Neural Network Design (2nd Edition)
This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules.
Neural Network Design (2nd Edition)
Nº de artículo: 45536327

Neural Network Design (2nd Edition)

Nº de artículo: 45536327

USD 50

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This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules.
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What Stands Out

Comprehensive Knowledge
Covers fundamental and advanced concepts of neural networks, catering to both novices and experts, ensuring a complete understanding of theory and practical applications.
Updated Techniques
Includes the latest advancements and methodologies in neural network design, offering readers an insight into current trends and challenges in the field.
Practical Examples
Features hands-on case studies and real-world applications, enhancing learning by demonstrating how theoretical concepts can be applied to solve actual problems.

Detalles de producto

Shop Neural Network Design (2nd Edition) online at a best price in Ecuador. 0971732116
Publisher Martin Hagan
Publication date September 1, 2014
Edition 2nd ed.
Language English
Print length 800 pages
ISBN-10 0971732116
ISBN-13 978-0971732117
Item Weight 2.98 pounds (1.35 kg)
Dimensions 7.5 x 1.81 x 9.25 inches (19.1 x 4.6 x 23.5 cm)

Who Should Buy?

Suitable For
  • Students

    Ideal for students seeking a comprehensive introduction to neural networks and their design principles in AI and machine learning.

  • Professionals

    Beneficial for professionals looking to enhance their understanding of neural network architectures and applications in industry contexts.

  • Researchers

    Suitable for researchers who need in-depth knowledge and references for advancing their work in neural network technologies.

Not Suitable For
  • Beginners

    Not suitable for absolute beginners with no prior experience in machine learning or neural network concepts.

DESCRIPCIÓN DEL PRODUCTO

Neural Network Design (2nd Edition)

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Neural Networks Editorial Review

**** The "Neural Network Design (2nd Edition)" book has garnered overwhelmingly positive feedback from readers, highlighting its effectiveness as a comprehensive educational resource in the realm of neural networks. Many reviewers noted that the book addresses common pitfalls encountered with other texts by providing clear explanations and a structured approach to learning. A standout feature of this textbook is its careful attention to mathematical foundations, presenting concepts in a way that is approachable even for those with minimal prior knowledge in these areas. Readers appreciate the wealth of worked examples and illustrations, which significantly aid understanding and retention of complex topics like multi-layer perceptrons and recurrent neural networks. This book appears to bridge the gap between novice and expert, making it a suitable recommendation for those new to the field. The linear progression through topics avoids the abrupt difficulty spikes often found in other literature, making learning a smoother experience. While some critics pointed out that the text does not cover convolutional neural networks, the book's authors apparently recognize this limitation as a natural stepping stone to advanced study, as learners will likely be well-prepared to tackle specialized content afterward. Reviewers also praised the book’s hands-on approach, suggesting that a hard copy of the book is preferable for note-taking and referencing, despite it being available for free online. Overall, "Neural Network Design" stands out as a meticulously crafted guide that demystifies neural networks through clear, methodical instruction, making it an invaluable addition to any aspiring data scientist's library. **

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ventajas

  • Clear explanations of complex topics
  • Comprehensive layout with many worked examples
  • Accessible to beginners with minimal math background
  • Linear progression through the material
  • Strong mathematical foundation provided

Contras

  • Does not cover convolutional neural networks

Product Price History

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