Debugging deep learning models is a complex and challenging task that requires significant expertise and experience. One of the main reasons for this complexity is that deep learning models involve multiple layers of interconnected nodes, which can make it difficult to pinpoint the source of errors or identify areas where optimization is needed. Additionally, deep learning models often rely on large amounts of data to train, which can increase the risk of overfitting or underfitting the model. This is why it is essential to have a deep understanding of both the deep learning model itself, as well as the data from which the model is being trained, when it comes to debugging deep learning models.....

Data analysis is a process where the data is inspected, cleaned, transformed and modeled with the aim of extracting actionable knowledge. This knowledge can support the decision-making process in businesses. As a result, data analysis has become an essential tool for businesses to increase their competitive edge and improve operational efficiency.....

Autoencoders are a type of neural network that can be used to learn a compressed representation of input data. They work by training the network to reconstruct the input data from a lower-dimensional latent representation, which is typically obtained using an encoder. Autoencoders are versatile and can be used for a variety of tasks, including data compression, anomaly detection, and feature learning high-dimensional data using a neural network model with a narrow bottleneck layer in the middle (oops, this is probably not true for Variational Autoencoder, and we will investigate it in details in later sections). A nice byproduct is dimension reduction: the bottleneck layer captures a compressed latent encoding....