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DAVE, MonkeyOCRv2, and DocPO: A Survey of Document VLM Training
Executive summary DAVE, MonkeyOCRv2, and DocPO address different stages of a document vision language model VLM lifecycle rather than competing as three interchangeable training recipes. Method Primary stage Main contribution Best use case DAVE Vision encoder pretraining and multi decoder alignment…
Continue reading →Uncertainty in Deep Learning: Deep Ensembles and MC Dropout
Why uncertainty matters A neural network can be confidently wrong. A softmax score of 0.99 only says that one logit is much larger than the others; it does not prove that the input resembles the training data or that the prediction is reliable. This distinction matters whenever a prediction drives a…
Continue reading →Navier–Stokes Equations: From Conservation Laws to Fluid Flow
Why these equations matter The Navier–Stokes equations describe how the velocity, pressure, density, and temperature of a fluid evolve. They sit behind weather prediction, aircraft design, blood flow simulation, ocean circulation, combustion, and the movement of water through a pipe. Their ingredien…
Continue reading →Data Studio Architecture and Data Storage Specification
Production AI data platforms need more than a file upload endpoint. This specification defines a complete, reproducible path from Hugging Face compatible repositories to immutable revisions, transactional metadata, content addressed storage, columnar indexes, and revision scoped serving. Live demo T…
Continue reading →An Introduction to 3D Reconstruction: From Images to Geometry
An Introduction to 3D Reconstruction A photograph records how a three dimensional scene looks from one position. 3D reconstruction tries to reverse that projection: given one or more observations, estimate the shape, position, and sometimes the appearance of the objects that produced them. This prob…
Continue reading →Q-Learning from Scratch: Solving a Grid World with Python
Introduction Most machine learning models learn from labeled examples. Reinforcement learning is different: an agent interacts with an environment , observes the consequences of its actions, and learns which decisions produce the largest long term reward. In this tutorial, we will implement Q learni…
Continue reading →Bayesian Inference: Priors, Likelihoods, and Decisions
Why Bayesian inference matters Deterministic pipelines often fall apart when the data distribution shifts or the amount of evidence changes. Bayesian inference keeps a full probability distribution over uncertain quantities, so you can update beliefs as new observations arrive and keep downstream de…
Continue reading →Intelligent Document Processing: Benchmarking
Benchmarking Document AI: OmniDocBench, PubTabNet, CISOL, TabRecSet Document AI has exploded in capability over the past two years, but reproducing published claims still hinges on solid evaluation practices. I condensed my benchmark research notes into this guide so it’s easier to see which dataset…
Continue reading →AI Art Tutorial: High quality image generation
AI Art Tutorial: High quality image generation with ComfyUI In the ever evolving landscape of artificial intelligence, tools like ComfyUI are empowering artists and enthusiasts to create breathtaking, one of a kind images. By leveraging cutting edge models and a user friendly interface, ComfyUI turn…
Continue reading →Build a ChatGPT-like chatbot for free with Ollama and Open WebUI
Introduction Has the power of ChatGPT led you to explore large language models LLMs and want to build a ChatGPT like chatbot app? Do you want to create a chatbot with your own personal touch? Do you want to deploy a chatbot tool for your team at work to support daily tasks? This post shows you how t…
Continue reading →Linear Regression: Foundations, Estimation, and Diagnostics
Linear Regression: Foundations, Estimation, and Diagnostics Linear regression is a fundamental method for modeling the relationship between a continuous response variable and one or more explanatory variables. It is widely used for prediction, estimation, hypothesis testing, and the analysis of rela…
Continue reading →Hàm sigmoid dưới góc nhìn xác suất
Trong bài toán phân loại nhị phân, mô hình thường tạo ra một điểm số thực $z in mathbb R $. Tuy nhiên, một số thực bất kỳ chưa thể được diễn giải trực tiếp như xác suất. Hàm sigmoid giải quyết vấn đề này bằng cách ánh xạ $z$ vào khoảng $ 0, 1 $: $$ sigma z = frac 1 1 + e^ z . $$ Nếu đặt $$ P y=1 mid…
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