| Titre : | Deep learning and neural networks |
| Titre de série : | Artificial intelligence for humans, 3 |
| Auteurs : | Jeff Heaton, Auteur |
| Type de document : | Monographie imprimée |
| Editeur : | St. Louis (Mo.) : Heaton Research, Inc., 2015 |
| ISBN/ISSN/EAN : | 978-1-5057-1434-0 |
| Format : | 1 vol. (XLIX-323 p.) / ill., couv. ill. en coul. / 24 cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Index. décimale : | 005.73 |
| Résumé : |
Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization. |
| Sommaire : |
Chapter 1: Neural Network Basics Chapter 2: Self Organizing Maps Chapter 3: Hopfield & Boltzmann Machines Chapter 4: Feedforward Neural Networks Chapter 5: Training & Evaluation Chapter 6: Backpropagation Training Chapter 7: Other Propagation Training Chapter 8: NEAT, CPPN and HyperNEAT Chapter 9: Deep Learning Chapter 10: Convolutional Neural Networks Chapter 11: Pruning and Model Selection Chapter 12: Dropout and Regularization Chapter 13: Time Series and Recurrent Networks Chapter 14: Architecting Neural Networks Chapter 15: Visualization Chapter 16: Modelling with Neural Networks |
| Type de document : | Livres |
Disponibilité (2)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| INF/732 | Livre | bibliothèque sciences exactes | Empruntable |
| INF/732 | Livre | bibliothèque sciences exactes | Empruntable |
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