around working directly with a Git repository.
TeachAI
A practical starting point for creating classroom guidance, responsible-use norms, and age-appropriate AI activities.
AI4K12
A K-12-oriented framework for introducing AI concepts through a small set of durable ideas and classroom practices.
Google Creative Lab
An interactive project for exploring image, sound, and pose classification without a full programming setup.
Microsoft
A project-based curriculum covering regression, classification, clustering, natural language processing, time series, and reinforcement learning with quizzes and assignments.
Microsoft
A broad curriculum spanning neural networks, computer vision, natural language processing, and other foundational AI techniques through lessons and labs.
PyTorch
An end-to-end quickstart through tensors, datasets, transforms, model construction, automatic differentiation, optimization, and saving a trained model.
TensorFlow
Official notebook tutorials for beginner workflows, data loading, Keras models, vision, text, structured data, and distributed training.
MIT 6.S191
A concise university course covering deep-learning foundations, sequence models, generative models, reinforcement learning, and current applications.
Full Stack Deep Learning
A practitioner-focused course covering prompt engineering, augmented language models, LLMOps, deployment, user experience, and product development.
Hugging Face
A practical notebook for creating a synthetic question set and evaluating retrieval-augmented answers with automated judges and explicit metrics.
LlamaIndex
An official starter path through document loading, indexing, retrieval, querying, and inspection of a compact RAG application.
LangChain
A guided implementation of retrieval and agentic RAG using loaders, embeddings, vector stores, tools, and a model-driven workflow.
OpenAI Developer Documentation
Guidance for defining objectives, collecting representative cases, selecting graders, and continuously evaluating model behavior.
Google PAIR
A human-centered product guide covering user needs, data, mental models, explainability, feedback, control, and graceful failure in AI products.
Google Codelabs
A guided product exercise for applying human-centered data practices, explainability, and calibrated trust to an AI feature concept.
Microsoft Learn
A business-leader learning path on creating value, responsible adoption, organizational readiness, and scaling AI initiatives.
OpenAI Academy
A practical learning path for moving from individual prompts to repeatable workflows, review habits, and useful applications of AI at work.
Duke University · Jon Reifschneider
A non-coding specialization on machine-learning foundations, project leadership, data processes, human-centered design, privacy, and ethical AI product decisions.
Google for Developers
A structured introduction to fairness, accountability, safety, and privacy considerations when developing and scaling AI systems.
NIST
A voluntary framework organized around Govern, Map, Measure, and Manage for operationalizing trustworthy and responsible AI risk management.
NIST
A companion profile that applies the AI RMF to generative-AI risks, actions, measurement needs, and governance considerations.
OWASP GenAI Security Project
A practitioner reference for common LLM application risks such as prompt injection, sensitive-data disclosure, excessive agency, and insecure output handling.
Microsoft
A public overview of Microsoft's responsible-AI principles, governance approach, and resources for putting accountability into product development.
Made With ML · Goku Mohandas
An end-to-end course covering product design, data, modeling, testing, reproducibility, serving, observability, and CI/CD for machine-learning systems.
DataTalks.Club
A project-led curriculum covering experiment tracking, orchestration, deployment, monitoring, testing, infrastructure, and a production capstone.
Full Stack Deep Learning
A systems-oriented course on infrastructure, experiment management, troubleshooting, testing, deployment, monitoring, data management, and team practices.
Google for Developers · Martin Zinkevich
A field guide to production ML that prioritizes solid pipelines, simple baselines, meaningful features, measurable objectives, and staged system evolution.
MLflow
Official quickstarts for experiment tracking, model packaging, evaluation, registry workflows, and serving.
PyTorch · Nathan Inkawhich
A code-led introduction to training a deep convolutional generative adversarial network, including data, generator, discriminator, loss, optimization, and output inspection.
OpenAI Help Center
First-party guidance for creating, editing, storyboarding, and iterating on short generated videos in Sora.
Adobe
Official tutorials for text-to-video and image-to-video generation, camera controls, iteration, and integration with an editing workflow.
