#tensor operations
369 related articles
Tutorials·3 min
PyTorch Beginner Tutorial: A Complete Guide to Tensor Operations and Neural Network Construction
A detailed PyTorch beginner guide covering tensor operations, dynamic computational graphs, GPU acceleration, and building your first neural network with nn.Module, with learning path recommendations and code examples.
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·4 min
AI Engineer Growth Roadmap: From Programming Fundamentals to RAG and MCP Agent Development
A systematic AI engineer learning roadmap covering programming, math, ML, and data engineering foundations, plus frontier AI technologies like LLM, RAG, Agents, and MCP with free open-source resources.
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·3 min
Self-Learning NLP: A Complete Path from Zero to Practical Application
A complete self-learning path for NLP covering fundamentals, Transformer concepts, hands-on projects, and tools like Hugging Face to help developers master NLP without returning to school.
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·4 min
RosaicLabs and Atom RTL Reveal Hidden Shifts in the x86 Ecosystem
Deep analysis of RosaicLabs, Intel Atom core RTL licensing, and 32-Tile AMX expansion — exploring x86 architecture's open licensing and customization transformation in the AI era.
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·3 min
Mathematical Foundations of Machine Learning: A Complete Guide from Mitchell's Definition to the Probabilistic Perspective
A deep dive into the mathematical foundations of ML, from Tom Mitchell's classic definition (Task T, Performance P, Experience E) to Bayesian decision theory and the probabilistic perspective.
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·3 min
Calibra v0.7.1: A Detailed Guide to Pre-Training Data Integrity Detection for Robot Learning
Calibra v0.7.1 introduces an integrity workflow to detect timestamp anomalies, motion jitter, camera defects, and incomplete episodes in robot learning data before training, supporting LeRobot, HDF5, and robomimic formats.
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·3 min
Introduction to Machine Learning: Core Concepts of Supervised Learning, Classification, and Featurization Explained
A systematic guide to core machine learning concepts including supervised learning as function mapping, classification characteristics, design matrices, and featurization for converting variable-length data.
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·2 min
Building Custom C/C++ Inference Engines: A Deep Dive into the Tradeoffs Between Performance, Control, and Engineering Cost
Deep analysis of why teams build custom C/C++ inference engines instead of using PyTorch or TensorRT, exploring performance, minimal dependencies, and long-term maintenance tradeoffs.
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·3 min
Control Theory Meets Machine Learning: A Learning Roadmap from Fundamentals to the Cutting Edge
A tailored ML guide for control theory learners covering reinforcement learning, data-driven control, Learning-based MPC, and a three-stage roadmap with practical advice.
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·3 min
Mechanically Strain-Induced Chirality: Reshaping Material Symmetry Through Physical Mechanics
Explore how mechanical strain breaks material symmetry to induce chiral structures. This article analyzes the physical mechanisms, advantages, and applications in programmable metamaterials, pharmaceuticals, and flexible electronics.
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·4 min
SELENE Open-Source Project: An Interactive Notebook Library for Systematically Learning AI and Machine Learning from Scratch
SELENE is an open-source AI learning resource built on Jupyter Notebooks, systematically covering ML, deep learning, Transformers, and LLMs with interactive code and math derivations for beginners.
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·3 min
Running the 1.56TB Kimi K3 Model on 8GB RAM with Pure C
A developer built a pure C99 inference engine that runs the 1.56TB Kimi K3 model on 8GB RAM using MoE sparsity and NVMe on-demand loading—no GPU, 176KB binary.
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·3 min
MI355X Running Kimi K3 Benchmark: The Logic Behind Outperforming B300 in Performance per Dollar
Deep analysis of AMD MI355X running Kimi K3 with superior cost-efficiency vs NVIDIA B300, and its implications for the AI inference hardware market.
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·3 min
Persistent State Machines: How INT4 Memory Cells Reshape LLM Attention Mechanisms
Exploring how persistent state machines with INT4-quantized memory cells reshape LLM attention, breaking KV Cache memory bottlenecks for long-context inference on edge devices and high-concurrency scenarios.
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·3 min
RearAware: A Local AI That Blurs Cat Butts Reveals the Dataset Dilemma
RearAware is a local AI Chrome extension that detects and blurs cat butts in video calls. This article analyzes its niche dataset challenges and explores solutions like augmentation, synthetic data, and transfer learning.
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·3 min
PyTorch vs TensorFlow: How Beginners Should Choose a Deep Learning Framework
Should deep learning beginners choose PyTorch or TensorFlow? This article compares both frameworks on research trends, ecosystem, and deployment, with practical switching advice.
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·2 min
BackdropKit: A Privacy-First Tool for Generating Launch Assets Locally in Your Browser
BackdropKit is a privacy-first local tool for creating launch assets. It supports screenshot beautification, demo videos, and data redaction—all running in your browser with no file uploads.
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·6 min
AI Coding Boosts Productivity 10x: Are Programmers Writing Their Own Pink Slips?
A senior developer admits 95% of work is done by Claude Code, with 10x productivity gains. From coding to architecture, AI is eroding programmers' core skill moats. Deep analysis of AI coding's impact on tech employment.
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·3 min
How Cheap Can It Be to Serve a 2.8 Trillion Parameter Model? A Deep Dive into Inference Economics
Deep analysis of the real cost of serving a 2.8 trillion parameter model. From MoE sparse activation to batching scale effects and inference optimization, revealing why model size and serving cost are less correlated than assumed.
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·3 min
You Don't Always Need a Vector Database for Vector Search — Brute Force Might Be All You Need
In-depth analysis of when brute force vector search beats vector databases. For RAG apps with under a few hundred thousand vectors, brute force offers exact recall, simpler architecture, and easier debugging.
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