Advanced Techniques In Optimization For Machine Learning And Imaging

Advanced Techniques in Optimization for Machine Learning and Imaging

In recent years, non-linear optimization has had a crucial role in the development of modern techniques at the interface of machine learning and imaging. The present book is a collection of recent contributions in the field of optimization, either revisiting consolidated ideas to provide formal theoretical guarantees or providing comparative numerical studies for challenging inverse problems in imaging. The work of these papers originated in the INdAM Workshop “Advanced Techniques in Optimization for Machine learning and Imaging” held in Roma, Italy, on June 20-24, 2022. The covered topics include non-smooth optimisation techniques for model-driven variational regularization, fixed-point continuation algorithms and their theoretical analysis for selection strategies of the regularization parameter for linear inverse problems in imaging, different perspectives on Support Vector Machines trained via Majorization-Minimization methods, generalization of Bayesian statistical frameworks to imaging problems, and creation of benchmark datasets for testing new methods and algorithms.

Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow

'Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow' is the definitive guide for data scientists, AI practitioners, and technology enthusiasts committed to optimizing their machine learning workflows. This meticulously crafted book offers an in-depth exploration of advanced machine learning operations (MLOps), with a strong focus on the practical deployment, monitoring, and management of machine learning models using TensorFlow and Kubeflow. The journey begins with an overview of machine learning fundamentals and the inner workings of TensorFlow. As readers progress, they delve deeper into data preprocessing, feature engineering, and model building, gradually mastering the complexities of fine-tuning and optimizing models for production readiness. The pivotal aspect of automating machine learning pipelines with Kubeflow is thoroughly examined, empowering readers to deploy TensorFlow models with utmost confidence. Furthermore, the book provides valuable insights into advanced TensorFlow techniques, ethical AI development, and model management with TensorFlow Serving, ensuring comprehensive coverage of key topics. 'Optimizing Machine Learning Pipelines: Advanced Techniques with TensorFlow and Kubeflow' is crafted to elevate its readers into proficient MLOps practitioners, adept at harnessing the power of TensorFlow and Kubeflow to deliver impactful AI solutions. Whether you are embarking on your first machine learning project or seeking to enhance your existing AI capabilities, this book is your essential resource for mastering advanced machine learning operations.

Advanced Computational Intelligence Methods for Processing Brain Imaging Data