Achine Learning Design Patterns
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Machine Learning Design Patterns
Author: Valliappa Lakshmanan
language: en
Publisher: O'Reilly Media
Release Date: 2020-10-15
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Machine Learning Design Patterns
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly.
Design Patterns of Deep Learning with TensorFlow
Architecting AI: Design patterns for building deep learning products KEY FEATURES ● Master foundational concepts in design patterns of deep learning. ● Benefit from practical insights shared by an industry professional. ● Learn to build data products using deep learning. DESCRIPTION Design Patterns of Deep Learning with TensorFlow is your comprehensive guide to learning deep learning from a design pattern perspective. In this book, we explore deep learning within the context of building hyper-personalization models, exploring its applications across various industries and scenarios. It starts by showing how deep learning enhances retail through customer segmentation and data analysis. You will learn neural networks, computer vision with CNNs, and NLP for analyzing customer behavior. This book addresses challenges like uneven data and optimizing models with techniques like backpropagation, hyperparameter tuning, and transfer learning. Finally, it covers setting up data pipelines and deploying your system. With practical tips and actionable advice, this book equips readers with the skills and strategies needed to thrive in today's competitive AI landscape. By the end of this book, you will be equipped with the knowledge and practical skills to build and deploy deep learning-powered hyper-personalization systems that deliver exceptional customer experiences. WHAT YOU WILL LEARN ● Understand about hyper-personalized AI models for tailored user experiences. ● Design principles of computer vision and NLP models. ● Inner working of transformers equipping readers to understand the intricacies of generative AI and large language models (LLMs) like ChatGPT. ● To get the best out of deep learning models through hyperparameter tuning and transfer learning. ● Learn how to build deployment pipelines to serve models into production environments seamlessly. WHO THIS BOOK IS FOR This book caters to both beginners and experienced practitioners in the field of data science and Machine Learning. Through practical examples, it simplifies complex ideas, linking them to design patterns. TABLE OF CONTENTS 1. Customer Hyper-personalization 2. Introduction to Design Patterns and Neural Networks 3. Design Patterns in Visual Representation Learning 4. Design Patterns for Non-Visual Representation Learning 5. Design Patterns for Transformers 6. Data Distribution Challenges and Strategies 7. Model Training Philosophies 8. Hyperparameter Tuning 9. Transfer Learning 10. Setting Up Data and Deployment Pipelines