In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision […]
Category: Deep Learning
Defeating the ‘Token Tax’: How Google Gemma 4, NVIDIA, and OpenClaw are Revolutionizing Local Agentic AI: From RTX Desktops to DGX Spark
Run Google’s latest omni-capable open models faster on NVIDIA RTX AI PCs, from NVIDIA Jetson Orin Nano, GeForce RTX desktops […]
Meta Releases TRIBE v2: A Brain Encoding Model That Predicts fMRI Responses Across Video, Audio, and Text Stimuli
Neuroscience has long been a field of divide and conquer. Researchers typically map specific cognitive functions to isolated brain regions—like […]
Meet SymTorch: A PyTorch Library that Translates Deep Learning Models into Human-Readable Equations
Can symbolic regression be the key to transforming opaque deep learning models into interpretable, closed-form mathematical equations? or Say you […]
How to Design Complex Deep Learning Tensor Pipelines Using Einops with Vision, Attention, and Multimodal Examples
In this tutorial, we walk through advanced usage of Einops to express complex tensor transformations in a clear, readable, and […]
Microsoft AI Proposes OrbitalBrain: Enabling Distributed Machine Learning in Space with Inter-Satellite Links and Constellation-Aware Resource Optimization Strategies
Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground […]
How Tree-KG Enables Hierarchical Knowledge Graphs for Contextual Navigation and Explainable Multi-Hop Reasoning Beyond Traditional RAG
In this tutorial, we implement Tree-KG, an advanced hierarchical knowledge graph system that goes beyond traditional retrieval-augmented generation by combining […]
A Coding Guide to Demonstrate Targeted Data Poisoning Attacks in Deep Learning by Label Flipping on CIFAR-10 with PyTorch
In this tutorial, we demonstrate a realistic data poisoning attack by manipulating labels in the CIFAR-10 dataset and observing its […]
Meet ‘kvcached’: A Machine Learning Library to Enable Virtualized, Elastic KV Cache for LLM Serving on Shared GPUs
Large language model serving often wastes GPU memory because engines pre-reserve large static KV cache regions per model, even when […]
Microsoft Research Releases Skala: a Deep-Learning Exchange–Correlation Functional Targeting Hybrid-Level Accuracy at Semi-Local Cost
TL;DR: Skala is a deep-learning exchange–correlation functional for Kohn–Sham Density Functional Theory (DFT) that targets hybrid-level accuracy at semi-local cost, […]