| Titre : |
Machine Learning with PyTorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python |
| Type de document : |
texte imprimé |
| Auteurs : |
Sebastian RASCHKA, Auteur ; Yuxi (Hayden) LIU, Auteur ; Vahid MIRJALILI, Auteur |
| Editeur : |
Birmingham : Packt Publishing |
| Année de publication : |
2022 |
| Importance : |
741 p. |
| ISBN/ISSN/EAN : |
978-1-80181-931-2 |
| Note générale : |
Foreword
Contributors
Table of Contents
Preface
Other Books You May Enjoy
Index |
| Catégories : |
Apprentissage automatique
Apprentissage par renforcement (intelligence artificielle)
Intelligence artificielle
Python (langage de programmation)
Réseaux neuronaux (informatique)
Scikit-Learn (logiciel)
|
| Index. décimale : |
006.3 Intelligence artificielle |
| Résumé : |
Giving Computers the Ability to Learn from Data
Training Simple Machine Learning Algorithms for Classification
A Tour of Machine Learning Classifiers Using Scikit-Learn
Building Good Training Datasets - Data Preprocessing
Compressing Data via Dimensionality Reduction
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Combining Different Models for Ensemble Learning
Applying Machine Learning to Sentiment Analysis
Predicting Continuous Target Variables with Regression Analysis
Working With Unlabeled Data - Clustering Analysis
Implementing a Multiplayer Artificial Neural Network from Scratch
Parallelizing Neural Network Training With PyTorch
Going Deeper - The Mechanics of PyTorch
Classifying Images with Deep Convolutional Neural Networks
Modeling Sequential Data Using Recurrent Neural Networks
Transformers - Improving Natural Language Processing with Attention Mechanisms
Generative Adversarial Networks for Synthesizing New Data
Graph Neural Networks for Capturing Dependencies in Graph Structured Data
Reinforcement Learning for Decision Making in Complex Environments
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