Enroll Course: https://www.coursera.org/learn/probabilistic-deep-learning-with-tensorflow2
In the rapidly evolving landscape of artificial intelligence, understanding and quantifying uncertainty in deep learning models is no longer a luxury, but a necessity. The Coursera course ‘Probabilistic Deep Learning with TensorFlow 2’ offers a comprehensive and practical approach to this critical area, building upon foundational TensorFlow knowledge.
This course is designed for those who have a grasp of the basics of TensorFlow and are looking to push the boundaries of their deep learning applications. It delves into the power of probabilistic modeling, a framework that allows us to explicitly account for the inherent noise and uncertainty present in real-world datasets. This is particularly vital for applications demanding high reliability, such as autonomous systems and medical diagnostics, where knowing the confidence of a prediction is as important as the prediction itself.
The syllabus is structured to provide a robust understanding of key probabilistic deep learning techniques. The journey begins with **TensorFlow Distributions**, introducing the TensorFlow Probability (TFP) library. You’ll learn to leverage Distribution objects for sampling and probability computation, and crucially, how to make them trainable. The practical application comes with implementing a Naive Bayes classifier.
Next, the course tackles **Probabilistic Layers and Bayesian Neural Networks**. This section addresses the limitations of standard deep learning models in quantifying uncertainty. By using TFP’s probabilistic layers, you’ll build models that can provide measures of uncertainty in both data and the model itself. A Bayesian Convolutional Neural Network (CNN) on MNIST and MNIST-C datasets serves as the hands-on assignment.
The curriculum then moves to **Bijectors and Normalizing Flows**, a sophisticated class of generative models. You’ll explore how bijective transformations can model complex data distributions, using TFP’s bijector objects to implement these transformations and learn distributions from data. The assignment involves creating a RealNVP normalizing flow model for the LSUN bedroom dataset, enabling new data generation and likelihood evaluation.
Following this, **Variational Autoencoders (VAEs)** are explored. As a popular type of likelihood-based generative model, VAEs involve learning both an encoder and a decoder network. The course guides you through implementing VAEs with TFP, allowing you to encode data into a latent space and generate new samples. The practical aspect includes developing a VAE for a celebrity faces dataset.
Finally, the **Capstone Project** consolidates all learned concepts. You’ll create a synthetic image dataset using normalizing flows and then train a VAE on it, demonstrating a holistic understanding of probabilistic deep learning.
**Recommendation:**
‘Probabilistic Deep Learning with TensorFlow 2’ is an exceptional course for anyone serious about building more robust, reliable, and interpretable deep learning models. The instructors provide clear explanations and the programming assignments are well-designed to reinforce learning. If you’re looking to add a powerful new dimension to your deep learning toolkit, this course is highly recommended.
Enroll Course: https://www.coursera.org/learn/probabilistic-deep-learning-with-tensorflow2