Enroll Course: https://www.coursera.org/learn/probabilistic-deep-learning-with-tensorflow2
In the rapidly evolving field of deep learning, understanding and quantifying uncertainty is becoming increasingly crucial. The course ‘Probabilistic Deep Learning with TensorFlow 2’ on Coursera offers a comprehensive dive into this essential area, building on foundational TensorFlow concepts and introducing learners to probabilistic modeling techniques.
### Course Overview
This course is part of a specialization that emphasizes the probabilistic approach to deep learning, which is vital for applications where noise and uncertainty are prevalent, such as autonomous systems and medical diagnostics. The course is structured into five main modules, each focusing on different aspects of probabilistic deep learning.
### Syllabus Breakdown
1. **TensorFlow Distributions**: The course kicks off with an introduction to the TensorFlow Probability (TFP) library, where learners will explore Distribution objects and learn how to sample from and compute probabilities. The hands-on programming assignment involves implementing a Naive Bayes classifier on the Iris dataset, providing a practical foundation for the concepts learned.
2. **Probabilistic Layers and Bayesian Neural Networks**: This module emphasizes the importance of uncertainty in predictions, especially in safety-critical applications. Students will learn to use probabilistic layers to develop models that quantify uncertainty, culminating in a programming assignment where they create a Bayesian CNN for the MNIST datasets.
3. **Bijectors and Normalising Flows**: Here, learners will delve into normalising flows, a powerful class of generative models. The course teaches how to implement bijective transformations using TFP, with a programming assignment focused on developing a RealNVP normalising flow model for the LSUN bedroom dataset.
4. **Variational Autoencoders**: This module covers one of the most popular generative models, the Variational Autoencoder (VAE). Students will learn to implement a VAE using TFP, with a hands-on project involving celebrity face datasets, allowing them to encode data into a compressed latent space and generate new samples.
5. **Capstone Project**: The course culminates in a capstone project that integrates all the concepts learned. Students will create a synthetic image dataset using normalising flows and train a VAE on it, solidifying their understanding of probabilistic deep learning.
### Conclusion
‘Probabilistic Deep Learning with TensorFlow 2’ is an excellent course for anyone looking to deepen their understanding of deep learning while incorporating the critical aspect of uncertainty. The blend of theoretical knowledge and practical assignments ensures that learners not only grasp the concepts but also apply them effectively. I highly recommend this course to data scientists, machine learning practitioners, and anyone interested in advancing their skills in probabilistic modeling.
### Tags
– Deep Learning
– TensorFlow
– Probabilistic Modeling
– Machine Learning
– Bayesian Networks
– Variational Autoencoders
– Normalising Flows
– Data Science
– Online Learning
– Coursera
Enroll Course: https://www.coursera.org/learn/probabilistic-deep-learning-with-tensorflow2