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Transformer Encoder from Scratch

data science & mlAdvanced365d access
₹199onwards

Implement a full Transformer encoder — multi-head attention, positional encoding, layer norm — in PyTorch from scratch. Train on a classification task. No HuggingFace shortcuts.

  • Implement scaled dot-product attention and multi-head attention from scratch in PyTorch
  • Build sinusoidal positional encoding and understand why position matters in Transformers
  • Assemble a complete Transformer encoder block with residual connections and layer norm
  • Train an encoder classifier end-to-end on a real text classification dataset
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Fine-tuning Data Preparation Pipeline

data science & mlAdvanced365d access
₹199onwards

Scrape content, clean it, auto-generate instruction-response pairs using an LLM, score quality with an evaluator model, and output a production-ready JSONL dataset.

  • Build an async web scraping pipeline using httpx and BeautifulSoup
  • Clean, deduplicate, and validate raw text content at scale
  • Auto-generate instruction-response training pairs using an LLM
  • Score dataset quality using an LLM judge and apply rule-based filters
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