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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

Overview of LLM solutions

LLMs excel in diverse tasks such as answering questions, machine translation, language modeling, sentiment analysis, and text summarization. They generate unstructured text but can be guided to produce structured output. LLM solutions harness this ability and leverage custom data from knowledge bases to create targeted, valuable outcomes for organizations and individuals. By properly streamlining processes and enhancing output quality objectively, an LLM solution can unlock the true potential of LLM-generated content, making it more powerful and practical across applications.

The increasing accessibility of LLMs with pre-trained world knowledge has played a significant role in making these benefits more attainable for a broader audience. Thanks to various LLM providers and open source platforms, organizations and developers can now more easily adopt and integrate LLMs into their workflows. Prominent LLM providers, such as OpenAI (GPT-4 or GPT-3.5), Microsoft...

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