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Build deep learning models using TensorFlow 2
Create classification systems and other, practical deep learning applications
Build deep learning models using TensorFlow 2
Create classification systems and other, practical deep learning applications
Follow along with hands-on coding to discover deep learning from scratch
Tackle different neural network models using the latest frameworks
Take advantage of years of online research to learn TensorFlow 2 efficiently
Chapter 1: Introduction to Artificial Intelligence.- Chapter 2. Regression.- Chapter 3. Classification.- Chapter 4. Basic Tensorflow.- Chapter 5. Advanced Tensorflow.- Chapter 6. Neural Network.- Chapter 7. Backward Propagation Algorithm.- Chapter 8. Keras Advanced API.- Chapter 9. Overfitting.- Chapter 10. Convolutional Neural Networks.- Chapter 11. Recurrent Neural Network.- Chapter 12. Autoencoder.- Chapter 13. Generative Adversarial Network (GAN).- Chapter 14. Reinforcement Learning.- Chapter 15. Custom Dataset.
Erscheinungsjahr: | 2022 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: |
xxiii
713 S. 323 s/w Illustr. 713 p. 323 illus. |
ISBN-13: | 9781484279144 |
ISBN-10: | 148427914X |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: |
Long, Liangqu
Zeng, Xiangming |
Hersteller: | APRESS |
Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
Maße: | 235 x 155 x 39 mm |
Von/Mit: | Liangqu Long (u. a.) |
Erscheinungsdatum: | 28.01.2022 |
Gewicht: | 1,101 kg |
Follow along with hands-on coding to discover deep learning from scratch
Tackle different neural network models using the latest frameworks
Take advantage of years of online research to learn TensorFlow 2 efficiently
Chapter 1: Introduction to Artificial Intelligence.- Chapter 2. Regression.- Chapter 3. Classification.- Chapter 4. Basic Tensorflow.- Chapter 5. Advanced Tensorflow.- Chapter 6. Neural Network.- Chapter 7. Backward Propagation Algorithm.- Chapter 8. Keras Advanced API.- Chapter 9. Overfitting.- Chapter 10. Convolutional Neural Networks.- Chapter 11. Recurrent Neural Network.- Chapter 12. Autoencoder.- Chapter 13. Generative Adversarial Network (GAN).- Chapter 14. Reinforcement Learning.- Chapter 15. Custom Dataset.
Erscheinungsjahr: | 2022 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: |
xxiii
713 S. 323 s/w Illustr. 713 p. 323 illus. |
ISBN-13: | 9781484279144 |
ISBN-10: | 148427914X |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: |
Long, Liangqu
Zeng, Xiangming |
Hersteller: | APRESS |
Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
Maße: | 235 x 155 x 39 mm |
Von/Mit: | Liangqu Long (u. a.) |
Erscheinungsdatum: | 28.01.2022 |
Gewicht: | 1,101 kg |