#backpropagation alternative
13 related articles
·5 min
Multiway Turing Machines: A New Paradigm from Single Computation to Multiple Possibilities
An in-depth analysis of Wolfram's multiway Turing machines, exploring how computation expands from single paths to multiway graph structures, and deep connections to AI search algorithms and quantum computing.
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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.
Read more →
·5 min
Inside Cursor: How Recursive Model Improvement Enables AI to Self-Evolve
A Cursor ML engineer breaks down AI training methodology: outer/inner loop acceleration, preventing reward hacking, textual feedback, and recursive self-improvement (RSI) where models train the next generation.
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·2 min
Gradient-Free Evolutionary Model GENREG: How Radial Space Search Bypasses Backpropagation
GENREG-Radial Space is a gradient-free evolutionary optimization model that replaces backpropagation with structured radial space search. This article analyzes its core mechanisms, temporal evolution design, exploration-exploitation balance, and potential as a hybrid paradigm.
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·2 min
A Beginner's Guide to Grokking Machine Learning: Self-Study Paths and Resource Recommendations
Why Grokking Machine Learning is a top pick for ML beginners — covering the author, content, legal access options, and an effective self-study roadmap.
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·3 min
AI-Powered OSINT in Practice: A Learning Path and Tool Guide for CS Professionals
How can OSINT practitioners with a CS background automate intelligence with AI? This guide covers computer vision, VLMs, and Agent frameworks including YOLO, SAM, and Grounding DINO.
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·4 min
The Sprout Experiment: Exploring Deterministic Symbolic Reasoning AI Without Neural Networks
Sprout is a contrarian AI research experiment that abandons GPUs and neural networks in favor of deterministic symbolic reasoning. It features an auditable knowledge base and refuses to answer when evidence is insufficient, prioritizing explainability and governance.
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·7 min
The Agent Engineer Transition Roadmap: A Deep Dive into Three Core Skill Systems
Want to become an Agent engineer? This article systematically covers three core skill tracks—LLM fundamentals, LangChain architecture development, and enterprise deployment—to help you avoid detours.
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·6 min
Deep Dive into GPT-5.6 Ultra's Sub-Agent Architecture and Ten Key AI Industry Trends
In-depth analysis of GPT-5.6 Ultra's sub-agent collaborative reasoning, the global rise of Chinese AI models, world-model evaluation gaps, and AI's real-world deployment challenges and bubble warnings.
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·7 min
Building an AI Agent from Scratch: Implementing Core Conversational Agent Features in Python
Learn AI Agent development from scratch. This tutorial covers LLMs and prompts, then builds a conversational agent in Python using the DeepSeek API with multi-turn dialogue and system prompts.
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·3 min
AI Large Language Model Zero-to-Hero Systematic Tutorial: A Complete Guide from Basics to Practice
A comprehensive 748-episode AI LLM tutorial covering Transformer architecture, Prompt Engineering, RAG, Agent, fine-tuning, and enterprise projects like AI customer service and knowledge bases.
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