Fine-tuning Large Language Models (LLMs) | w/ Example Code
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Evaluating Object Detection Models Using Mean Average Precision - KDnuggets
Evaluating Object Detection Models Using Mean Average Precision - KDnuggets : In this article we will see see how precision and recall are used to calculate the Mean Average Precision (mAP).
Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall - KDnuggets
Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall - KDnuggets : This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated, and how they relate to evaluating deep learning models.
Working With The Lambda Layer in Keras - KDnuggets
Working With The Lambda Layer in Keras - KDnuggets : In this tutorial we'll cover how to use the Lambda layer in Keras to build, save, and load models which perform custom operations on your data. Originally published at Paperspace: https://blog.paperspace.com/working-with-the-lambda-layer-in-keras
From Y=X to Building a Complete Artificial Neural Network - KDnuggets
From Y=X to Building a Complete Artificial Neural Network - KDnuggets : In this tutorial, we will start with the most simple artificial neural network (ANN) and move to something much more complex. We begin by building a machine learning model with no parameters—which is Y=X.
Optimizing the Levenshtein Distance for Measuring Text Similarity - KDnuggets
Optimizing the Levenshtein Distance for Measuring Text Similarity - KDnuggets : For speeding up the calculation of the Levenshtein distance, this tutorial works on calculating using a vector rather than a matrix, which saves a lot of time. We’ll be coding in Java for this implementation.
A Guide to Preparing OpenCV for Android - KDnuggets
A Guide to Preparing OpenCV for Android - KDnuggets : This tutorial guides Android developers in preparing the popular library OpenCV for use. Using a step-by-step guide, the library will be imported into Android Studio and then can be used for performing any of the operations it supports, such as object detection, segmentation, tracking, and more.
Introduction to Federated Learning - KDnuggets
Introduction to Federated Learning - KDnuggets : Federated learning means enabling on-device training, model personalization, and more. Read more about it in this article.
Breaking Privacy in Federated Learning - KDnuggets
Breaking Privacy in Federated Learning - KDnuggets : Despite the benefits of federated learning, there are still ways of breaching a user’s privacy, even without sharing private data. In this article, we’ll review some research papers that discuss how federated learning includes this vulnerability.
Genetic Algorithm (PyGAD) Plays CoinTex Game
CoinTex is an open-source cross-platform multi-level adventure game developed in Python using Kivy. CoinTex is available for Android at Google Play: https://play.google.com/store/apps/details?id=coin.tex.cointexreactfast&hl=en To pass a level in CoinTex, the player has to collect all the randomly distributed coins while avoiding collision with the monsters and the fires. The monsters are moved randomly. Using only the genetic algorithm (GA) without any machine/deep learning algorithms, a game playing agent is created that plays CoinTex like a professional. The agent is able to stand even in complex levels with many coins, monsters, and fires. The GA is implemented using a Python 3 library named PyGAD. Find its documentation here to get started: https://pygad.readthedocs.io . Install PyGAD using pip: pip install pygad The source code of CoinTex at GitHub: https://github.com/ahmedfgad/CoinTex The source code of the genetic algorithm agent: https://github.com/ahmedfgad/CoinTe...