Machine Learning Algorithms in Depth

11 Advanced deep learning algorithms

This chapter covers

• Variational autoencoders for time series anomaly detection
• Mixture density networks using amortized variational inference
• Attention and transformers
• Graph neural networks
• ML research, including deep learning

In the previous chapter, we looked at fundamental deep learning algorithms to help represent numerical, image, and text data. In this chapter, we will continue our discussion with advanced deep learning algorithms. The algorithms have been selected for their state-of-the-art performance architectures and a wide range of applications. We will investigate generative models based on variational autoencoders (VAEs) and implement, from scratch, an anomaly detector for time series ...