Troubleshooting Machine Learning Systems: Tips and Strategies
Dive into the world of diagnosing and debugging machine learning systems with insights on fixing learning algorithms, understanding model failures, and strategies for improvement. Explore the importance of data collection, feature selection, hyperparameter tuning, and more to enhance your system's p
0 views • 43 slides
Understanding Sources of Error in Machine Learning
This comprehensive overview covers key concepts in machine learning, such as sources of error, cross-validation, hyperparameter selection, generalization, bias-variance trade-off, and error components. By delving into the intricacies of bias, variance, underfitting, and overfitting, the material hel
0 views • 13 slides
Enhancing Student Success Prediction Using XGBoost
There is a growing concern about academic performance in higher education institutions. This project aims to predict student dropout and success using XGBoost, focusing on early identification of at-risk students to provide personalized support. Leveraging data from Polytechnic Institute of Portaleg
0 views • 13 slides
Practical Data Mining Evaluation Techniques
Data mining evaluation is crucial for determining the predictive power of machine learning models. Issues such as training, testing, and tuning are explored, along with techniques like holdout, cross-validation, and hyperparameter selection. The evaluation process assesses model performance, statist
0 views • 40 slides
Convolutional Neural Networks for Sentence Classification
Experiments show that a simple CNN with minimal hyperparameter tuning and static vectors achieves excellent results for sentence-level classification tasks. Fine-tuning task-specific vectors further improves performance. A dataset from Rotten Tomatoes is used for the experiments, showcasing results
0 views • 10 slides
Machine Learning Technique for Dynamic Aperture Computation in Circular Accelerators
This research presents a machine learning approach for computing the dynamic aperture of circular accelerators, crucial for ensuring stable particle motion. The study explores the use of Echo-state Networks, specifically Linear Readout and LSTM variations, to predict particle behavior in accelerator
0 views • 17 slides
Advancements in Machine Learning for Electron Density Prediction
Electron density is crucial for understanding atomic bonding. This research project explores using machine learning, specifically a Unet architecture, to predict electron density in a Lithium-Oxygen-Lithium system. The data set was generated by varying the positions of Lithium atoms and calculating
0 views • 8 slides