“Understanding Loss Functions and Optimization Algorithms in Artificial Neural Networks(ANNs)”
pub.towardsai.net·5h
λFunctional Programming
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Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach
arxiv.org·1d
🎯Reinforcement Learning
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Deep Learning Without Training
🔥PyTorch
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Hyper-Specific Sub-Field Selection: **Predictive Maintenance of Semiconductor Fabrication Equipment**
🧠Machine Learning
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Waterfall Methodology AI: The Smart Evolution of Traditional Project Management
💬Prompt Engineering
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Introducing Nested Learning: A new ML paradigm for continual learning
💬Prompt Engineering
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Normalized tensor train decomposition
arxiv.org·1d
🧮Embeddings
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Alleviating Hyperparameter-Tuning Burden in SVM Classifiers for Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization
arxiv.org·2d
🧠Machine Learning
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Accelerating MySQL Query Optimization via Reinforcement Learning & Hypergraph Analysis
🔍Query Optimization
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Taming Chaos: Predicting Unpredictable Systems Without Guesswork by Arvind Sundararajan
📊Dynamic Programming
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Energy Loss Functions for Physical Systems
arxiv.org·3d
📐Linear Algebra
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Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach
arxiv.org·2d
🕸️Graph Theory
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Topographical sparse mapping: A training framework for deep learning models
👁️Computer Vision
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Why Nonparametric Models Deserve a Second Look
towardsdatascience.com·2d
🧠Machine Learning
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