The Deep Learning Architect's Handbook : Build and deploy production-ready DL solutions leveraging the latest Python techniques - Ee Kin Chin

The Deep Learning Architect's Handbook

Build and deploy production-ready DL solutions leveraging the latest Python techniques

By: Ee Kin Chin

eBook | 29 December 2023

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Harness the vast power of deep learning to drive productivity and efficiency with the latest technology using this practical guide covering the entire deep learning life cycle

Key Features

  • Exploit the power of deep learning to drive productivity in your business
  • Get hands-on experience of every step of the deep learning life cycle
  • Take your deep learning skills to the next level with this practical guide

Book Description

The potential of deep learning is limitless, but it is not easy to quickly become efficient and effective in this complex and ever-changing domain. What you need is a practical book that blasts through the theory and focuses on the practicalities of designing and deploying deep learning models in a business context. Deep Learning Architect is that book. We begin with a brief overview of the fundamental concepts as a refresher, but once that's out of the way, we forge ahead into the endless possibilities deep learning provides, with examples that use image, audio, text, and video data. We explore the main deep learning architectures as well as how to effectively evaluate the performance of your model so you can tweak it to perfection. As always, security is critical, so we also cover adversarial analysis. It's tempting to think that your work is done there, but knowing how to deploy and maintain your models in production is crucial in the real world, and the book ends with a thorough examination of this practical necessity. By the end of this book, you will be able to build complex deep learning models and structure your data for the purposes of deep learning, and verify and explain your models transparently and understandably.

What you will learn

  • Use Neural Architecture Search (NAS) to automate the design of ANNs
  • Use RNNs, CNNs, BERT, transformers, and more to build your model
  • Deal with multi-modal data drift
  • Evaluate the quality and bias of your model
  • Protect your model from adversarial attacks
  • Deploy a model with DataRobot AutoML

Who This Book Is For

Deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. The audience of this book are professionals in the deep learning and AI space who are going to use the knowledge in their business use cases.

Table of Contents

  1. Deep Learning Life Cycle
  2. Designing Deep Learning Architectures
  3. Understanding Convolutional Neural Networks
  4. Understanding Recurrent Neural Networks
  5. Understanding Autoencoders
  6. Understanding Neural Network Transformers
  7. Deep Neural Architecture Search
  8. Neural Network Interpretation
  9. Bias, Fairness and Trust
  10. Adversarial Analysis
  11. Deploying Deep Learning Models in Production
  12. Multimodal Data Drift
  13. Deep Learning Model Monitoring and Retraining
  14. Tackling a new use case
  15. Tackling a new use case with DataRobot AutoML
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