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Transformers from Scratch
The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.
A study on Attention mechanism. This study shows the impact, evolution… | by Nilesh Barla | PerceptronAI | Medium
Encoder Decoder Models for Dummies | by Yujian Tang | Plain Simple Software | Medium
Attention Seq2Seq with PyTorch: learning to invert a sequence | by Benjamin Etienne | TDS Archive | Medium
The Easiest Way to Understand Self-Attention with Code. | by Dhananjay Kumar | Medium
Understanding Transformers: A Step-by-Step Math Example — Part 1 | by Fareed Khan | Medium
Solving Transformer by Hand: A Step-by-Step Math Example | by Fareed Khan | Level Up Coding
Transformer: The Self-Attention Mechanism | by Sudipto Baul | Machine Intelligence and Deep Learning | Medium
LSTM for Time Series Prediction in PyTorch - MachineLearningMastery.com
NLP From Scratch: Classifying Names with a Character-Level RNN — PyTorch Tutorials 2.8.0+cu128 documentation
NLP From Scratch: Generating Names with a Character-Level RNN — PyTorch Tutorials 2.8.0+cu128 documentation
NLP From Scratch: Translation with a Sequence to Sequence Network and Attention — PyTorch Tutorials 2.8.0+cu128 documentation
bentrevett/pytorch-seq2seq: Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
pytorch-seq2seq/1 - Sequence to Sequence Learning with Neural Networks.ipynb at main · bentrevett/pytorch-seq2seq
pytorch-seq2seq/2 - Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.ipynb at main · bentrevett/pytorch-seq2seq
pytorch-seq2seq/3 - Neural Machine Translation by Jointly Learning to Align and Translate.ipynb at main · bentrevett/pytorch-seq2seq
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