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Deep Learning

Before starting to learn Deep Learning, make sure to first learn basics of Machine Learning

ANN basics Part 1 ANN basics Part 2 ANN basics Part 3 ANN basics Part 4 Advanced ANN Part 1 Advanced ANN Part 2 Advanced ANN Part 3 Advanced ANN Part 4 Advanced ANN Playlist Advanced ANN Part 5 Advanced ANN Part 6
Boosting Algorithms Part 1 Boosting Algorithms Part 2 Boosting Algorithms Part 3 Boosting Algorithms Part 4
Basic CNN Model Part 1 Basic CNN Model Part 2 Basic CNN Model Part 3 Basic CNN Model Part 4 Backprop in CNN
CNN Architectures Part 1
Autoencoders Part 1 GANs Part 1
Practical Considerations Part 1
RNN & LSTM Theory Part 1
RNN & LSTM Practical Part 1
Transformer Architecture and LLMs Part 1
LLM Pre-Training Part 1 LLM Pre-Training Part 2 LLM Pre-Training Part 3 LLM Pre-Training Part 4 LLM Pre-Training Part 5
Fine Tuning LLMs Part 1 Fine Tuning LLMs Part 2
RAG Part 1 RAG Part 2
Image Captioning, Text to Image Part 1 Image Captioning, Text to Image Part 2
Deployment Part 1

RAG Part 1

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What You Will Learn

  • Introduction to RAG

Content Covered

Covers the basics of Retrieval Augmented Generation.

Resources

  • Sentence Similarity using BERT and SBERT (from playlist)
  • Limitation of IR using LLMs (from playlist)
  • RAG using LangChain (from playlist)
  • Text Classification with Transformers
  • Sentiment Analysis with BERT and Hugging Face
  • Text Classification with Transformer Models (TowardsDataScience)
  • NER: Definition
  • NER with BERT in PyTorch
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