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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
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.
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.
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.
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.
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.
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.