We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular data, introducing a model abstraction to enable modular implementation of tabular models, and allowing external foundation models to be incorporated to handle complex columns (e.g., LLMs for text columns). We demonstrate the usefulness of PyTorch Frame by implementing diverse tabular models in a modular way, successfully applying these models to complex multi-modal tabular data, and integrating our framework with PyTorch Geometric, a PyTorch library for Graph Neural Networks (GNNs), to perform end-to-end learning over relational databases.

In this post we are going to explore few methods towards the explainability of BERT, and why it may be worth the time. BERT is an open source machine learning framework for natural language processing (NLP). BERT is designed to help computers understand the meaning of ambiguous language in text by using surrounding text to establish context. BERT, which stands for Bidirectional Encoder Representations from Transformers, is based on Transformers, a deep learning model in which every output element is connected to every input element, and the weightings between them are dynamically calculated based upon their connection (in NLP, this process is called attention). Let me explain the problem in a layman term first before delving into details and throwing a lot of technical and Machine learning jargons at you. So to put simply, if we have text columns in our dataset along with numerical columns and if we want to understand how that text column's content is contributing in our predictions (what words, bigrams, trigrams are playing important role) then how can we do it?

According to Taweh Beysolow, "Natural Language Processing (NLP) is a subfield of computer science that is focused on allowing computers to understand language in a'natural' way, as humans do." NLP has evolved so rapidly gaining traction in its applications inn artificial intelligence (AI). In this project, we will explore one of the most exciting NLP applications i.e. We will build a machine learning model that can categorize tweets as positive (pro-vaccine), negative (anti-vaccine) or neutral. Stay tuned and let's jump into the project.

In this series of articles, I would like to show how we can use a deep learning algorithm for fake news detection and compare some neural network architecture. This is the second part of this series, where I would like to create several deep learning models with Keras and Tensorflow. In the previous part of this series, I made exploratory data analysis for fake and not fake news. I used different analytics technic to compare fake and not fake news, let's give this work for neural networks. To start modeling, we need to make data preprocessing. Let's check NA values in our train data frame: As we can see, there is a lot of NA value in our dataset.