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The Deep Learning Architect's Handbook

You're reading from  The Deep Learning Architect's Handbook

Product type Book
Published in Dec 2023
Publisher Packt
ISBN-13 9781803243795
Pages 516 pages
Edition 1st Edition
Languages
Author (1):
Ee Kin Chin Ee Kin Chin
Profile icon Ee Kin Chin

Table of Contents (25) Chapters

Preface Part 1 – Foundational Methods
Chapter 1: Deep Learning Life Cycle Chapter 2: Designing Deep Learning Architectures Chapter 3: Understanding Convolutional Neural Networks Chapter 4: Understanding Recurrent Neural Networks Chapter 5: Understanding Autoencoders Chapter 6: Understanding Neural Network Transformers Chapter 7: Deep Neural Architecture Search Chapter 8: Exploring Supervised Deep Learning Chapter 9: Exploring Unsupervised Deep Learning Part 2 – Multimodal Model Insights
Chapter 10: Exploring Model Evaluation Methods Chapter 11: Explaining Neural Network Predictions Chapter 12: Interpreting Neural Networks Chapter 13: Exploring Bias and Fairness Chapter 14: Analyzing Adversarial Performance Part 3 – DLOps
Chapter 15: Deploying Deep Learning Models to Production Chapter 16: Governing Deep Learning Models Chapter 17: Managing Drift Effectively in a Dynamic Environment Chapter 18: Exploring the DataRobot AI Platform Chapter 19: Architecting LLM Solutions Index Other Books You May Enjoy

Decoding the original transformer architecture holistically

Before we look into the structure of the model, let’s talk about the basic intent of transformers.

As we covered in the previous chapter, transformers are also a family of architectures that utilize the concept of encoder and decoder. The encoder encodes data into what is known as the code and the decoder decodes the code into a data format that looks similar to raw, unprocessed data. The very first transformer used both the encoder and decoder concepts to build the entire architecture and demonstrated its application in text generation. The subsequent adaptations and improvements either used only the encoder or only the decoder to achieve different tasks. In a transformer, however, the encoder’s goal is not to compress the data to achieve a smaller and more compact representation of the data, but instead mainly to serve as a feature extractor. Additionally, the decoder’s goal for transformers is not...

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