Study of variational autoencoders in machine learning

Abstract

Autoencoders are essential in the field of machine learning because of the wide range of applications and distinctive talents they have. The ability of autoencoders to learn condensed and effective representations of complicated input data is one of the main factors in their significance. Autoencoders offer effective data compression by encoding the input data into a lower-dimensional latent space, which is useful in situations with constrained storage or bandwidth. Autoencoders are also frequently employed for unsupervised learning tasks like data generation, dimensionality reduction, and anomaly detection. Without relying on explicit labels or supervision, they enable us to find underlying patterns and structures in the data. Overall, the versatility and utility of autoencoders make them a fundamental tool in the machine learning toolbox, empowering researchers and practitioners to tackle a wide range of problems across diverse domains. Generative models, such as autoencoders, play a fundamental role in machine learning by enabling the creation of new, synthetic data that closely resembles the original input distribution. These models have revolutionised various domains, including image generation, text synthesis, and music composition, among many others. By capturing the underlying patterns and structures of the training data, generative models provide a powerful framework for creative applications, data augmentation, and simulation studies. This project aimed to explore the capabilities and applications of autoencoders, a type of neural network architecture, in the field of machine learning. The main focus of the project was to refactor legacy code used for image identification and transform it into an autoencoder capable of generating MNIST images. Through extensive experimentation and analysis, the project demonstrated the effectiveness of autoencoders in learning representations of input data and generating high-quality synthetic images. The findings of this study contribute to our understanding of autoencoders and their potential for various tasks, including image generation. The project also highlighted the importance of clean code practises, code refactoring, and neural network architectural design principles in adapting existing models for new purposes