Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability
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
منتج #: 51000237

Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability

منتج #: 51000237

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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.
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Practical Approach
Offers hands-on techniques to implement probabilistic models in Python using Keras and TensorFlow Probability, making it ideal for practitioners and students seeking practical knowledge.
Comprehensive Coverage
Covers a wide range of topics in probabilistic deep learning, ensuring readers gain a thorough understanding, from theory to application, setting it apart from other resources.
Latest Tools
Utilizes cutting-edge tools and libraries, emphasizing modern methods in machine learning that empower users to build robust models, enhancing both their learning and practical skills.

تفاصيل المنتج

Discover the power of Probabilistic Deep Learning! Get your copy of Probabilistic Deep Learning book with Python, Keras, and TensorFlow at Ubuy مصر. Available worldwide for delivery!
  • Probabilistic Deep Learning shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results. Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the Python-based Tensorflow Probability framework, and set up Bayesian neural networks that can state their uncertainties.Key Features· The maximum likelihood principle that underlies deep learning applications· Probabilistic DL models that can indicate the range of possible outcomes· Bayesian deep learning that allows for the uncertainty occurring in real-world situations· Applying probabilistic principles to variational auto-encodersAimed at a reader experienced with developing machine learning or deep learning applications.About the technology Probabilistic deep learning models are better suited to dealing with the noise and uncertainty of real world data ―a crucial factor for self-driving cars, scientific results, financial industries, and other accuracy-critical applications.Oliver Dürr is professor for data science at the University of Applied Sciences in Konstanz, Germany.Beate Sick holds a chair for applied statistics at ZHAW, and works as a researcher and lecturer at the University of Zurich, and as a lecturer at ETH Zurich.Elvis Murina is a research assistant, responsible for the extensive exercises that accompany this book.Dürr and Sick are both experts in machine learning and statistics. They have supervised numerous bachelors, masters, and PhD the seson the topic of deep learning, and planned and conducted several postgraduate and masters-level deep learning courses. All three authors have been working with deep learning methods since 2013 and have extensive experience in both teaching the topic and developing probabilistic deep learning models.
Publisher Manning Publications
Publication date 8 Feb. 2021
Edition 1st
Language English
Print length 252 pages
ISBN-10 1617296074
ISBN-13 978-1617296079
Item weight 210 g
Dimensions 18.73 x 0.76 x 23.5 cm

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Suitable For
  • Data Scientists

    Ideal for data scientists looking to enhance their model-building skills using probabilistic approaches and deep learning frameworks.

  • Machine Learning Students

    Perfect for students aiming to understand advanced concepts in deep learning and probabilistic modeling with practical Python applications.

  • Researchers

    Great resource for researchers exploring innovative probabilistic methods in machine learning and applying them to real-world scenarios.

Not Suitable For
  • Beginners

    Not suitable for complete beginners in programming or machine learning due to its advanced concepts and technical depth.

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    إجابه: من السهل التسوق في Probabilistic Deep Learning: With Python, Keras and عبر الإنترنت من يوباي. كل ما عليك فعله هو البحث عن المنتج واختيار طريقة الشحن الخاصة بك أثناء الدفع وسيتم توصيله الى عنوانك
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    إجابه: نعم ، في يوباي Egypt هذا المنتج متاح لك للتسوق بسعر مناسب. Probabilistic Deep Learning: With Python, Keras and غير متوفر محليًا ولكن يمكنك الوثوق بنا بخدماتنا للشحن السريع.
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Data Mining Editorial Review

The book "Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability" has garnered mixed reviews from customers, reflecting a blend of appreciation and criticism that illuminate different facets of the reading experience. It is noted for being well-written and easy to follow, which makes it a suitable introduction for those new to the field. Many reviewers commend the clarity of the explanations and the inclusion of code examples and graphical representations that enhance understanding. However, some users feel the book falls short of expectations, particularly regarding depth. They argue that the initial sections serve more as an introduction to deep learning and TensorFlow rather than delving into the probabilistic aspects, which only begin around the middle of the text. As a result, readers seeking comprehensive insights into probabilistic deep learning may find the content too superficial and even question the book's overall value compared to online courses that might provide a more practical and extensive learning experience. Editing errors have also been a recurring theme in the feedback, with numerous reviewers highlighting issues such as typos and translation mistakes, which detract from the professionalism of the material. Overall, while the text offers a friendly entry point into the subject, it may not meet the expectations of seasoned practitioners or those seeking in-depth knowledge on probabilistic methods in deep learning. **

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إيجابيات

  • Well-written and clear language.
  • Engaging and easily-paced read.
  • Good code examples and figures to illustrate concepts.

سلبيات

  • Initial sections are overly basic and light on content.

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