Cybersecurity has always had a dual-use problem: the same technical knowledge that helps defenders find vulnerabilities can also help attackers […]
Category: Deep Learning
A Coding Guide to Build a Production-Grade Background Task Processing System Using Huey with SQLite, Scheduling, Retries, Pipelines, and Concurrency Control
In this tutorial, we explore how to build a fully functional background task processing system using Huey directly, without relying […]
OpenAI Launches GPT-Rosalind: Its First Life Sciences AI Model Built to Accelerate Drug Discovery and Genomics Research
Drug discovery is one of the most expensive and time-consuming endeavors in human history. It takes roughly 10 to 15 […]
Building Transformer-Based NQS for Frustrated Spin Systems with NetKet
The intersection of many-body physics and deep learning has opened a new frontier: Neural Quantum States (NQS). While traditional methods […]
A Step-by-Step Coding Tutorial on NVIDIA PhysicsNeMo: Darcy Flow, FNOs, PINNs, Surrogate Models, and Inference Benchmarking
In this tutorial, we implement NVIDIA PhysicsNeMo on Colab and build a practical workflow for physics-informed machine learning. We start […]
Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput
Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or […]
How Knowledge Distillation Compresses Ensemble Intelligence into a Single Deployable AI Model
Complex prediction problems often lead to ensembles because combining multiple models improves accuracy by reducing variance and capturing diverse patterns. […]
Alibaba’s Tongyi Lab Releases VimRAG: a Multimodal RAG Framework that Uses a Memory Graph to Navigate Massive Visual Contexts
Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment […]
An End-to-End Coding Guide to NVIDIA KVPress for Long-Context LLM Inference, KV Cache Compression, and Memory-Efficient Generation
In this tutorial, we take a detailed, practical approach to exploring NVIDIA’s KVPress and understanding how it can make long-context […]
An Implementation Guide to Running NVIDIA Transformer Engine with Mixed Precision, FP8 Checks, Benchmarking, and Fallback Execution
In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision […]