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1. towardsdatascience.com › how-to-increase-the-accuracy-of-a-neural-network-9f5d1cImproving the Performance of a Neural Network

   towardsdatascience.com › how-to-increase-the-accuracy-of-a-neural-network-9f5d1c

   17 maj 2018 · There are some techniques to avoid overfitting: Regularisation of data (L1 or L2). Dropouts — Randomly dropping connections between neurons, forcing the network to find new paths and generalise. Early Stopping — Precipitates the training of the neural network, leading to reduction in error in the test set. Hyperparameter Tuning.

2. machinelearningmastery.com › improve-deep-How To Improve Deep Learning Performance

   machinelearningmastery.com › improve-deep-

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   6 sie 2019 · 1. Improve Performance With Data. You can get big wins with changes to your training data and problem definition. Perhaps even the biggest wins. Here’s a short list of what we’ll cover: Get More Data. Invent More Data. Rescale Your Data. Transform Your Data. Feature Selection. 1) Get More Data. Can you get more training data?

3. docs.ultralytics.com › guides › model-evaluation-insightsInsights on Model Evaluation and Fine-Tuning - Ultralytics

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   29 cze 2024 · By evaluating and fine-tuning your model, you can identify weaknesses, improve its accuracy, and boost overall performance. In this guide, we'll share insights on model evaluation and fine-tuning that'll make this step of a computer vision project more approachable.

4. medium.com › @rithpansanga › improving-precision-and-recall-in-machine-learningImproving Precision and Recall in Machine Learning: Tips and ......

   medium.com › @rithpansanga › improving-precision-and-recall-in-machine-learning

   4 sty 2023 · In conclusion, there are several ways to improve the precision and recall of a machine learning model. These include collecting more data, fine-tuning model hyperparameters, using a different...

5. docs.ultralytics.com › guides › yolo-performance-metricsYOLO Performance Metrics - Ultralytics YOLO Docs

   docs.ultralytics.com › guides › yolo-performance-metrics

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   12 lis 2023 · Performance metrics are key tools to evaluate the accuracy and efficiency of object detection models. They shed light on how effectively a model can identify and localize objects within images. Additionally, they help in understanding the model's handling of false positives and false negatives.

6. www.cudocompute.com › blog › accuracy-precision-recall-in-deep-learningAccuracy, precision and recall in deep learning - CUDO Compute

   www.cudocompute.com › blog › accuracy-precision-recall-in-deep-learning

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   1 lip 2024 · Accuracy, precision and recall in deep learning. Emmanuel Ohiri. Jul 1, 2024, 5:05 PM. Classification metrics quantitatively measure an Artificial Intelligence (AI) model's performance, highlighting its strengths and weaknesses, and help assess how well a deep learning model categorizes data into different classes.

7. towardsdatascience.com › choosing-performance-metrics-61b40819eae1Choosing Performance Metrics. Accuracy, recall, precision, F1...

   towardsdatascience.com › choosing-performance-metrics-61b40819eae1

   26 paź 2020 · Oct 26, 2020. -- classification_report from scikit-learn. Accuracy, recall, precision, F1 score––how do you choose a metric for judging model performance? And once you choose, do you want the macro average? Weighted average? For each of these metrics, I’ll look more closely at what it is and what its best use cases are.