Iftekhar Ahmed

This blog post aims to make PixelCNN easy to understand for beginners. We’ll start with an example to show the problem PixelCNN is designed to solve, then break down its core idea of using masked convolutions to model images pixel by pixel. From there, we’ll walk through its architecture step-by-step, including Type A and Type B masks, and build a simple PixelCNN from scratch using PyTorch. We’ll also discuss its strengths, limitations, and how it connects to similar ideas in other fields. By the end of this blog, you’ll have a clear understanding of what PixelCNN is, how it works, and how to implement it yourself.

The blog post explores Generative Adversarial Networks (GANs), which consist of two neural networks, the Generator and the Discriminator, competing in a dynamic process to create realistic data. Unlike Variational Autoencoders (VAEs), GANs produce sharp images by having the Generator create fake data while the Discriminator evaluates its authenticity. This adversarial interaction drives both networks to improve continuously, resulting in highly convincing outputs. The post explains the architecture and training of GANs using an analogy of a counterfeiter and a detective and discusses challenges like mode collapse and training instability. It concludes with a practical example of building a GAN using PyTorch and the Fashion-MNIST dataset, showcasing the generation of new fashion items.

This blog post aims to demystify autoencoders for beginners. We'll start with an example to illustrate the problem autoencoders solve, then dive into their fundamental architecture, and finally, build a simple autoencoder from scratch using PyTorch. We'll also explore their limitations and introduce you to the concept of Variational Autoencoders (VAEs), which address some of these challenges and build it from scratch. By the end of this blog, you'll have a solid grasp of what autoencoders are, how they work, and where they fit in, and you'll build an autoencoder along with a variational autoencoder from scratch.