Sharing My Learning in Math, Programming and AI
Every type of learning which exists in ML a small try to keep them listed together.
Overview of Recommendation system - Workshop for Pycon India 2025
Universe’s most universal law.
We discuss the Theoretical Foundation of Parallelism and Concurrency and some low level primitives. Discuss how it is done on Python and compare it with other programming language.
Exploring different tools and technologies to store, integrate, analyze, process, visualize data at large scale in a batch and stream fashion both with custom tools and AWS managed services
In this post, we discuss ways of managing different versions of Data and Model artificats. Configuration Management and Data Validation. Monitoring the model using TIG stack. Logging and Alert.
A dive into different types of attention from it start with Bahdanau to Flash Attention.
Different ways to increase the model’s prediction power using different regularization Techniques
Understanding different types of CNN layers and some common architectures
3D visualization of different optimizers.
A deep dive into different types of Metrics for evaluating Machine Learning Models.
Object Detection - SSD | YOLO | R-CNN | Fast R-CNN | Faster R-CNN
Deep into linguistics, For the curious souls to know why sometime languages don’t make sense and we want computers to understand it better than us.
Learning different ways of weight Intialization their effects and common pitfalls.
Let’s save some time and memory, coz both are expensive.
Lambda, Comphrensions, Decorators and Generators with some sweet python tips and tricks.
Word2vec, Glove, Fastext, ELMO, Character-Aware Language Model
We go through RNN, LSTM, GRU building intution and coding from scratch to understand forward and backward pass.
What is NLP, what problems does it solve and components within it.