Introduction to deep learning : from logical calculus to artificial intelligence /
"This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining...
Guardado en:
| Autor principal: | |
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| Formato: | Libro |
| Lenguaje: | Inglés |
| Publicado: |
Cham, Switzerland :
Springer,
c2018.
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| Colección: | Undergraduate topics in computer science
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| Materias: | |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 02889nam a2200325Ia 4500 | ||
|---|---|---|---|
| 001 | 99963133104151 | ||
| 005 | 20241030105359.0 | ||
| 008 | 240205s2018 sz a b 001 0 eng d | ||
| 020 | |a 3319730037 |q (paperback) | ||
| 020 | |a 9783319730035 |q (paperback) | ||
| 035 | |a (OCoLC)1419994677 | ||
| 035 | |a (OCoLC)on1419994677 | ||
| 040 | |a U@S |b spa |c U@S | ||
| 049 | |a U@SA | ||
| 050 | 4 | |a Q325.5 |b .S63 2018 | |
| 100 | 1 | |a Skansi, Sandro. | |
| 245 | 1 | 0 | |a Introduction to deep learning : |b from logical calculus to artificial intelligence / |c Sandro Skansi. |
| 260 | |a Cham, Switzerland : |b Springer, |c c2018. | ||
| 300 | |a xiii, 191 p. : |b il. ; |c 24 cm. | ||
| 490 | 1 | |a Undergraduate topics in computer science | |
| 504 | |a Incluye referencias bibliográficas e índice. | ||
| 505 | 0 | |a 1. From logic to cognitive science -- 2. Mathematical and computational prerequisites -- 3. Machine learning basics -- 4. Feedforward neural networks -- 5. Modifications and extensions to a feed-forward neural network -- 6. Convolutional neural networks -- 7. Recurrent neural networks -- 8. Autoencoders -- 9. Neural language models -- 10. An overview of different neural network architectures -- 11. Conclusion. | |
| 520 | |a "This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism." --Descripción del editor. | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Artificial intelligence |x Mathematics. | |
| 650 | 7 | |a Aprendizaje automático. |2 UDESA | |
| 650 | 7 | |a Redes neuronales (Computación) |2 UDESA | |
| 650 | 7 | |a Inteligencia artificial |x Matemáticas. |2 UDESA | |
| 830 | 0 | |a Undergraduate topics in computer science | |