Recently, I stumbled upon a white paper, which talked about the latest in AI applications in Marketing Analytics. It specifically talked about the application of XAI (Explainable AI) in marketing mix modelling [white paper]. This caught my attention and I started exploring more about three things: XAI, the current state of marketing analytics, and XAI’s potential applications … [Read more...] about Explainable AI: Application of Shapely Values in Marketing Analytics
Technical Guide
How To Get Started With Graph Machine Learning
This blog is a part of my “deep learning update” series and I want to open it up with a question: What have I learned about Graph ML in 2+ months? Nothing? If that was your first thought, no worries, it’s probably true. 😅 (Bad) jokes aside, my “relative knowledge” (the knowledge I “possess” vs the knowledge I’m aware of) is asymptotically … [Read more...] about How To Get Started With Graph Machine Learning
Advanced Forecasting Using Bayesian Diffusion Modeling
Across all areas of data science there is huge demand for innovative modeling solutions aimed at forecasting and elucidating dynamic phenomena. High profile use cases of modeling and forecasting dynamic phenomena include: Finance — prediction of share price movements or commodity price fluctuationsBiomedical science — prediction of biological trajectories, e.g. … [Read more...] about Advanced Forecasting Using Bayesian Diffusion Modeling
On Transformers, TimeSformers, And Attention
Transformers are a very powerful Deep Learning model that has been able to become a standard in many Natural Language Processing tasks and is poised to revolutionize the field of Computer Vision as well. It all began in 2017 when Google Brain published the paper destined to change everything, Attention Is All You Need [4]. Researchers apply this new architecture to … [Read more...] about On Transformers, TimeSformers, And Attention
To ROUGE Or Not To ROUGE?
In this article, we will learn about … … the difference between extractive and abstractive text summarization. … what the ROUGE score is. … why and where it fails. Text Summarization We refer to text summarization as the process of training an Artificial Intelligence (AI) model to produce a smaller chunk of text out of a bigger chunk of text. Where “smaller … [Read more...] about To ROUGE Or Not To ROUGE?
What Is Your Model Hiding? A Tutorial on Evaluating ML Models
Imagine you trained a machine learning model. Maybe, a couple of candidates to choose from. You ran them on the test set and got some quality estimates. Models are not overfitted. Features make sense. Overall, they perform as well as they can, given the limited data at hand. Now, it is time to decide if any of them is good enough for production use. How to evaluate … [Read more...] about What Is Your Model Hiding? A Tutorial on Evaluating ML Models
BERT Inner Workings
I created this notebook to better understand the inner workings of Bert. I followed a lot of tutorials to try to understand the architecture, but I was never able to really understand what was happening under the hood. For me it always helps to see the actual code instead of just simple abstract diagrams that a lot of times don’t match the actual implementation. If you’re like … [Read more...] about BERT Inner Workings
GPT2 For Text Classification Using Hugging Face Transformers
This notebook is used to fine-tune GPT2 model for text classification using Hugging Face transformers library on a custom dataset. Hugging Face is very nice to us to include all the functionality needed for GPT2 to be used in classification tasks. Thank you Hugging Face! I wasn’t able to find much information on how to use GPT2 for classification so I … [Read more...] about GPT2 For Text Classification Using Hugging Face Transformers
A Comprehensive Introduction to Bayesian Deep Learning
Photo by Cody Hiscox on Unsplash Preamble Neural Network Generalization Back to Basics: The Bayesian Approach Frequentists Bayesianists Bayesian Inference and Marginalization How to Use a Posterior in Practice? Maximum A Posteriori Estimation Full Predictive Distribution Approximate Predictive Distribution Bayesian Deep Learning Recent Approaches to Bayesian Deep … [Read more...] about A Comprehensive Introduction to Bayesian Deep Learning
Extractive Text Summarization Using Contextual Embeddings
Text Summarization is a process of generating a compact and meaningful synopsis from a huge volume of text. Sources for such text include news articles, blogs, social media posts, all kinds of documentation, and many more. If you are new to NLP and want to read more about text summarization, this article will help you understand the basic and advanced concepts. The … [Read more...] about Extractive Text Summarization Using Contextual Embeddings