Community Bonding period ended last week and my first blog is based on the work carried out in the last week. My meeting with GSOC mentors at the start of the week helped me chalk out an agenda for the week. As the first step, I familiarized myself with Tensorflow operations, functions and distribution strategies. My previous experience with PyTorch as well as `website tutorials` on basic Deep Learning models helped me quickly learn Tensorflow. As the next step, I read VQ-VAE paper & understood the tensorflow open source implementation. VQ-VAE addresses 'posterior collapse' seen in traditional VAEs and overcomes it by discretizing latent space. This in turn also improved the generative capability by producing less blurrier images than before. Familiarizing about VQ-VAE early on helps in understading the latents used in Diffusion models in later steps. I also explored a potential dataset - `IXI (T1 images) ` - and performed some exploratory data analysis, such as age & sex distribution. The images contain entire skull information, it may require brain extraction & registration. It maybe more useful to use existing preprocessed datasets & align them to a template. For next week, I'll be conducting further literature survey on Diffusion models.