We deliver AI algorithm tutorials and the latest news, curated by a team of researchers from Peking University, Shanghai Jiao Tong University, Central South University, and leading AI companies such as Huawei, Kuaishou, and SenseTime. Join us in the AI alchemy—making life better!
This article walks through using the UNETR transformer architecture to segment 3D brain MRI scans from the BRATS dataset, detailing environment setup, data preprocessing with MONAI, model construction, training with DiceCE loss, validation metrics, and visualizing the best‑performing model outputs.
This article explains how mixing a Poisson process with a discrete k‑state hidden Markov model creates a Poisson HMM that captures autocorrelation in integer‑valued time‑series, detailing the model formulation, prediction via expectation over states, and parameter estimation using MLE or EM.
The article explains why monitoring production ML models is essential to detect data and target drift, describes the open‑source Evidently AI library and its statistical tests, and demonstrates its use on a weather‑forecast example and a plant‑seedling image classification case, including dashboards, code snippets, and visual analysis of drift impact.
This article walks through constructing a learnable chess AI by integrating AlphaZero‑style Monte Carlo Tree Search with a decoder‑only Transformer, detailing the game tree logic, model architecture, input and output encodings, self‑play training loop, and code implementation in PyTorch.
This article walks through building a large‑scale semantic search system on the English Wikipedia using the Weaviate vector database, GraphQL queries, and pre‑trained Sentence‑BERT and BERT Q&A models, covering dataset preparation, schema design, import pipelines, query examples, and production deployment strategies.
This article explains the core concepts of ensemble learning, covering the bias‑variance trade‑off, the mechanics of bagging with bootstrap and random forests, the sequential strategies of boosting (AdaBoost and gradient boosting), and the heterogeneous stacking framework with meta‑models and multi‑layer extensions.
This article explains practical techniques for improving NLP model accuracy on massive corpora, covering challenges of multi‑field text, word‑embedding choices, a fasttext‑based regression demo with book‑review data, feature engineering tricks, and a comparison with tf‑idf + LASSO.
Lightning Flash 0.3 expands the PyTorch Lightning ecosystem with eight new computer‑vision and NLP tasks, modular API design, integrated model hubs, visualisation callbacks, customizable data‑source hooks, and a central registry for model backbones, all illustrated with concrete code examples.
The article analyzes how Nimble reduces GPU scheduling overhead and enables parallel execution through ahead‑of‑time scheduling and automatic multi‑stream assignment, achieving up to 22.3× inference speedup over PyTorch and significantly improving GPU utilization for deep learning workloads.
This article explains how GridSearchCV automates hyperparameter tuning for machine‑learning models, demonstrates its use with a RandomForest classifier on the breast‑cancer dataset—including code, cross‑validation, best‑parameter results, and discusses its advantages and scalability limits.
