The world’s oceans are changing at a drastic pace. Beneath the waves and along the coasts, climate change and environmental degradation have spurred the most radical transformations in human history. In response, the people who know the ocean most intimately are taking action for the sake of our shared future. Community scientists track species in California tidepools. Researchers dive into the waters around Sydney to replant kelp forests. Scientists and First Nations communities collaborate to restore clam gardens in the Pacific Northwest. In At Every Depth, the oceanographer Tessa Hill and the science journalist Eric Simons profile these and other efforts to understand and protect marine environments, taking readers to habitats from shallow tidepools to the deep sea. They delve into the many human connections to the ocean—how people live with and make their living from the waters—journeying to places as far-flung as coral reefs, the Great Pacific Garbage Patch, and the Arctic and Antarctic poles. At Every Depth shares the stories of people from all walks of life, including scientists, coastal community members, Indigenous people, shellfish farmers, and fisheries workers. It brings together varied viewpoints, showing how scientists’ research and local and Indigenous knowledge can complement each other to inform a more sustainable future. Poignantly written and grounded in science, this book offers a narrative perspective on the changing oceans, letting us see how our relationships to the oceans are changing too.

Programming With Python - 4 BOOK BUNDLE!! Deep Learning with Keras Here Is a Preview of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models Activation functions Handwritten recognition using MNIST Solving multi-class classification problems Recurrent neural networks and sequence classification And much more... Convolutional Neural Networks in Python Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Python Machine Learning Here Is A Preview Of What You’ll Learn Here… Basics behind machine learning techniques Different machine learning algorithms Fundamental machine learning applications and their importance Getting started with machine learning in Python, installing and starting SciPy Loading data and importing different libraries Data summarization and data visualization Evaluation of machine learning models and making predictions Most commonly used machine learning algorithms, linear and logistic regression, decision trees support vector machines, k-nearest neighbors, random forests Solving multi-clasisfication problems Data visualization with Matplotlib and data transformation with Pandas and Scikit-learn Solving multi-label classification problems And much, much more... Machine Learning With TensorFlow Here Is a Preview of What You’ll Learn Here… What is machine learning Main uses and benefits of machine learning How to get started with TensorFlow, installing and loading data Data flow graphs and basic TensorFlow expressions How to define your data flow graphs and how to use TensorBoard for data visualization Main TensorFlow operations and building tensors How to perform data transformation using different techniques How to build high performance data pipelines using TensorFlow Dataset framework How to create TensorFlow iterators Creating MNIST classifiers with one-hot transformation Get this book bundle NOW and SAVE money!

Here is an accessible, algorithmically oriented guide to some of the most interesting techniques of complexity theory. The book shows that simple algorithms are at the heart of complexity theory. The book is organized by technique rather than by topic. Each chapter focuses on one technique: what it is, and what results and applications it yields.

This book constitutes the refereed proceedings of the 39th Computer Graphics International Conference on Advances in Computer Graphics, CGI 2022, held Virtually, during September 12–16, 2022. The 45 full papers included in this book were carefully reviewed and selected from 139 submissions. They were organized in topical sections as follows: image analysis & processing; graphs & networks; estimation & feature matching; 3d reconstruction; rendering & animation; detection & recognition; colors, paintings & layout; synthesis & generation; ar & user interfaces; medical imaging; segmentation; object detection; image attention & perception; and modeling & simulation.

These proceedings of the World Congress 2006, the fourteenth conference in this series, offer a strong scientific program covering a wide range of issues and challenges which are currently present in Medical physics and Biomedical Engineering. About 2,500 peer reviewed contributions are presented in a six volume book, comprising 25 tracks, joint conferences and symposia, and including invited contributions from well known researchers in this field.

This book constitutes the thoroughly referred post-proceedings of the 21st International Workshop on Combinatorial Algorithms, IWOCA 2010, held in London, UK, in July 2010. The 31 revised full papers presented together with extended abstracts of 8 poster presentations were carefully reviewed and selected from a total of 85 submissions. A broad variety of combinatorial graph algorithms for the computations of various graph features are presented; also algorithms for network compuation, approximation, computational geometry, games, and search are presented and complexity aspects of such algorithms are discussed.

This book constitutes the proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization”.