Machine Learning
portes grátis
Machine Learning
Theory and Practice
Kalita, Jugal
Taylor & Francis Ltd
12/2024
282
Mole
9780367433529
Pré-lançamento - envio 15 a 20 dias após a sua edição
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1. Introduction. 2. Regression. 3. Tree-Based Classi cation and Regression. 4. Arti cial Neural Networks. 5. Reinforcement Learning. 6. Unsupervised Learning. 7. Conclusions.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Pattern Recognition;Statistical Learning;Data Mining;Neural Networks;Iris Dataset;Elastic Net Regression;Unsupervised Machine Learning;Unlabeled Dataset;DBSCAN Algorithm;MNIST Dataset;Random Forests;Cumulative Rewards;Deep RL.;Machine Learning;Supervised Machine Learning;DBSCAN;Softmax Layer;Convolutional Layer;Dunn Index;Davies Bouldin Index;Lasso Regression;Gini Index;Inductive Bias;Ridge Regression;Tree Library;Batch Normalization;Bagged Decision Trees;Regression Model;Leaf Node
1. Introduction. 2. Regression. 3. Tree-Based Classi cation and Regression. 4. Arti cial Neural Networks. 5. Reinforcement Learning. 6. Unsupervised Learning. 7. Conclusions.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Pattern Recognition;Statistical Learning;Data Mining;Neural Networks;Iris Dataset;Elastic Net Regression;Unsupervised Machine Learning;Unlabeled Dataset;DBSCAN Algorithm;MNIST Dataset;Random Forests;Cumulative Rewards;Deep RL.;Machine Learning;Supervised Machine Learning;DBSCAN;Softmax Layer;Convolutional Layer;Dunn Index;Davies Bouldin Index;Lasso Regression;Gini Index;Inductive Bias;Ridge Regression;Tree Library;Batch Normalization;Bagged Decision Trees;Regression Model;Leaf Node
