#regularization
86 related articles
Research·3 min
MixupMP: How Data Augmentation Fixes the Uncertainty Quantification Flaws of Deep Ensembles
Deep dive into AISTATS 2024 paper MixupMP: revealing Deep Ensembles' fundamental UQ flaws and fixing them via Mixup augmentation and Martingale Posterior framework for better calibration and OOD detection.
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Deep Dives·1 min
Neural Networks for Beginners: From Fundamentals to Handwritten Digit Recognition
Understand neural networks from scratch. Learn input layers, hidden layers, forward propagation, backpropagation, gradient descent, with a handwritten digit recognition example.
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Tutorials·2 min
Python Machine Learning in Practice: A Complete Guide from Algorithm Derivation to Code Implementation
A systematic Python ML course using a 3-stage framework: algorithm derivation, code implementation, and experimental analysis. Covers logistic regression, decision trees, and ensemble learning.
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Product Reviews·1 min
DeepSeek V4 vs Qwen3.6 Real-World Testing: In-Depth Comparison Across 8 Categories and 150 Scenarios
BenchLocal real-world testing of DeepSeek V4 Pro, V4 Flash vs Qwen3.6 27B across 8 categories and 85 scenarios. V4 Pro leads by 6% but stumbles on math reasoning. Qwen3.6 Q6 rivals V4 Pro in agent tasks.
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Product Reviews·4 min
Cube Studio Deep Dive: Tencent's Open-Source One-Stop Cloud-Native AI Platform
Deep dive into Tencent's open-source AI platform Cube Studio, covering distributed training, LLM fine-tuning, inference deployment, VGPU virtualization, and domestic hardware support for enterprise MLOps.
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Tutorials·3 min
ComfyUI LoRA Training Tutorial: TrainTools-MZ Plugin Turns Model Training into Node-Based Visual Workflow
ComfyUI-TrainTools-MZ is a ComfyUI training node plugin based on kohya-ss/sd-scripts, enabling LoRA fine-tuning directly in the node editor. This guide covers installation, configuration, and usage.
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