#neural network from scratch
275 related articles
·2 min
A 14-Byte Neural Network Solves Mazes from Scratch: An Experiment in Extreme AI Compression
How can a 14-byte neural network solve 96.5% of unseen mazes? Explore extreme model compression, the relationship between model size and task complexity, and small models' potential in edge computing.
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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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·4 min
Neural Networks from Scratch: A Deep Dive into the Fundamentals from Linear Regression to Gradient Descent
From linear regression and logistic regression to gradient descent, this guide derives the core mechanisms of neural networks step by step, covering Sigmoid, cross-entropy, activation functions, and backpropagation.
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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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·2 min
TabPFN: A Transformer Model for Tabular Classification That Predicts in One Second Without Training
Deep dive into TabPFN's core principles and use cases. Built on Transformer architecture and in-context learning, TabPFN classifies small tabular data in one second without hyperparameter tuning, matching XGBoost accuracy.
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·2 min
Efficient Few-Shot Learning: Insights from Achieving 100% Accuracy with Just 16 Samples
Exploring the deep significance behind achieving 100% accuracy with just 16 samples, analyzing the critical role of data efficiency and stability in continuous learning systems.
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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
Symbio: Technical Analysis and Challenges of the AI Self Fine-Tuning Loop
In-depth analysis of Symbio's AI self fine-tuning loop mechanism, exploring the technical logic of self fine-tuning loops, personalization value, and challenges like catastrophic forgetting and model drift.
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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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·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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·2 min
The Accidental Architects of the Internet's Brain: How Technical Infrastructure Emerges from the Bottom Up
How the internet's core architecture was accidentally built by engineers solving specific problems—from TCP/IP to search engines to AI data infrastructure—revealing bottom-up emergence patterns.
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·2 min
Deep Dive into UT Austin's Online AI Master's Program: Course Intensity, Pacing, and Application Tips
Comprehensive analysis of UT Austin's online MSAI program covering course intensity, work-study balance tips, and application strategies based on real Reddit student feedback.
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·3 min
Reproducing GPT-2: How a Hidden Bug Affected Weight Training
An in-depth analysis of a hidden bug discovered while reproducing GPT-2 from scratch, revealing how implementation errors silently degrade weight quality and sharing practical debugging methodologies.
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·3 min
Gemini Robotics 2 Deep Dive: How Whole-Body Intelligence Is Reshaping the Future of Robotics
Deep dive into Google DeepMind's Gemini Robotics 2: how whole-body intelligence unifies perception, reasoning, and motor control, and the challenges of bringing embodied AI from lab to commercial deployment.
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·3 min
Gemini Robotics 2 Deep Dive: How Whole-Body Intelligence Is Reshaping the Future of Robotics
Deep dive into Google DeepMind's Gemini Robotics 2: how whole-body intelligence unifies perception, reasoning, and motor control, and the challenges from lab demos to commercial deployment.
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·4 min
Cracking TSP: A Closure Theory for Identifying Critical "Exception Edges"
Exploring a mathematically precise definition of "exception edges" in TSP, using closure problem theory to identify critical non-local edges that determine optimal solutions, providing verifiable structural priors for RL and NCO solvers.
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·3 min
Is a Linguistics-to-Computational Linguistics Master's Worth It? Career Prospects and Transition Advice
Is a linguistics-to-computational-linguistics master's worth it? This article analyzes career paths in computational linguistics in the AI era, the competitive advantages of a hybrid background, and practical advice for transitioning from humanities to NLP.
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·4 min
AI Agent Memory Systems: Genuine Technical Progress or RAG in Disguise?
In-depth analysis of AI agent memory systems: examining whether current improvements represent real progress or just RAG repackaged, and what architectural changes are truly needed.
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·2 min
SenseNova-Vision Training Data Pipeline Explained: A Guide to Fine-Tuning Unified Vision Models
SenseNova-Vision adds a complete training data pipeline with dataset registration, format converters, and end-to-end docs, making unified vision model fine-tuning for segmentation, OCR, and editing far more accessible.
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