The Deep Learning Architect’s Handbook
Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle.
Key Features:
- Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions.
- Gain hands-on experience in every step of the deep learning life cycle.
- Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations.
- Purchase of the print or Kindle book includes a free PDF eBook.
Book Description:
Deep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives.
This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions.
You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency.
What You Will Learn:
- Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs).
- Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model.
- Deal with multi-modal data drift in a production environment.
- Evaluate the quality and bias of your models.
- Explore techniques to protect your model from adversarial attacks.
- Get to grips with deploying a model with DataRobot AutoML.
Who This Book Is For:
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. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.