#learn PyTorch efficiently
98 related articles
·3 min
The Fundamental Conflict Between Backpropagation and Continual Learning: Can Catastrophic Forgetting Ever Be Truly Solved?
Exploring the fundamental conflict between backpropagation and continual learning, analyzing the roots of catastrophic forgetting, limitations of current solutions, and whether local learning or neuromorphic computing can offer true breakthroughs.
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·3 min
How PhD Students Can Use AI for Programming Without Losing Their Coding Skills: A Layered Delegation Strategy
How can PhD students avoid coding skill atrophy when using AI programming assistants? This article proposes a layered delegation strategy with actionable advice for researchers.
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·2 min
Qwen Scribe: A Deep Dive into the Local Speech Transcription Tool for Apple Silicon
Qwen Scribe is a local speech transcription tool optimized for Apple Silicon, running fully offline with Qwen models. Explore its technical features, privacy benefits, and comparison with Whisper.
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·2 min
Qwen Scribe: A Deep Dive into the Local Speech Transcription Tool for Apple Silicon
Qwen Scribe is a local speech transcription tool optimized for Apple Silicon, powered by Qwen models for fully offline use. Explore its technical features, privacy benefits, and comparison with Whisper.
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·2 min
Is Formal Language Theory Still Relevant in NLP? Deep Reflections Behind a Course Selection Dilemma
Formal Languages vs. Programming Language Principles—which course matters more for computational linguistics and NLP? A deep analysis from Chomsky Hierarchy to Lambda calculus to modern LLM theory.
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·4 min
A Guide to Full-Stack Machine Learning Project Repository Structure: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and externalized configuration to help ML developers move from experimental code to production-grade engineering standards.
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·4 min
Full-Stack Machine Learning Project Repository Structure Guide: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and configuration externalization to help ML developers move from experimental code to production-grade engineering.
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·3 min
The Real Daily Life of MLE and MLOps Engineers: Key Differences Between Three Roles and Career Paths
An in-depth look at the real daily work of data scientists, MLEs, and MLOps engineers — covering responsibilities, essential tools, and career paths to help you find your direction in AI.
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·3 min
A Beginner's Guide to AI Agents: Two Learning Paths for Developers and Practitioners
How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.
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·2 min
6 High-Paying AI Career Paths: A Beginner's Shortcut and Salary Guide
A breakdown of the 6 best high-paying AI career paths for beginners: LLM application development, AI agents, computer vision, AI infrastructure, AIGC, and embodied AI—with salary ranges, core skills, and who they suit.
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·6 min
AI Application Engineer Interview Guide: Core Topics in Model Quantization and Inference Deployment
A systematic review of must-know topics for AI Application Engineer interviews: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
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·6 min
AI Application Engineer Interview Guide: Core Topics on Model Quantization and Inference Deployment
A systematic guide to must-know AI application engineer interview topics: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
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·5 min
China Weighs Restricting Top AI Model Exports as DeepSeek Develops Its Own Inference Chip
China's Commerce Ministry weighs restricting overseas access to top open-weight AI models from Alibaba, ByteDance, and DeepSeek, while DeepSeek develops its own inference chip amid a US-China AI export standoff.
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·5 min
China Weighs Restricting Top AI Model Exports, While DeepSeek Quietly Builds Its Own Inference Chip
China's Ministry of Commerce is weighing restrictions on overseas access to top open-weight AI models from Alibaba, ByteDance, and DeepSeek. Meanwhile, DeepSeek is quietly building its own inference chip and raising ~$7B in first external funding.
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AstrBot: A Deep Dive into the Multi-Pl…
·2 min
AstrBot: A Deep Dive into the Multi-Platform AI Agent Development Framework
AstrBot is an open-source AI Agent framework supporting WeChat, QQ, Telegram and more, with multi-LLM compatibility and plugin extensibility. Full technical breakdown inside.
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·3 min
Gradient Descent and Backpropagation Explained: How Neural Networks Learn from Scratch
Understand how neural networks learn: a complete guide to cost functions, gradient descent, backpropagation, and SGD — ideal for deep learning beginners building intuition from the ground up.
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·2 min
Build From Scratch or Just Use sklearn? A Complete ML Engineer Learning Path Guide
Should you implement ML algorithms from scratch or just use sklearn? This guide breaks down the optimal learning path for ML engineers by career stage and company type.
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·2 min
HF Viewer: Free Interactive Visualization Tool for Exploring 2,300+ Open-Source AI Model Architectures
HF Viewer is a free interactive tool for visualizing 2,300+ open-source AI model architectures. Explore Transformers and more via graph nodes, animations, and paper links.
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·2 min
AI Large Model Learning Roadmap: A Complete Five-Stage Guide from Zero to Independent Deployment
A complete 5-stage AI large model learning roadmap — from Python basics and prompt engineering to RAG pipelines, Agent development, and private model deployment.
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LFortran + Enzyme: A New Path to Autom…
·2 min
LFortran + Enzyme: A New Path to Automatic Differentiation for Fortran Code
LFortran + Enzyme enables automatic differentiation for decades of Fortran scientific code without rewrites. Learn the technical principles, implementation path, and impact on scientific ML.
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