— The main goal of this blog is to make the readers understand the architecture of GoogLeNet and Implement it from scratch using Tensorflow and Keras.


In order to improve the performance of neural network architecture, the network should be deeper (in terms of a number of layers) but, there are complications for creating a deeper network. The main problem with deeper neural networks is overfitting. The second problem with more layers is, increase in computational power. Previous image classification models like VGG16 use only a 3x3 filter in their network which used to be a bit difficult in capturing…

This blog is a complete package for understanding Generative Adversarial Networks including its math intuition and Implementing it using Tensorflow.

What are Generative Adversarial Networks?

GAN is a deep neural network architecture comprised of two neural networks, competing for one against the other, that’s the reason the adversarial term is used.

GAN’s learn and mimic any distribution of data. That is, GANs can be taught to create new outputs. Their output is so good that it has the potential of both good and bad. Look at this website here. All the faces you see on this website are generated by…

Nitish Kumar Pilla

Data Scientist Enthusiast, Master’s Student in Computer Science

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