Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Search Algorithms and Loss Functions for Bayesian Clustering by David B. Dahl (11761055)

“…<p>We propose a randomized greedy search algorithm to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. …”

APSO–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

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APSO–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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Loss function values of target detection algorithms during training. by Guodong Su (7876934)

“…<p>Loss function values of target detection algorithms during training.…”

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

APSO–WOA–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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APSO–WOA–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

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Changes in loss values for different algorithms. by Xiaojun Wang (50332)

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Comparison of the curves of the DCIOU loss function and the CIOU loss function. by Xunqian Xu (21261899)

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Comparison results of the loss functions. by Jia Li (160557)

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Loss function variation curve. by Chunhua Yang (346871)

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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

APSO–WOA–CNN and APSO–CNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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APSO–WOA–CNN and APSO–CNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

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<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

The losses of the training process before and after the optimization of loss function. by Peihan Wen (6324315)

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Recursive attention function and loss function change. by Wang Linfeng (17102068)

Loss Function Comparison. by Hongwei Yue (574068)

Loss Function Comparison. by Hongwei Yue (574068)