#tensor operations
413 related articles
·3 min
GPU Utilization at 100% Doesn't Mean Full Load: How to Truly Diagnose Training Bottlenecks
nvidia-smi showing 100% GPU utilization doesn't mean optimal training efficiency. Learn about DCGM, PyTorch Profiler, and MFU metrics for diagnosing real GPU training bottlenecks.
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·3 min
Open Secure AI Alliance Launched: Building AI Safety on an Open-Source Foundation
The Open Secure AI Alliance launches with NVIDIA and other tech giants, building AI agent security through open-source model weights, safety evaluations, and frontier research for industry-wide standards.
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·4 min
Getting Started with Machine Learning: How to Overcome Math Anxiety and Find a Pragmatic Learning Path
Scared off by math when starting ML? This article addresses beginners' math anxiety, clarifies how much linear algebra, calculus, and statistics you actually need, and provides a pragmatic top-down learning path with recommended resources.
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·4 min
Guide to Building a Localized Document Intelligence System: From Architecture Design to Model Selection
A detailed guide on building a localized document intelligence system to replace Azure Document Intelligence for offline document parsing, covering layout analysis, OCR engine selection, multimodal LLM deployment, and hybrid solution design.
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·2 min
Local GPU or Cloud for Machine Learning? A Purchasing Decision Guide
Should ML beginners buy a local GPU laptop or use cloud computing? This guide analyzes cloud platforms like Colab and Kaggle vs. gaming laptops, offering budget-friendly recommendations and hybrid strategies.
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·5 min
Career Planning for ML Security Engineers: A Complete Learning Path from Scratch
A systematic career development guide for ML security engineers covering math foundations, ML core skills, and cybersecurity — with project ideas and learning resources for aspiring AI security professionals.
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·2 min
Getting Started with Local AI Model Deployment: A Complete Guide from Downloading on Hugging Face to Running with llama.cpp
A beginner-friendly guide to local AI model deployment, covering secure model downloads from Hugging Face, running inference, exporting to GGUF format, and high-performance local execution with llama.cpp.
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·4 min
Q8_K_XL vs. MXFP4 Naming Debate: The Quantization Truth That FP8 ≠ Q8_0
Deep dive into LLM quantization formats Q8_K_XL vs MXFP4, explaining why FP8 ≠ Q8_0, debunking the "8-bit is lossless" myth for local deployment users.
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·3 min
Unsloth Now Supports AMD GPUs: 2x Training Speedup and 70% VRAM Savings Across 500+ Models
Unsloth officially supports AMD GPUs across RDNA 3-4, Strix Halo, and MI300 series, delivering 2x training speedup and 70% VRAM savings on 500+ models with RL and vLLM weight sharing support.
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·4 min
How to Choose Machine Learning Starter Tools? A Beginner's Development Environment Setup Guide
Confused about choosing between VS Code, Jupyter, Google Colab, and Anaconda for ML? This guide clarifies each tool's role and recommends a zero-cost beginner setup to help you start learning fast.
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·2 min
Supervision: A Model-Agnostic Engineering Toolkit for Computer Vision
Supervision is Roboflow's open-source CV toolkit offering model-agnostic detection visualization, object tracking, zone counting, and dataset format conversion to help developers build complete vision applications.
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·3 min
Laguna S 2.1 Release: How Multiple Deployment Options Are Reshaping AI Product Competition
Laguna S 2.1 launches with flexible deployment strategies supporting cloud API, on-premise, and managed services. Analysis of its deployment-first philosophy covering data sovereignty, cost control, and vendor lock-in.
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·3 min
persistent-inference: Solving TF/Keras Cold Start Problems with Just Two Files
Deep dive into the persistent-inference open-source project: solve TF/Keras cold start problems with just two files by keeping models resident in memory, eliminating reload overhead.
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·4 min
Introduction to Mathematical Foundations of Machine Learning: Definitions, Probabilistic Perspective & Core Framework Explained
Starting from Tom Mitchell's T-P-E framework, this guide explores ML's probabilistic perspective, random variables, and decision-making under uncertainty to build solid math foundations for ML.
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·3 min
Cursor Agent vs Codex: Which Should Visual AI Developers Choose?
In-depth comparison of Cursor Agent Window vs OpenAI Codex for Vision AI development, analyzing large task handling, multi-file edits, debugging, and long-running tasks to help developers decide.
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·3 min
Running a Small Language Model on an $8 Chip: Pushing the ESP32-S3 to Its Limits
Exploring training and running a small language model (SLM) on an ESP32-S3 microcontroller costing just $8. Learn about model design under extreme hardware constraints, quantization strategies, and edge AI's potential.
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·3 min
The Complete Machine Learning Learning Roadmap: From Anxiety to Clarity
Overwhelmed by machine learning? This practical ML roadmap breaks the journey into three phases—math basics, classical ML, and deep learning—with mindset tips and project strategies for engineers.
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·3 min
Running a Local AI Voice Assistant on a $50 Arduino Board: A Practical Guide to Edge AI
How to run a fully local AI voice agent on a $50 Arduino Uno Q board, covering speech recognition, intent understanding, and TTS implementation for edge AI applications.
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·3 min
Hadamard Transform: A Deep Dive into the Lightweight Deep Learning Technique That Replaces Multiplication with Addition and Subtraction
Deep dive into how the Hadamard Transform replaces matrix multiplication with only addition and subtraction for lightweight deep learning, covering FWHT principles, edge inference acceleration, and ultra-low-bit LLM quantization.
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·3 min
Weak at ML Programming? A Complete Path to Breakthrough Through Systematic Practice
A systematic coding practice path for ML practitioners who 'understand theory but can't implement,' covering math basics to deep learning components with Deep-ML platform guidance.
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