class YashChindam:
def __init__(self):
self.name = "Yash Chindam"
self.role = "AI / ML Engineer"
self.focus = ["LLMs", "RAG Systems", "Computer Vision", "NLP", "Deep Learning"]
self.passion = "Building AI systems that solve real-world problems"
self.currently = "Exploring Generative AI & multimodal research"
self.hf_profile = "https://huggingface.co/yashchindam"
def say_hi(self):
print("Thanks for dropping by! Let's build something intelligent together π")
me = YashChindam()
me.say_hi()
Results: RMSE |
π£οΈ AI Voice Onboarding System
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Accuracy: 85β92% Β· Speed: 30β60s/doc |
ποΈ Vision-Based Entity Extraction
Accuracy: Entity F1 80β92% |
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ποΈ Podcast Generator
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Pipeline: 9-step automated analysis Β· Parallel multi-key processing |
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Supported: 8+ document types Β· FNOL Β· Proof of Loss Β· Damage Photos |
ποΈ More Projects (click to expand)
| Project | Description | Stack |
|---|---|---|
| RAG w/ LLaMA2 + LangChain + ChromaDB | End-to-end RAG pipeline using LLaMA 2 | LLaMA 2 ChromaDB LangChain |
| PDF Chatbot with RAG | Conversational PDF Q&A with RAG architecture | RAG FAISS LLMs |
| Conversational Q&A Chatbot β Mistral Nemo | Contextual, multi-turn chatbot using Mistral Nemo | Mistral Nemo LangChain |
| Image Captioning | Deep learning-based automatic image captioning | PyTorch CNN LSTM |
| License Plate Recognition | Automatic license plate detection and OCR | OpenCV OCR Python |
| Text Summarization β BART | Abstractive text summarization with BART | BART Transformers HuggingFace |
| Anime Title Generator β Flan-T5 | Fine-tuned Flan-T5 for creative anime title generation | Flan-T5 HuggingFace |
| Research Paper Title Generator β BART | Fine-tuned BART for academic title generation | BART Transformers |
| Movie Title Generator β Flan-T5 | Flan-T5 fine-tuned for cinematic title generation | Flan-T5 HuggingFace |
| Predicting Credit Card Approvals | ML classifier for credit card approval prediction | scikit-learn Pandas |
| Google Play Store Trend Analysis | EDA and trend analysis of Play Store app data | Pandas Matplotlib Seaborn |
| RAG Implementation & Prompt Optimization | Benchmarking and optimizing RAG prompt strategies | RAG LLMs Python |
Published models & datasets on Hugging Face
| Resource | Link |
|---|---|
| 𧬠Drug-Protein Interaction Model | yashchindam/Drug-Protein-Interaction-Prediction-Using-CLIP-and-Deep-Learning |
| π¦ Drug-Protein Dataset | datasets/yashchindam/Drug-Protein-Interaction-Prediction-Using-CLIP-and-Deep-Learning |

