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Book Categories |
| Preface | ||
| 1 | Introduction | 1 |
| 2 | Supervised Learning | 7 |
| 3 | Single-Layer Networks | 15 |
| 4 | MLP Representational Capabilities | 31 |
| 5 | Back-Propagation | 49 |
| 6 | Learning Rate and Momentum | 71 |
| 7 | Weight-Initialization Techniques | 97 |
| 8 | The Error Surface | 113 |
| 9 | Faster Variations of Back-Propagation | 135 |
| 10 | Classical Optimization Techniques | 155 |
| 11 | Genetic Algorithms and Neural Networks | 185 |
| 12 | Constructive Methods | 197 |
| 13 | Pruning Algorithms | 219 |
| 14 | Factors Influencing Generalization | 239 |
| 15 | Generalization Prediction and Assessment | 257 |
| 16 | Heuristics for Improving Generalization | 265 |
| 17 | Effects of Training with Noisy Inputs | 277 |
| A | Linear Regression | 293 |
| B | Principal Components Analysis | 299 |
| C | Jitter Calculations | 311 |
| D | Sigmoid-like Nonlinear Functions | 315 |
| References | 319 | |
| Index | 339 |
| Price | Condition | Delivery | Seller | Action |
| $99.99 | Digital |
| WonderClub |
Eric Brause
reviewed Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks on June 26, 2019
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Most books about neural networks are silent when it comes to less obvious details of what training feedforward networks actually does, and what the result of the network represents. This book analyzes all these details by means of statistics, and consequently gives a very solid foundation for understanding neural networks. The definitive book for feedforward neural networks - extremely good! The math-heavy treatise makes it unsuitable for a beginners book, though.
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Add Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can g, Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks to the inventory that you are selling on WonderClubX
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Add Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can g, Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks to your collection on WonderClub |
