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.
As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications.
By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.
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. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.
Table of Contents
- Deep Learning Life Cycle
- Designing Deep Learning Architectures
- Understanding Convolutional Neural Networks
- Understanding Recurrent Neural Networks
- Understanding Autoencoders
- Understanding Neural Network Transformers
- Deep Neural Architecture Search
- Exploring Supervised Deep Learning
- Exploring Unsupervised Deep Learning
- Exploring Model Evaluation Methods
- Explaining Neural Network Predictions
- Interpreting Neural Network
- Exploring Bias and Fairness
- Analyzing Adversarial Performance
- Deploying Deep Learning Models in Production
(N.B. Please use the Look Inside option to see further chapters)
Ee Kin Chin is a Senior Deep Learning Engineer at DataRobot. He holds a Bachelor of Engineering (Honours) in Electronics with a major in Telecommunications. Ee Kin is an expert in the field of Deep Learning, Data Science, Machine Learning, Artificial Intelligence, Supervised Learning, Unsupervised Learning, Python, Keras, Pytorch, and related technologies. He has a proven track record of delivering successful projects in these areas and is dedicated to staying up to date with the latest advancements in the field.
