I want to learn axiomatic (Hilbert style ) logic. not just a book that says that it exist and is an good way to proof theorems. What is a good book to learn and practice this method? would like: ...
Introduction to Higher-Order Categorical Logic (Cambridge Studies in Advanced Mathematics)
Introduction to higher order catagorical logic by J. Lambek, March 25, 1988, Cambridge University Press edition, Paperback in English
Gödel's incompleteness theorems
Gödel's incompleteness theorems are two theorems of mathematical logic that are concerned with the limits of provability in formal axiomatic theories. These results, published by Kurt Gödel in 1931, are important both in mathematical logic and in philosophy of mathematics. The theorems are interpreted as showing that Hilbert's program to find a complete and consistent set of axioms for all mathematics is impossible.
Reading philosophy for the examples
Philosophy books often have really good examples. Here are a few of my favourites.
Iconic Math
As an introduction to a forthcoming book (late 2025), this page links to three narrated videos about unary boundary logic:
Welcome to Software Foundations in Coq
Sets for Mathematics in nLab
deduction system, natural deduction, sequent calculus, lambda-calculus, judgment
Which framework to learn?
As the title says, I have been exploring theory about AI agents and LLMs for a while and I want to learn frameworks and actually build some solid…
Recommended books for learning Dutch – Understanding Dutch
List of the best books for learning Dutch, regularly updated. Grammar workbooks, phrasebooks, and essential introductions to Dutch.
Crafting a good (reasoning) model
A recent talk I gave on model training, reasoning, and the next frontier.
Categorical logic and type theory
Categorical logic and type theory by Bart Jacobs, 1999, Elsevier Science edition, in English - 1st ed.
Build a Reasoning Model (From Scratch)
"An exceptional deep dive into the next frontier of AI.” —Aman Chadha, Google Build a Reasoning Model (From Scratch) is a practical guide to understanding how modern reasoning-oriented LLMs work by building their core methods step by step. The book tells a clear engineering story: start with a conventional pre-trained LLM, learn how text generation works, build reliable evaluation tools, improve reasoning through inference-time methods, then move into training-based approaches such as reinforcement learning and distillation. The progression is deliberate. Early chapters establish the baseline model and explain text generation, KV caching, and evaluation with math verifiers. The middle chapters show how reasoning can be improved without changing model weights, using chain-of-thought prompting, sampling, self-consistency, response scoring, and self-refinement. Later chapters move to changing the model itself through reinforcement learning with verifiable rewards, GRPO improvements, format rewards, and finally distillation from stronger reasoning models into smaller ones. The book is especially useful because it implements the core methods from scratch rather than treating them as black-box library calls. Readers see how self-consistency, self-refinement, Best-of-N, and training-based methods actually work, including their cost and latency trade-offs. It also discusses common failure modes, including cases where refinement can make answers worse. Difficult concepts such as softmax, temperature, and top-p sampling are clarified with code-linked explanations and diagrams, and visual workflows make pipelines and scoring methods easier to follow. Reading the book feels like following a guided technical build rather than a loose survey of AI topics. Each concept is introduced because the project now needs it. Diagrams, roadmaps, code listings, exercises, and repeated workflow summaries help readers stay oriented through advanced material. This structure reflects Sebastian Raschka’s professional strength: explaining complex machine learning topics by making every detail concrete and showing exactly where each section fits in the larger story. He does not treat mechanisms like evaluation, log-probabilities, KL regularization, or distillation as isolated abstractions; he connects them to the goal of making reasoning models understandable and implementable. Physically and organizationally, the book has eight chapters and seven substantial appendixes. That design keeps the main narrative focused while moving supporting material like references, exercise solutions, model source code, larger models, batching, evaluation alternatives, and chat interfaces into ordered appendixes. The result is a logically flowing book that remains hands-on, navigable, and technically deep without constantly interrupting the central build.
You Actually Do Need to Understand Mythos
Regnestykker
Sheets with calculus exercises suitable for kids that are learning the first basic arithmetic operations.