Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability 1st Edition
genuine image
Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability 1st Edition
Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing.
Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability 1st Edition
Nº de artículo: 31410831

Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability 1st Edition

88% of respondents would recommend this to a friend

Nº de artículo: 31410831

HNL 1667

Price Details

Excluding Shipping & Custom charges ( Shipping and custom charges will be calculated on checkout )

*All items will import from Estados Unidos

En stock
Estados Unidos Importado de la tienda USA

QTY:

Haz tu pedido ahora y recíbelo por ahí Viernes, Octubre 09
Secured transactionsecured transaction

Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.

Our Top Logistics Partners
  • fedex
  • dhl
Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing.
Garantía U-Care:
Ninguno
Selecciona un plan
fast shipping

Fast
Shipping

free return

Free
Return*

secure packaging

Secure Packaging

100% original products

100% Original Products

pci-dss

PCI DSS Compliance

iso certified

ISO 27001 Certified

paypal payment
visa payment
mastercard payment

What Stands Out

Comprehensive Guide
This book provides a complete introduction to probabilistic deep learning, integrating theory with practical applications, making it suitable for beginners and practitioners alike.
Hands-On Python
Utilizes Python, Keras, and TensorFlow Probability, offering practical coding examples that empower readers to implement probabilistic models in their own projects effortlessly.
Expert Insights
Written by knowledgeable authors, the book includes expert insights and best practices, ensuring readers receive reliable information to enhance their understanding and skills in this advanced field.

Detalles de producto

Explore the world of TensorFlow Probability and Keras with Python. Get the 1st edition of Probabilistic Deep Learning book from Ubuy Honduras and master the latest techniques.
Publisher Manning
Publication date November 10, 2020
Edition 1st
Language English
Print length 296 pages
ISBN-10 1617296074
ISBN-13 978-1617296079
Item Weight 7.4 ounces (209.79 grams)
Dimensions 7.38 x 0.3 x 9.25 inches (18.7 x 0.8 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Perfect for data scientists seeking to incorporate probabilistic models into their analyses using Python libraries like Keras.

  • Machine Learning Engineers

    Ideal for engineers who want to develop robust machine learning models applying probabilistic approaches in TensorFlow.

  • Students & Educators

    Great resource for students and educators aiming to understand probabilistic deep learning concepts and their practical applications.

Not Suitable For
  • Beginner Programmers

    Not suitable for beginners lacking foundational programming skills and understanding of machine learning principles.

DESCRIPCIÓN DEL PRODUCTO

Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability 1st Edition

¿Tienes alguna consulta? Chatea con nosotros

Preguntas y respuestas de los clientes

  • Pregunta: What is the focus of 'Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability'?

    Respuesta: 'Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability' primarily focuses on how to incorporate probabilistic modeling into deep learning. This approach provides a framework for better uncertainty quantification in predictions. By blending probabilistic methods with deep learning techniques, the book teaches readers how to apply these concepts using popular platforms like Keras and TensorFlow Probability. This is especially useful for scenarios where data is sparse or noisy, allowing practitioners to make more robust predictions and assess their confidence.
  • Pregunta: Who is the target audience for this book?

    Respuesta: The book is tailored for data scientists, machine learning engineers, and researchers who have a foundational understanding of deep learning. It is particularly beneficial for those who wish to enhance their knowledge of probabilistic techniques in the context of neural networks. Additionally, practitioners looking to implement these methods in real-world projects using Python, Keras, and TensorFlow will find the content highly applicable, enabling them to tackle complex data challenges.
  • Pregunta: What tools and libraries are covered in this book?

    Respuesta: 'Probabilistic Deep Learning' emphasizes the use of Python along with specific libraries such as Keras and TensorFlow Probability. Keras serves as an accessible interface for building neural networks, while TensorFlow Probability extends this with tools for probabilistic reasoning. Readers can expect to learn how to leverage these libraries for tasks such as Bayesian neural networks and variational inference, which can enhance models with uncertainty capabilities, beneficial for fields like healthcare or finance.
  • Pregunta: Are there practical examples included in the book?

