Enroll Course: https://www.coursera.org/learn/probabilistic-deep-learning-with-tensorflow2
If you’re looking to deepen your understanding of deep learning with a focus on uncertainty quantification, the ‘Probabilistic Deep Learning with TensorFlow 2’ course on Coursera is an excellent choice. Building on foundational TensorFlow skills, this course explores the probabilistic approach, which is essential for safety-critical applications like medical diagnosis, autonomous vehicles, and more. The curriculum covers key topics such as TensorFlow Distributions, probabilistic layers, Bayesian neural networks, bijectors, normalising flows, and variational autoencoders. Each module combines theoretical knowledge with practical programming assignments, enabling you to implement models like Naive Bayes classifiers, Bayesian CNNs, RealNVP normalising flows, and VAEs.
The course culminates in a capstone project where you create a synthetic image dataset using normalising flows and train a variational autoencoder. The hands-on approach paired with comprehensive lectures makes this course suitable for intermediate learners eager to incorporate uncertainty modeling into their AI toolbox. Whether you’re an aspiring data scientist or a seasoned machine learning engineer, this course will enhance your skill set and prepare you for real-world challenges involving complex data distributions and risk-sensitive applications. I highly recommend this course for anyone interested in advancing their knowledge in probabilistic deep learning and generative modeling with TensorFlow 2.
Enroll Course: https://www.coursera.org/learn/probabilistic-deep-learning-with-tensorflow2