datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ai-ml-foundations-book-collection
Introduction
I put this collection together after spending a lot of time reading what I think are some of the best books on AI, machine learning, deep learning, probabilistic modeling, optimization, reinforcement learning, transformers, LLMs, validation, and fairness. I want to share this with the community for one simple reason: I want to give people a structured path through the books that actually help them understand things deeply, instead of sending them through random… See the full description on the dataset page: https://huggingface.co/datasets/Ujjwal-Tyagi/ai-ml-foundations-book-collection.Lumina-Math-Foundations-1B
Lumina-Math-Foundations-1B
Lumina-Math-Foundations-1B is an industrial-scale foundational mathematical reasoning dataset in Indonesian,
Dataset Summary
Language: Indonesian (id) with LaTeX mathematical formulas.
Scale: 1 Billion Synthetic High-Fidelity Mathematical Reasoning instances.---
Data Schema & Field Breakdown
Field Name
Type
Description
problem
string
100% pure human natural language problem statement with standard LaTeX math… See the full description on the dataset page: https://huggingface.co/datasets/cloverx-id/Lumina-Math-Foundations-1B.ai-ml-foundations-book-collection
Introduction
I put this collection together after spending a lot of time reading what I think are some of the best books on AI, machine learning, deep learning, probabilistic modeling, optimization, reinforcement learning, transformers, LLMs, validation, and fairness. I want to share this with the community for one simple reason: I want to give people a structured path through the books that actually help them understand things deeply, instead of sending them through random courses… See the full description on the dataset page: https://huggingface.co/datasets/invincible-jha/ai-ml-foundations-book-collection.ai-ml-foundations-book-collection
Introduction
I put this collection together after spending a lot of time reading what I think are some of the best books on AI, machine learning, deep learning, probabilistic modeling, optimization, reinforcement learning, transformers, LLMs, validation, and fairness. I want to share this with the community for one simple reason: I want to give people a structured path through the books that actually help them understand things deeply, instead of sending them through random courses… See the full description on the dataset page: https://huggingface.co/datasets/amine-khelif/ai-ml-foundations-book-collection.ai-ml-foundations-book-collection
Introduction
I put this collection together after spending a lot of time reading what I think are some of the best books on AI, machine learning, deep learning, probabilistic modeling, optimization, reinforcement learning, transformers, LLMs, validation, and fairness. I want to share this with the community for one simple reason: I want to give people a structured path through the books that actually help them understand things deeply, instead of sending them through random… See the full description on the dataset page: https://huggingface.co/datasets/Dinamitrii/ai-ml-foundations-book-collection.ai-ml-foundations-book-collection
Introduction
I put this collection together after spending a lot of time reading what I think are some of the best books on AI, machine learning, deep learning, probabilistic modeling, optimization, reinforcement learning, transformers, LLMs, validation, and fairness. I want to share this with the community for one simple reason: I want to give people a structured path through the books that actually help them understand things deeply, instead of sending them through random… See the full description on the dataset page: https://huggingface.co/datasets/prashant-AI-ML/ai-ml-foundations-book-collection.intrinsic-intelligence-foundations
🌿 Intrinsic Intelligence Foundations
Toward truly autonomous and benevolent intelligence — beyond externally imposed objectives.
Intrinsic Intelligence Foundations is a structured, math-aware JSONL corpus built from K. Takahashi’s theoretical preprints (Fractal Category Theory / PF–UGV / “no-meta” autonomy line).It is designed to help LLMs understand mathematical structure, category-theoretic formalisms, and equation-level reasoning, while exposing an explicit architecture… See the full description on the dataset page: https://huggingface.co/datasets/kadubon/intrinsic-intelligence-foundations.