Pytorch Tutorial: Learning to Rank скачать в хорошем качестве

Pytorch Tutorial: Learning to Rank

PyTorch Learning-to-Rank Tutorial In this comprehensive tutorial, we dive deep into building a learning-to-rank system using PyTorch. We'll explore the MSLR-WEB10k dataset, implement a neural ranking model, and evaluate it using various ranking metrics including NDCG and MAP. Follow along as we build, train, and analyze a complete ranking system from scratch. 📚 Topics Covered: • Loading and exploring the MSLR-WEB10k dataset • Building custom PyTorch datasets for ranking • Implementing neural ranking models • Understanding pairwise ranking loss functions • Evaluating with NDCG, MAP, and MRR metrics • Training loops and optimization strategies • Visualizing results and feature importance ⏱️ Timestamps: 00:00 PyTorch Learning-to-Rank Tutorial 01:07 Data Loading and Exploration 04:07 Data Preprocessing and PyTorch Dataset 05:19 Neural Ranking Model 06:30 Ranking Loss Functions 07:25 Evaluation Metrics 08:27 Training 10:10 Visualization and Analysis 11:09 Final Evaluation on Test Set 12:07 Example Rankings 13:20 Feature Importance Analysis 14:25 Summary and Conclusions #PyTorch #MachineLearning #LearningToRank #DeepLearning #InformationRetrieval #Tutorial #NeuralNetworks #RankingAlgorithms #DataScience