Posts

GPT – Generative Pre-trained Transformers

GPT is a decoder-only, autoregressive language model built on the Transformer architecture. It predicts the next token in a sequence and powers state-of-the-art natural language generation.

BERT - Bidirectional Encoder Representations from Transformers

BERT is a transformer-based model that uses bidirectional self-attention for improved language understanding and task-specific fine-tuning.

Transformers

Transformers are a deep learning architecture utilizing self-attention, cross-attention, and multi-head attention to efficiently process sequences, enabling powerful models for tasks like language modeling and transfer learning.

Denoising Diffusion Probabilistic Models [Diffusion Models]

Denoising Diffusion Probabilistic Models (DDPMs) generate high-quality images by progressively adding and then removing noise, learning to reverse the process to create realistic images.

Generative Adversarial Networks [GANs]

Generative Adversarial Networks (GANs) use two competing neural networks to generate realistic data, with applications in image generation, synthetic data creation, and more.

REST APIs - Guide

RestAPIs are most widely used API framework and this is a breif description about what REST APIs are.

Git and Github

Git is a powerful version control system that enables developers to track changes, collaborate efficiently, and manage project versions with ease.

Prompt Engineering Frameworks

Craft clear prompts by defining roles, tasks, goals, and desired outcomes for better AI results.

Enhancing Generative AI [GenAI] models: Fine Tuning vs. RAG

An overview of Fine Tuning vs. Retrieval Augmented Generation (RAG) techniques for enhancing Generative AI models, highlighting their strengths, limitations, and ideal use cases.

Simulated Annealing [SA]

Simulated Annealing (SA) is an optimization algorithm inspired by the annealing process in metallurgy, known for its ability to escape local optima and find near-global solutions in complex problems.

Ant Colony Optimization [ACO]

ACO is an optimization algorithm inspired by ant behavior, using pheromone trails and heuristic information to efficiently solve complex problems like routing and scheduling.

Particle Swarm Optimization [PSO]

PSO is an effective, easy-to-implement optimization algorithm inspired by social behavior in nature, used across various fields for solving complex problems like machine learning and engineering design.

Genetic Algorithm [GA]

GA is an optimization technique inspired by natural selection, using processes like selection, crossover, and mutation to evolve solutions over generations, with applications in areas like machine learning, design optimization, and AI behavior evolution.