Probabilistic Deep Learning with TensorFlow

Standard deep learning models make predictions with absolute confidence, even when they are wrong. Probabilistic deep learning solves this by allowing neural networks to quantify their doubts, making them safer and more reliable for critical real-world applications. In this written course, you will transition from deterministic deep learning to probabilistic modeling. You will learn how to represent uncertainty in both your data and your model weights, enabling you to build robust neural networks that can express when they are unsure. Starting with foundational probability concepts, you will progress to coding practical, probabilistic architectures. What you'll learn: - Understand the core concepts of probability distributions and uncertainty quantification in deep learning. - Build neural networks that output probability distributions using the TensorFlow Probability library. - Model aleatoric uncertainty to capture the inherent noise present in real-world datasets. - Implement Bayesian neural networks to estimate epistemic uncertainty in model parameters. - Apply modern variational inference techniques and Monte Carlo methods to train probabilistic models. - Evaluate probabilistic forecasts using proper scoring rules and calibration metrics. The course begins with foundational terminology and basic distribution concepts before guiding you through written explanations and code snippets for constructing, training, and evaluating uncertainty-aware models. This course is designed for developers, data analysts, and machine learning enthusiasts who have a basic understanding of neural networks and Python, and want to learn how to handle uncertainty in their models. No prior experience with probabilistic programming is required. Start reading to build deep learning models that know what they don't know.