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Probabilistic Deep Learning with Python

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Probabilistic Deep Learning with Python teaches the increasingly popular probabilistic approach to deep learning that allows you to tune and refine your results more quickly and accurately without as much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data.

Learn more about the book here: https://bit.ly/2YFIOq4

Probabilistic Deep Learning with Python teaches the increasingly popular probabilistic approach to deep learning that allows you to tune and refine your results more quickly and accurately without as much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data.

Learn more about the book here: https://bit.ly/2YFIOq4

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Probabilistic Deep Learning with Python

  1. 1. A New Approach to Deep Learning With Probabilistic Deep Learning with Python. Take 42% off by entering sldurr into the discount code box at checkout at manning.com.
  2. 2. Fit probabilistic models Probabilistic Deep Learning with Python teaches the increasingly popular probabilistic approach to deep learning. Probabilistic models do not only yield single predictions but a whole distribution of possible outcomes. It’s a great method for dealing with the uncertainty of real-world data!
  3. 3. Model real world randomness Emphasizing practical techniques that utilize the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant probabilistic deep learning models that can reliably handle the data inherent variability. A deep learning scatter plot modeling the uncertainty
  4. 4. A complete resource Probabilistic Deep Learning with Python shows how to apply probabilistic deep learning models on a broad range of applications. Hands-on code examples and illustrative Jupyter notebooks ensure that you’re focused on the practical applications of the abstract-but- powerful concepts of probabilistic deep learning.
  5. 5. Learn from a team of experts 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 at ZHAW, 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 thesis on 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.
  6. 6. If you want to learn more about the book, check it out on liveBook here. Take 42% off Probabilistic Deep Learning with Python by entering sldurr into the discount code box at checkout at manning.com.

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