Explain Large Machine Learning Model Output
In this post I will explain Integrated Gradient, a popular technique to explain black box type machine learning model.
Work In Progress Financial Data Science Part1 Cloud Infrastructure
In this article, I will demonstrate how to use AWS Step Function. AWS Step Function is a service that lets you orchestrate multiple AWS services in a workflow. I will use it to analyze financial data with some simple code examples. The code examples are written in AWS CDK, which is a tool that helps you create cloud resources with your preferred programming language.
Machine Learning Model Inference
Inference is the process of using machine learning model to predict the outcome of a given input record. Inference typically requires low latency to ensure a smooth customer experience. This post will outline a few options for optimizing inference operation.
Llm Based Agent Framework Wip
Exploring the Dynamics of Multi-Agent Frameworks: An Introduction
Distributed Systems Review
This post will review a distributed system’s research paper:
Millions of Tiny Databases by Brooker et al.
In Demand Generative Ai Skills For The Future
The domain of Generative AI is advancing swiftly, leading to a surge in the need for adept professionals adept at steering through its intricate terrain. Pioneering firms are on the lookout for gifted contributors who can aid in crafting cutting-edge AI models. Let’s delve into the expertise that’s gaining prominence in this trailblazing field.
Fact checking NLP: Selecting the right evidence using BERT
In order to verify a claim, we can utilize a knowledge corpus like Wikipedia. Checking a claim generally involves three steps 1) relevant document retrieval from knowledge corpus, 2) relevant sentence retrieval from the documents, 3) identify whether the claim is supported by the evidence sentences.
Language model to identify next mutant coronavirus 501Y.Vx: Research paper review
A virus continuously evolves to escape its host immune system. A mutant virus needs to have two properties:
- Fitness
- Semantic change
My Picks From Neurips 2020
NeurIPS 2020 is virtual this year. As a result, not only the talks were virtual, but also the networking and poster sessions were held online. I got to experience gather.town for the first time. It felt like playing video games at times. I changed my avatar many times :D
All the keynotes had sign language interpretation. I thought it was cool!
Below are some of the talks that I enjoyed watching or reading.
Machine Learning For Autonomous Vehicle
Autonomous vehicle (AV) heavily utilizes machine learning for various tasks. Majority of these tasks are related to its perception. The perception module helps the vehicle sees the world. Recent advancements in deep learning have improved the perception for autonomous.
Effective Reinforcement Learning
Covid 19 Tracking Through Statistics
I have done some analysis on COVID-19 and its related datasets, with Bangladesh as a casestudy. You can read the findings of the study in this blog written in Bengali.
Reproducible Machine Learning
Can we reproduce an ML model’s validation loss across two training runs?
Amazing Ai
High Dimensional Visualization
Optimization often produces high dimensional data. This post will include some example plots for these optimizations.
Distributed Gradient Descent
Recursive Feature Elimination With Cross Validation (rfecv)
Feature selection helps machine learning model separate out noise from signal. It drops unnecessary features that are not contributing to the model’s performance. Following slides describe RFECV which is the recursive method of eliminating noisy features.
Math Behind Random Forest
Random forest is one of the widely used machine learning models for supervised learning task. It is robust to missing values in dataset as well as to outliers. It is an ensemble of many decision trees. Therefore, it achieves good accuracy in practice. In this post, I will present detail mathematics of how a Random forest works.
Parallel Computing For Python Workload
Modern day’s computer processor comes with multiple cores. Utilizing different cores often vastly reduces runtime of programs. This is helpful in the context where program manipulates large of amount of data. This tutorial will list out some ways to enable parallelization of Python code involving Pandas data frame.
Relational Machine Learning
(Colab Notebook for the blog post)
In real world data often live in non-euclidean space. Examples include social networks, point clouds, etc. Such data contains topological information and are non-linear in nature. Typical machine learning models treat data point as independent to each other. In this post we will look at a model that exploits the inter-relationship of the data points and apply them to perform machine learning task such classification. First we look at some non-euclidean data
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