    Respuesta: Yes, the book is rich with practical examples and case studies. Each chapter typically includes code snippets and detailed explanations that guide readers through implementing techniques using real datasets. For instance, you might explore how to build a probabilistic model for predicting customer retention in e-commerce, blending theory with practical coding exercises to solidify your understanding and application of the concepts.
  • Pregunta: How does this book handle uncertainty in machine learning?

    Respuesta: The book addresses uncertainty by integrating probabilistic modeling into deep learning paradigms. Readers will learn how to implement models that not only output predictions but also estimate the confidence of those predictions. This approach is crucial in domains such as autonomous driving or medical diagnoses, where understanding the level of uncertainty can significantly influence decision-making processes.
  • Pregunta: Is prior knowledge of deep learning necessary to read this book?

    Respuesta: A foundational understanding of deep learning is strongly recommended for readers. Since the book covers advanced topics in probabilistic deep learning, familiarity with basic concepts such as neural networks, loss functions, and gradient descent will facilitate comprehension. However, the book often revisits key principles to help reinforce learning, making it potentially accessible for motivated beginners willing to engage with introductory materials first.
  • Pregunta: What is the relationship between probability and machine learning as discussed in the book?

    Respuesta: The book illustrates that probability forms the bedrock of many machine learning algorithms by enabling the modeling of uncertainty and variability in data. According to the book, integrating probabilistic frameworks allows practitioners to create better decision-making systems that account for the inherent unpredictability of real-world scenarios. For example, industries such as finance and robotics face uncertainty, and utilizing these probabilistic methods can enhance predictive accuracy and reliability.
  • Pregunta: Can the concepts learned in this book be applied to real-world problems?

    Respuesta: Absolutely! The concepts explored in 'Probabilistic Deep Learning' are highly applicable to various real-world problems, including finance, healthcare, and robotics. Understanding how to model uncertainty can lead to superior decision-making frameworks in autonomous systems or risk assessment in financial applications. By learning how to harness the power of probabilistic deep learning, professionals can tackle challenges that require nuanced insights beyond simple predictions.
  • Pregunta: What differentiates this book from other deep learning literature?

    Respuesta: 'Probabilistic Deep Learning' uniquely accomplishes the fusion of deep learning with probabilistic modeling, which is often oversimplified in traditional deep learning texts. This book provides in-depth insight into uncertainty quantification and probabilistic reasoning—areas that are becoming increasingly relevant as machine learning applications grow in complexity. The focus on TensorFlow Probability further distinguishes it, offering practical coding guidance not often found in standard resources.
  • Pregunta: Where can I buy 'Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability 1st Edition' in Honduras?

    Respuesta: You can purchase 'Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability 1st Edition' on Ubuy. Ubuy offers a wide selection of books and other products, making it a reliable choice for acquiring this title. Simply visit their website and search for the book to find it conveniently.

Intelligence & Semantics Editorial Review

**** "Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability" offers an intriguing journey into the realm of deep learning with a particular focus on its probabilistic elements. Customers have lauded the authors for their ability to present complex concepts in a friendly and approachable manner. Reviewers express appreciation for the clarity with which topics such as maximum likelihood estimation using neural networks, Bayesian neural networks, and normalizing flows are articulated. This unique perspective aids readers in grasping advanced ideas that might otherwise seem daunting. While the book starts with a basic introduction to deep learning and TensorFlow—a section that some readers found somewhat light—the second half delves into more substantial and useful content. However, several reviews indicate that this section still lacks the depth some readers may desire. Despite the engaging delivery of information, there is a notable concern regarding the quality of editing throughout the text. Multiple reviewers have pointed out errors on almost every page, detracting from the overall reading experience. In conclusion, while "Probabilistic Deep Learning" presents an accessible introduction to advanced concepts in the field, readers should be prepared for issues with editing and superficial coverage of some topics. **

