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The Deep Learning Architect's Handbook

You're reading from   The Deep Learning Architect's Handbook Build and deploy production-ready DL solutions leveraging the latest Python techniques

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781803243795
Length 516 pages
Edition 1st Edition
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Author (1):
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Ee Kin Chin Ee Kin Chin
Author Profile Icon Ee Kin Chin
Ee Kin Chin
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Table of Contents (25) Chapters Close

Preface 1. Part 1 – Foundational Methods
2. Chapter 1: Deep Learning Life Cycle FREE CHAPTER 3. Chapter 2: Designing Deep Learning Architectures 4. Chapter 3: Understanding Convolutional Neural Networks 5. Chapter 4: Understanding Recurrent Neural Networks 6. Chapter 5: Understanding Autoencoders 7. Chapter 6: Understanding Neural Network Transformers 8. Chapter 7: Deep Neural Architecture Search 9. Chapter 8: Exploring Supervised Deep Learning 10. Chapter 9: Exploring Unsupervised Deep Learning 11. Part 2 – Multimodal Model Insights
12. Chapter 10: Exploring Model Evaluation Methods 13. Chapter 11: Explaining Neural Network Predictions 14. Chapter 12: Interpreting Neural Networks 15. Chapter 13: Exploring Bias and Fairness 16. Chapter 14: Analyzing Adversarial Performance 17. Part 3 – DLOps
18. Chapter 15: Deploying Deep Learning Models to Production 19. Chapter 16: Governing Deep Learning Models 20. Chapter 17: Managing Drift Effectively in a Dynamic Environment 21. Chapter 18: Exploring the DataRobot AI Platform 22. Chapter 19: Architecting LLM Solutions 23. Index 24. Other Books You May Enjoy

Preparing data with DataRobot

The first part of what the platform offers is the data preparation component. DataRobot simplifies the data preparation process by offering a range of features to streamline data ingest, cleaning, transformation, and integration. Let’s dive into these features in detail.

Ingesting data for deep learning model development

The development of deep learning models in DataRobot begins with the pivotal step of data ingestion. This process allows you to directly import your data from various sources, including cloud storage (such as AWS S3), Google Cloud Storage, local files, or databases such as PostgreSQL, Oracle, and SQL Server. The platform accepts diverse file formats, including CSV, XLSX, and ZIP files. Additionally, the platform supports image, text, document, geospatial, numerical, categorical, and summarized categorical data through secondary datasets as input data types. For the target data types, the platform supports numerical, categorical...

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