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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

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
NER: Definition
NER with BERT in PyTorch

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