Die kleine Madeleine hat einen riesengroßen Herzenswunsch, den ihr wirklich nur der Weihnachtsmann erfüllen kann. Natürlich wünscht sie sich, wie jedes andere Kind auch, zu Weihnachten Spielsachen, Plätzchen und Süßigkeiten, aber sie wünscht sich auch noch etwas ganz Besonderes. Etwas, was ihr niemand sonst erfüllen kann. Daher bittet sie ihren Schutzengel, ihren Herzenswunsch dem Weihnachtsmann zukommen zu lassen. Wird der Weihnachtsmann dem kleinen Mädchen diesen Wunsch erfüllen? Welches Wundervolle Abenteuer wird sie dabei wohl erleben? ________________________________________________________________________ Small Madeleine has one very big heart-felt wish for Christmas, which only Santa Claus is able to fulfill. As well as every other child, she ́s wishing toys, biscuits, cookies and sweets for Christmas, too. But she also has one very special wish, which nobody else can fulfill. So she asks her guardian angel to get her heart-felt wish to Santa Claus. Will Santa fulfill her request? What wonderful adventure will be waiting for her?

Guy Maddin is Canada's most iconoclastic filmmaker. Through his reinvention of half-forgotten film genres, his remobilization of abandoned techniques from the early history of cinema, and his unique editing style, Maddin has created a critically successful body of work that looks like nothing else in Canadian film. My Winnipeg (2008), which Roger Ebert called one of the ten best films of the first decade of the twenty-first century, has consolidated Maddin's international reputation. In this sixth volume of the Canadian Cinema series, Darren Wershler argues that Maddin's use of techniques and media that fall outside of the normal repertoire of contemporary cinema require us to re-examine what we think we know about the documentary genre and even 'film' itself. Through an exploration of My Winnipeg's major thematic concerns - memory, the cultural archive, and how people and objects circulate through the space of the city - Wershler contends that the result is a film that is psychologically and affectively true without being historically accurate.

Dive deeper into neural networks and get your models trained, optimized with this quick reference guide Key Features A quick reference to all important deep learning concepts and their implementations Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow. Book Description Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks. What you will learn Solve regression and classification challenges with TensorFlow and Keras Learn to use Tensor Board for monitoring neural networks and its training Optimize hyperparameters and safe choices/best practices Build CNN's, RNN's, and LSTM's and using word embedding from scratch Build and train seq2seq models for machine translation and chat applications. Understanding Deep Q networks and how to use one to solve an autonomous agent problem. Explore Deep Q Network and address autonomous agent challenges. Who this book is for If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.

This book provides an overview of the current state of the art in wireless networks around the globe, focusing on utilizing the latest artificial intelligence and soft computing techniques to provide design frameworks for wireless networks. These techniques play a vital role in developing a more robust algorithm suitable for the dynamic and heterogeneous environment, making the network self-managed, self-operational, and self-configurational, and efficiently reducing uncertainties and imprecise information.

Get Back to Basics with the core information you need to succeed. Learn what is important to know in Woodworker's Guide to Joinery from Butt Joints, Miter Joints, and Lap and Groove Joints, to Mortise-and-Tenon, Dovetail, and Box Joints. Learn how to master the various families of joinery - from themost basic butt joints to half-blind dovetails, tongue-and -groove joints, and beyond.

Fun robotics projects that teach kids to make, hack, and learn! There's no better way for kids to learn about the world around them than to test how things work. Building Your Own Robots presents fun robotics projects that children aged 7 – 11 can complete with common household items and old toys. The projects introduce core robotics concepts while keeping tasks simple and easy to follow, and the vivid, full-color graphics keep your kid's eyes on the page as they work through the projects. Brought to you by the trusted For Dummies brand, this kid-focused book offers your child a fun and easy way to start learning big topics! They'll gain confidence as they design and build a self-propelled vehicle, hack an old remote control car to create a motorized robot, and use simple commands to build and program a virtual robot—all while working on their own and enjoying a sense of accomplishment! Offers a kid-friendly design that is heavy on eye-popping graphics Focuses on basic projects that set your child on the road to further exploration Boasts a small, full-color, accessible package that instills confidence in the reader Introduces basic robotics concepts to kids in a language they can understand If your youngster loves to tinker, they'll have a whole lot of fun while developing their creative play with the help of Building Your Own Robots.

Despite popular forays into augmented and virtual reality in recent years, spatial computing still sits on the cusp of mainstream use. Developers, artists, and designers looking to enter this field today have few places to turn for expert guidance. In this book, Erin Pangilinan, Steve Lukas, and Vasanth Mohan examine the AR and VR development pipeline and provide hands-on practice to help you hone your skills. Through step-by-step tutorials, you’ll learn how to build practical applications and experiences grounded in theory and backed by industry use cases. In each section of the book, industry specialists, including Timoni West, Victor Prisacariu, and Nicolas Meuleau, join the authors to explain the technology behind spatial computing. In three parts, this book covers: Art and design: Explore spatial computing and design interactions, human-centered interaction and sensory design, and content creation tools for digital art Technical development: Examine differences between ARKit, ARCore, and spatial mapping-based systems; learn approaches to cross-platform development on head-mounted displays Use cases: Learn how data and machine learning visualization and AI work in spatial computing, training, sports, health, and other enterprise applications