Beginning Deep Learning with Tensorflow

Dekorationsartikel gehören nicht zum Leistungsumfang.

Sprache: Englisch

59,30 €

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 4-7 Werktage

Kategorien:

Beschreibung

Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners.

Yoüll start with an introduction to AI, where yoüll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, yoüll jump into simple classification programs for hand-writing analysis. Once yoüve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, yoüll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs.

Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer!

What You'll Learn

Develop using deep learning algorithms

Build deep learning models using TensorFlow 2

Create classification systems and other, practical deep learning applications

Who This Book Is For

Students, programmers, and researchers with no experience in deep learning who want to build up their basic skillsets. Experienced machine learning programmers and engineers might also find value in updating their skills.

What You'll Learn

Develop using deep learning algorithms

Build deep learning models using TensorFlow 2

Create classification systems and other, practical deep learning applications

Who This Book Is For

Über den Autor

Liangqu Long is a well-known deep learning educator and engineer in China. He is a successfully published author in the topic area with years of experience in teaching machine learning concepts. His two online video tutorial courses "Deep Learning with PyTorch" and "Deep Learning with TensorFlow 2" have received massive positive comments and allowed him to refine his deep learning teaching methods.

Xiangming Zeng is an experienced data scientist and machine learning practitioner. He has over ten years of experience using machine learning and deep learning models to solve real world problems in both academia and professionally. Xiangming is familiar with deep learning fundamentals and mainstream machine learning libraries such as Tensorflow and scikit-learn.

Zusammenfassung

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

Inhaltsverzeichnis

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.

Details

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 Apress L.P.
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