Hugging Face
A structured course on audio data, transformer architectures, classification, speech recognition, text-to-speech, and real-world audio applications.
Meta AI Research
A research codebase for audio and music generation that includes model inference, conditioning, training references, and evaluation utilities.
SpeechBrain
Official tutorials for speech recognition, speaker recognition, enhancement, separation, and other speech-processing workflows.
Stability AI
An image-to-3D research repository for generating textured 3D assets, with setup, inference, model, and output guidance.
NVIDIA Research
A research project on generating textured 3D meshes with explicit surface representations, accompanied by paper, visual results, and code references.
ComfyUI
A guided official tutorial entry point for understanding model loading, prompts, sampling, latent decoding, and saving output in a ComfyUI graph.
University of Helsinki & MinnaLearn
A non-technical course that builds intuition for defining AI, search, probability, machine learning, neural networks, and societal impact through short readings and exercises.
Harvard University · Brian Yu and David J. Malan
A project-led introduction to search, knowledge representation, uncertainty, optimization, machine learning, neural networks, and language in Python.
Stanford NLP
An end-to-end technical course on data pipelines, tokenization, transformer implementation, training, scaling, evaluation, and systems efficiency for language models.
Microsoft
A structured introduction to agent design patterns, tool use, multi-agent collaboration, planning, memory, retrieval, observability, and trustworthy operation.
Google for Developers
A guided build that uses Google's Agent Development Kit to define an agent, connect tools and models, manage sessions, and observe a working flow.
Model Context Protocol
An official tutorial for building a small MCP server with tools, connecting it to a host, handling transport correctly, and testing the resulting integration.
Hugging Face
A focused course on context engineering for coding agents using skills, plugins, MCP servers, subagents, hooks, and deliberate control of the agent loop.
A security framework for mapping AI risks, extending established controls, automating defenses, and adapting protection to model, data, infrastructure, and application layers.
MITRE
A structured knowledge base of adversary tactics, techniques, case studies, and mitigations for machine-learning and generative-AI systems.
Stanford Institute for Human-Centered AI
A globally sourced report on AI research, technical performance, adoption, investment, responsible AI, public opinion, education, policy, and societal impact.
Have a useful, rights-cleared learning resource?
Submit an original lesson, an openly licensed adaptation, or a curated resource. Every submission is reviewed before it appears publicly.
Choose a path
New lessons are reviewed before publication.
Choose a practical starting point, build a weekly practice rhythm, and learn AI without collecting courses you never finish.
Write, test, and improve prompts that give models the context, constraints, and evaluation signals they need.
Learn the model, data, optimization, and evaluation foundations behind modern AI by training and inspecting small systems yourself.
Understand tool use, retrieval, memory, guardrails, and how to design an agent workflow that stays observable.
Go from a visual brief to consistent generated images while managing references, revisions, and commercial-use checks.
Plan clips, maintain visual continuity, and combine generation with editing rather than relying on one-shot video prompts.
Use AI for music ideation, arrangement, and iteration while keeping licensing and voice rights in view.
Learn text-to-speech, avatars, lip sync, consent, and disclosure practices for trustworthy synthetic media.
Explore text-to-3D and image-to-3D workflows, then prepare assets for real editing, rendering, or game pipelines.
Build node-based image workflows that are reproducible, portable, and easier to debug than a growing pile of screenshots.
Use AI to accelerate planning, implementation, testing, and review without outsourcing engineering judgment.
Understand transformer-based language systems, build retrieval with citations, and evaluate quality before treating a demo as a product.
Move models from notebooks into reproducible services with versioned data, automated tests, deployment controls, and production monitoring.
Choose valuable AI problems, design trustworthy human-AI interactions, and connect experiments to measurable product and business outcomes.
Turn responsible-AI principles into risk registers, threat models, evaluation gates, incident plans, and accountable operating practices.
Use AI to support teaching and learning with clear goals, privacy safeguards, and academic-integrity boundaries.
