Implemented the complete DeepSeek V3 architecture from scratch, a 100M+ parameter transformer featuring Multi-Head Latent Attention (MLA), Mixture of Experts (MoE), and Multi-Token Prediction (MTP). Trained on the FineWeb-Edu dataset with ~2.5B tokens on an NVIDIA A100 80GB GPU.

Built a complete Speech-to-Text Transformer model from scratch using PyTorch, converting raw audio waveforms into text without pre-trained models. Implements convolutional downsampling, multi-head self-attention, Residual Vector Quantization (RVQ), and CTC loss for alignment-free training. Trained on the LJSpeech dataset using an A100 GPU.

Implemented a Tacotron 2 neural text-to-speech model from scratch in PyTorch. The model generates mel-spectrograms from raw text input using an encoder-decoder architecture with attention mechanisms, then converts them to audio waveforms. Trained on the LJSpeech dataset.

Fine-tuned various open-source LLMs including LLaMA 2, Mistral, Qwen, and vision-language models for domain-specific tasks. Leveraged efficient methods like LoRA, QLoRA, and quantization using Unsloth and Hugging Face.

Implemented a Large Language Model (LLM) from scratch, covering every stage from data preparation and model architecture to pretraining and fine-tuning. This project demystifies transformer-based models through hands-on code and experiments, enabling a deeper understanding of attention mechanisms and token prediction.

Built an end-to-end MLOps pipeline to predict the approval status of US visa applications. Implemented machine learning models, deep learning techniques, and automated deployment pipelines.

An interactive web application designed to help users understand and experiment with machine learning algorithms visually. It offers dynamic visualizations for concepts like linear regression, allowing users to adjust parameters such as slope and intercept interactively. The platform aims to simplify ML concepts for learners through intuitive design and engaging tools.

Developed an end-to-end MLOps project to detect malicious URLs using XGBoost. Integrated robust pipelines for data ingestion, model training, deployment, and monitoring.

Predicted customer satisfaction scores for future orders using historical e-commerce data from the Brazilian E-Commerce Public Dataset by Olist. This project leverages multiple machine learning models like CatBoost, XGBoost, and LightGBM, built within a ZenML pipeline to create a production-ready solution.