Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples.
The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0....
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms...
This book provides an introduction to recent advances in theory, algorithms and application of Boolean map distance for image processing. Applications include modeling what humans find salient or prominent in an image, and then using this for guiding smart image cropping, selective image filtering, image segmentation, image matting, etc....
This book systematically classifies the mathematical formalisms of computational models that are required for solving problems in mathematics, engineering and various other disciplines. It also provides numerical methods for solving these problems using suitable algorithms and for writing computer codes to find solutions. For discrete...
Now revised and expanded with over 50% new material, this definitive clinical reference is the text of choice for graduate-level courses in evidence-based psychotherapy. Foremost authorities describe the conceptual and scientific foundations of cognitive-behavioral therapy (CBT) and provide a framework for assessment and case...
This book is about quantum computing and quantum algorithms. The book starts with a chapter introducing the basic rules of quantum mechanics and how they can be used to build quantum circuits and perform computations.
Further, Grover's algorithm is presented for unstructured search discussing its consequences and...
