Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability

Price: $49.99 – $43.84
(as of Dec 24,2024 10:12:47 UTC – Details)

Publisher ‏ : ‎ Manning; 1st edition (November 10, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 296 pages
ISBN-10 ‏ : ‎ 1617296074
ISBN-13 ‏ : ‎ 978-1617296079
Item Weight ‏ : ‎ 1.1 pounds
Dimensions ‏ : ‎ 7.38 x 0.3 x 9.25 inches

Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability

In the world of deep learning, there is a growing interest in incorporating uncertainty into neural networks. This is where probabilistic deep learning comes into play, allowing models to not only make predictions, but also quantify the uncertainty around those predictions.

One popular framework for implementing probabilistic deep learning is TensorFlow Probability, which extends TensorFlow with tools for building and training probabilistic models. Paired with Keras, a high-level neural networks API, you can easily create and train probabilistic models in Python.

In this post, we will explore how to implement probabilistic deep learning using Python, Keras, and TensorFlow Probability. We will cover topics such as defining probabilistic layers, training models with uncertainty, and evaluating model performance using probabilistic metrics.

By the end of this post, you will have a solid understanding of how to leverage probabilistic deep learning techniques in your own projects, allowing you to build more robust and reliable models that can account for uncertainty in their predictions. So stay tuned for an in-depth dive into the world of probabilistic deep learning with Python, Keras, and TensorFlow Probability.
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