In this post, we will use Pytorch to train a NLP Sincereness Detector which will detect whether a question is asked sincerely or not.

(source: https://commons.wikimedia.org/wiki/File:Pytorch_logo.png)

In this post, we will explore Faster-RCNN object detector with Pytorch. We will use the pretrained Faster-RCNN model with Resnet50 as the backbone.

In this post, I will show you how to use BERT as a feature extractor (embedding extractor) and perform text classification on the outputs of BERT.

(source: BERT: Pre-training of Deep Bidirectional Transformers forLanguage Understanding https://arxiv.org/pdf/1810.04805v2.pdf)

Semantic Segmentation is an image analysis task in which we classify each pixel in the image into a class. In this post, we will perform semantic segmentation using pre-trained models built in Pytorch. They are FCN and DeepLabV3.

In this post, I am not going to discuss the details of the theory behinds these RNNs. Instead, I am going to show you how you can actually apply this RNNs to your application.