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Parva-26/README.md

   


◈ ABOUT

"I want to understand intelligence — so I'm building it from scratch."

I'm a 2nd-year B.Tech IT student at DJSCE, Mumbai obsessed with the mechanics of deep learning. Not the API-call kind — the rewrite the transformer from scratch, read the original paper, understand every gradient kind.

My goal is to contribute research at the intersection of LLMs, Mech Interp and Computer Vision, with a long-term target of Masters in AI/ML.

parva = {
    "currently"  : "Sem 4 · B.Tech IT · DJSCE Mumbai",
    "focus"      : ["LLMs", "Vision Transformers", "Mamba", "NLP", "CV"],
    "building"   : "research profile from ground up",
    "next"       : "first publication + international research internship",
    "motto"      : "from first principles, always"
}

◈ TECHNICAL ARSENAL

🔬 ML / DL — PRIMARY DOMAIN

🧮 LANGUAGES

📊 DATA & TOOLS


◈ CERTIFICATIONS & ACTIVE LEARNING

Course Provider Status
Machine Learning Specialization Andrew Ng — DeepLearning.AI + Stanford Completed
Deep Learning Specialization Andrew Ng — DeepLearning.AI Completed
NLP Specialization Andrew Ng — DeepLearning.AI Completed
Intro to Large Language Models NPTEL — IIT Delhi & IIT Bombay Completed
Neural Networks for CV and NLP NPTEL — IIT Guwahati Completed
CS231N Stanford Online (Youtube) Completed
CS153 Frontier Systems Stanford Online (Youtube) In Progress
HuggingFace CV Course HuggingFace In Progress

◈ RESEARCH INTERESTS

┌──────────────────────────────────────────────────────────────────┐
│                                                                  │
│   MECHANISTIC INTERP     →  TransformerLens, SAELens             │
│   LANGUAGE MODELS        →  Pretraining, SFT, RLHF, scaling      │                               
│   VISION TRANSFORMERS    →  ViT, Swin, dense prediction tasks    │
│   MULTIMODAL AI          →  Vision-language alignment            │
│   EFFICIENT INFERENCE    →  Quantization, distillation, pruning  │                                                                     
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

◈ ACTIVITY


◈ CONNECT

   

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  1. AlphaAgent AlphaAgent Public

    Multi-agent AI investment research terminal. ReAct agent + bull/bear debate system along with an arbiter, powered by LangGraph and Groq.

    Python

  2. gpt-from-scratch gpt-from-scratch Public

    A GPT-style Transformer language model implemented from scratch in PyTorch and trained on ~800M tokens using Google Colab GPUs. Inspired by Andrej Karpathy's nanoGPT but implemented independently.

    Python

  3. convnext-crowd-counting convnext-crowd-counting Public

    ConvNeXt-based crowd density estimation trained on ShanghaiTech dataset. Research experiment exploring modern ConvNet architectures for crowd analysis.

    Jupyter Notebook

  4. swin-crowd-counting swin-crowd-counting Public

    Crowd density estimation using Swin Transformer + FPN Decoder on ShanghaiTech dataset. MAE 41.45 on Part B. Active research toward transit crowd management.

    Jupyter Notebook

  5. neurostrat neurostrat Public

    AI-powered B2B/B2C outreach decision engine — predicts optimal contact channel and message tone using a multi-output ML pipeline (GradientBoosting + RandomForest, 93% F1). Built with scikit-learn, …

    TypeScript

  6. cbam-resnet cbam-resnet Public

    PyTorch implementation of CBAM: Convolutional Block Attention Module (Woo et al., ECCV 2018), integrated into a CIFAR-adapted ResNet-50 and evaluated on CIFAR-10.

    Python