This is the final part of the series “All about GANs” where we will be focusing more upon the StyleGAN. In this particular article, we will be exploring the architecture of StyleGAN in-depth.

And also, if you aren’t very familiar with the working of GAN (or) are interested to know about other variants of GANs and their in-depth working then you can refer to my previous blogs on GAN which are mentioned below.

Blog 1:

Blog 2:

Blog 3:

Blog 4:

In this particular blog which is the 3rd part of the series “All about GANs” where I will be discussing the architecture & working of DCGAN (Deep Convolution Generated Adversarial Network) and its implementation.

Here we will be implementing DCGAN using the PyTorch framework to generate sneaker images on our own dataset that was scraped from Instagram.

Welcome to the second blog of the entire series “All about GANs”. The entire blog series is broken down into 4 parts and this is the 2nd blog. In this blog we will be discussing other variants of GANs mainly:

  1. InfoGAN (Information GAN)
  2. BiGAN (Bidirectional GAN)
  3. CycleGAN

In the previous blogs, we introduced the concept of GAN and its working and also discussed different variants of GAN. If you are interested to understand more about the basics and working of GAN then please go through these previous blogs.

In this blog, we will be discussing other popular variants of GAN such as:

  1. cGAN (Conditional GAN)
  2. WGAN (Wasserstein GAN)
  3. InfoGAN (Information GAN)
  4. BiGAN (Bidirectional GAN)
  5. CycleGAN
  6. DCGAN (Deep convolutional GAN)
  7. BigGAN, StyleGAN & StyleGAN2


All about GANs” blog series will be broken down into 4 parts. In this blog which is PART-1 we will mainly discussing about cGAN & WGAN. We will discuss other variants in upcoming parts.

In our previous blog, we introduced the GAN (Generative Adversarial Networks) and its working and mathematics. We also looked at its limitation and problems.

I would strongly recommend reading the…


All those people that you can see in the image above do not exist in person and are all created using StyleGAN 2. GANs have been lately too popular because of their wide applications in several fields such as computer vision, gaming, medical science, artificial intelligence, etc.

Their ability to generate such high-quality photorealistic images is something that has astonished many people over the globe. It’s too hard to differentiate between the images that they generate whether they are real or fake!

Just take some time to analyze the below gif of people who do not exist in reality.


Convolutional Neural Networks (CNN) are mostly used for images and videos. These tend to perform better than the feed-forward network as the image is nothing but matrices of different values that represent different values that range from 0–255. For e.g.: A black and white image of dimension 100×100 would have around 10000 values in it when flattened. Similarly, an HD image of resolution 1920x108x3 would generate around 6 million values. These 6million values belong to a single image and a bunch of these are would be required to train the machine and model on would round up to a very…

Neural network’s under-the-hood mathematics has baffled many aspiring data scientists and mathematicians for a pretty long time. Our objective here is to discuss how exactly the neural network functions and how the gradient descent and backpropagation works which is the most important function of any neural network that defines the entire working of the neural network. Here we will try to uncover the work that goes inside a black box and inspect every process and step that takes place using Excel and simple mathematics. Some of the important steps that take place in a neural network are:

- Assigning of…

Decision tree is one of the most important models as it lays out an important concept that is used for other machine learning models like Random Forest, XGBoost, bagging & boosting, etc which all together come under the ensemble methods. It’s a tree-shaped model consisting of root nodes, branches, and internal & leaf nodes which are mostly used for supervised learning. So, it’s really important to understand the concept of a decision tree and here we have explained the functioning of decision trees.

A decision tree can be broadly categorized into — Regressors & Classifiers and hence this is where…

Sudeep Das

A passionate &inquisitive learner, member of Data Science Society(IMI Delhi). A strong passion for ML/DL, mathematics, quantum computing & philosophy.

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