Demystifying the Transformer: Step‑by‑Step PaddlePaddle Implementation

This article provides a comprehensive, code‑rich walkthrough of the Transformer architecture using PaddlePaddle, covering the encoder and decoder components, residual connections, layer normalization, feed‑forward networks, scaled dot‑product and multi‑head attention, and shows how to assemble the full model with training and inference functions.

Attention MechanismDecoderDeep Learning

0 likes · 17 min read

Demystifying the Transformer: Step‑by‑Step PaddlePaddle Implementation

Nightwalker Tech

Jul 19, 2023 · Artificial Intelligence

Step‑by‑Step Implementation of Transformer Blocks, Attention, Normalization, Feed‑Forward, Encoder and Decoder in PyTorch

This article provides a comprehensive tutorial on building the core components of a Transformer model—including multi‑head attention, layer normalization, feed‑forward networks, encoder and decoder layers—and assembles them into a complete PyTorch implementation, supplemented with explanatory diagrams and runnable code.

DecoderDeep LearningEncoder

0 likes · 13 min read

Step‑by‑Step Implementation of Transformer Blocks, Attention, Normalization, Feed‑Forward, Encoder and Decoder in PyTorch

Rare Earth Juejin Tech Community

Sep 28, 2022 · Artificial Intelligence

EGBAD: Efficient GAN‑Based Anomaly Detection – Theory and Practical Implementation

This article introduces the EGBAD model, an efficient GAN‑based anomaly detection method that replaces AnoGAN's costly latent variable search with an encoder, provides detailed PyTorch code for data loading, model construction, training, and inference, and compares its testing speed with AnoGAN.

DiscriminatorEGBADEncoder

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EGBAD: Efficient GAN‑Based Anomaly Detection – Theory and Practical Implementation

TiPaiPai Technical Team

Jun 18, 2021 · Artificial Intelligence

Mastering Text Recognition: Encoder & Decoder Strategies Explained

This article reviews modern text‑recognition systems, detailing how encoders such as CNN, CNN‑BiLSTM, and Transformer‑based models extract visual features, and how decoders like Position Attention, Transformer decoders, and RNN Seq2Seq align variable‑length text, while also discussing CTC loss and practical design choices.

CNNCTCDecoder

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Mastering Text Recognition: Encoder & Decoder Strategies Explained