Customer Reviews & Ratings

1 valoraciones de los clientes
  • 5 estrella
    100%
  • 4 estrella
    0%
  • 3 estrella
    0%
  • 2 estrella
    0%
  • 1 estrella
    0%

Revisar este producto

Comparte tus ideas con otros clientes

ventajas

  • Friendly and approachable writing style
  • Clear explanations of complex concepts
  • Unique perspective on probabilistic deep learning techniques
  • Potential applications of topics discussed, particularly normalizing flows

Contras

  • Basic introductory content may not satisfy advanced readers

Platform Trust & Buyer Confidence

trustpilot logo
4.2/5 9477 reseñas
Read reviews
JK
Jasmin
Verified buyer

“Great products and very good service: very easy and very fast international delivery.”

9 September 2026 · via Trustpilot
AG
Anke
Verified buyer

“Wonderful online shopping experience, smooth transaction from the start. Payment method works conveniently and delivery is unexpectedly fast and reliable. You go the extra mile for service. What makes this even more amazing, you deliver to Namibia. I will remain a happy Ubuy customer and will increase my purchases for sure! Thank you!”

10 September 2026 · via Trustpilot
H
Hazel
Verified buyer

“Very easy to find the products what you need, and so fast delivery, that’s why I highly recommended to others costumers to used ubuy.”

10 September 2026 · via Trustpilot
O
Opaleye
Verified buyer

“I received exactly what I ordered I was skeptical about your site because that was my first time to order. But the order came timely and neatly packaged. I was not disappointed. Thank you.”

11 September 2026 · via Trustpilot
AC
Adele
Verified buyer

“Easy to find and order what you want on the website. Delivery is quick to the UK”

8 September 2026 · via Trustpilot
Pago seguro Global Delivery Easy Returns Genuine Products

Product Price History

Información importante

  • Limitaciones: Para los productos enviados al extranjero, ten en cuenta que cualquier garantía del fabricante puede no ser válida; las opciones de servicio del fabricante pueden no estar disponibles; los manuales del producto, las instrucciones y las advertencias de seguridad pueden no estar en los idiomas del país de destino; los productos (y los materiales que los acompañan) pueden no estar diseñados de acuerdo con las normas, especificaciones y requisitos de etiquetado del país de destino; y los productos pueden no ajustarse al voltaje del país de destino y a otras normas eléctricas (lo que requiere el uso de un adaptador o convertidor, si procede). El destinatario es responsable de asegurarse de que el producto puede ser importado legalmente al país de destino. Cuando hagas un pedido a Ubuy o a sus filiales, el destinatario es el importador registrado y debe cumplir todas las leyes y normativas del país de destino.
  • No todos los productos que aparecen en Ubuy están a la venta, ya que Ubuy es un motor de búsqueda a nivel mundial. Los productos están sujetos a las normas de exportación/comercio.

HNL 1667

Haz tu pedido ahora y recíbelo por ahí Viernes, Octubre 09

QTY:

Secured transactionsecured transaction

PCI DSS compliant and ISO 27001:2022 certified, with encrypted payments and full buyer protection on every order.

Our Top Logistics Partners
  • fedex
  • dhl
Fastest cross-border delivery

características y beneficios

  • Learn probabilistic deep learning to enhance accuracy
  • Use Python, Keras, and TensorFlow Probability for practical applications
  • Improve network performance with the right distribution for various data types
  • Discover Bayesian variants to state their own uncertainty for increased accuracy
  • Build deep learning applications that handle real-world data noise and uncertainty
  • Includes free eBook in PDF, Kindle, and ePub formats

Ubuy Assurance

Experience worry-free shopping with 100% original products, PCI DSS-compliant payment security, ISO 27001-certified data protection, the fastest cross-border delivery, free returns *, and secure packaging on every order.