In this blog, we will go through a gentle introduction to Inception architecture by Google in 2014 and versions of the Inception network.

The Problems

Before the Inception network came research tries how to make deeper models because the most straightforward way of improving the performance of deep neural networks is by increasing their size but some of the problems researchers faces are :

• The problem of vanishing gradient arises as the model gets deeper because of which deeper layers not able to train properly and gradient update in deeper layers is not effective. Also, very deep networks are prone to overfitting.
• …

BERT is a state-of-the-art model by Google that came in 2019. In this blog, I will go step by step to finetune the BERT model for movie reviews classification(i.e positive or negative ). Here, I will be using the Pytorch framework for the coding perspective.

BERT is built on top of the transformer (explained in paper Attention is all you Need). If you want to know how the transformer work, you can check out my blog on the Transformer- Attention is all you Need

Before digging into the code let’s first understand how the model works.

Input text sentence would first be tokenized into words, then the special tokens ( [CLS], [SEP]…