Design Patterns of Deep Learning with TensorFlow

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.

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.

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.

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

Thomas V Joseph is a seasoned professional with over 23 years of extensive experience. He currently leads the Data Science and Analytics function at Hogarth Worldwide, focusing on building creative analytics data products for multiple brands. He has diverse industry background spanning Telecom, Healthcare, Retail, Manufacturing, Edtech, Infrastructure, and Fintech, and is a known author of published works on AI/ML. He excels in developing AI/ML strategic plans, cutting-edge solutions, and building teams for technology competitiveness. His research interests include Autonomous vehicles and Causal AI, where he explores areas such as obstacle detection, road and lane detection, LiDAR-3D Point cloud, causal graphs, causal interventions, and do-calculus.

In addition to his technical expertise, Thomas possesses strong soft skills, including strategic leadership, product design thinking, and research and development. He is adept at enhancing business engagements for Data Science initiatives, partnering with customers to architect solutions, spearheading delivery teams, and leading R&D efforts to build new solutions.