In this post, we collect our experiments with tranformer based models [repo]

In this post, we will explore TypeScript and Kotlin. TypeScript is a superset of JavaScript allowing static data types as an optional choice. Kotlin is a modern Android development language [repo]

In this post, we will explore LangChain and LlamaIndex - two popular frameworks to help with application development using large language models in Python [repo]

In this post, we will explore Transformers and Diffusers - two popular generative AI libraries by HuggingFace, both based on the transformer architecture in Python [repo]

Let us take a deep dive into the transformer architecture - key to the recent success of large language models.

In this post, we will explore NLTK (Natural Language Toolkit) and spaCy - two traditional libraries for statistics-based natural language processing in Python [repo]

In this post, we will explore scikit-learn, XGBoost, and pandas - three traditional machine learning libraries in Python [repo]

Let us revisit the bias-variance tradeoff - a topic of timeless intrigue in statistics and machine learning.

Let us understand the concepts of bagging and boosting in machine learning.

Let us revisit the distinction between parameters and hyperparameters in ML models.

In this post, we will explore: i) Distributed training on GPUs based on torch.cuda and torch.distributed, ii) Model optimization based on onnx and torch_tensorrt, and iii) Distributed inference with the nvidia triton inference server [repo]

Let us appreciate a nice explanation of implicit schema in databases as one might explain to a 5-year-old.

In this post, we will try to gain an intuitive understanding of MongoDB, Elasticsearch, and Qdrant with examples [repo]

In this post, we will learn how to use SQL to query a plant disease dataset loaded both as MySQL and PostgreSQL databases. We will also compare the capabilities of each database in general and specific to our example task.

In this post, we will learn about Kafka, Terraform, and Jenkins - three well recognized tools to automate and accelerate software deployment pipelines.

In this post, we will learn about three popular cloud service platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. We hope to build up this post asynchronously as we explore more of this ecosystem.

Let us take a look at key factors that contribute to the high prices of GPUs.

In this post, we will see how APIs work. We will learn about two distinct architectural styles: REST and GraphQL. We will also see code examples of i) how REST APIs are implemented in Python using the Django REST Framework (DRF), and ii) how GraphQL APIs are implemented in Python with the Graphene library [repo]

In this post, we hope to concisely introduce basic concepts in Docker and Kubernetes. We hope to get new learners interested in the exciting world of containerization and distributed systems.

Let us understand the prospects and challenges of using vector embeddings in database search. In the context of language, this is often called semantic search, although Google products today are evidence that semantic search is certainly not limited to text!

In this post, we hope to concisely introduce PyTorch and JAX - two prominent frameworks for deep learning [colab] [repo]

In this post, we will implement backpropogation using elementary arithmetic operations [colab] [repo]

This term, I am taking a graduate class titled “Special Topics in Computational Physics”. I look forward to it, and hopefully sharing some nice things I learn.

Welcome to my blog. I hope to communicate things I find interesting here.