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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

Transformer Architecture and LLMs Part 1

What You Will Learn

Transformer model basics

Content Covered

Introduces the Transformer architecture.

Resources

The Illustrated Transformer
Is Attention explanation?
Annotated Transformer
Annotated Transformer Video List
Positional Encoding Blog (Kazemnejad)
Positional Encoding Blog (TowardsDataScience Part I)
Positional Encoding Blog (TowardsDataScience Part II)
Chris McCormick AI videos
Illustrated BERT

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