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Book Categories |
| Introduction | ||
| Acknowledgments | ||
| 1 | Properties of Single Neurons | 1 |
| 2 | Synaptic Integration and Neuron Models | 37 |
| 3 | Essential Vector Operations | 63 |
| 4 | Lateral Inhibition and Sensory Processing | 85 |
| 5 | Simple Matrix Operations | 129 |
| 6 | The Linear Associator: Background and Foundations | 143 |
| 7 | The Linear Associator: Simulations | 175 |
| 8 | Early Network Models: The Perceptron | 209 |
| 9 | Gradient Descent Algorithms | 239 |
| 10 | Representation of Information | 281 |
| 11 | Applications of Simple Associators: Concept Formation and Object Motion | 351 |
| 12 | Energy and Neural Networks: Hopfield Networks and Boltzmann Machines | 401 |
| 13 | Nearest Neighbor Models | 433 |
| 14 | Adaptive Maps | 463 |
| 15 | The BSB Model: A Simple Nonlinear Autoassociative Neural Network | 493 |
| 16 | Associative Computation | 545 |
| 17 | Teaching Arithmetic to a Neural Network | 585 |
| Afterword | 629 | |
| Index | 631 |
| Price | Condition | Delivery | Seller | Action |
| $130.00 | Digital |
| WonderClub |
Josie Lancione
reviewed An Introduction to Neural Networks on October 22, 2016
An Introduction to Neural Networks
Definitely the neural networks textbook to choose if you want to learn about how bats' sonar works.
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