Products related to Patterns:
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Teaching, Tutoring and Training in the Lifelong Learning Sector
This core text provides comprehensive support for pre-service and in-service trainee teachers in the Lifelong Learning Sector covering all they need to know to achieve QTLS status. Supporting trainees through all stages of their professional development, the text takes the reader through the theoretical background underpinning teaching and learning and offers practical guidance on day-to-day challenges. This fourth edition has been fully revised and updated and includes a new chapter on teaching practice with notes on observation and lesson planning.New information on behaviour management has been added to support trainees in an aspect of teaching that many find challenging.
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Deep Learning Design Patterns
Deep learning has revealed ways to create algorithms for applications that we never dreamed were possible.For software developers, the challenge lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Design Patterns is here to help.In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects.Written by Google deep learning expert Andrew Ferlitsch, it's filled with the latest deep learning insights and best practices from his work with Google Cloud AI.Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. about the technologyYou don't need to design your deep learning applications from scratch!By viewing cutting-edge deep learning models as design patterns, developers can speed up their creation of AI models and improve model understandability for both themselves and other users. about the book Deep Learning Design Patterns distills models from the latest research papers into practical design patterns applicable to enterprise AI projects.Using diagrams, code samples, and easy-to-understand language, Google Cloud AI expert Andrew Ferlitsch shares insights from state-of-the-art neural networks.You'll learn how to integrate design patterns into deep learning systems from some amazing examples, including a real-estate program that can evaluate house prices just from uploaded photos and a speaking AI capable of delivering live sports broadcasting.Building on your existing deep learning knowledge, you'll quickly learn to incorporate the very latest models and techniques into your apps as idiomatic, composable, and reusable design patterns. what's inside Internal functioning of modern convolutional neural networksProcedural reuse design pattern for CNN architecturesModels for mobile and IoT devicesComposable design pattern for automatic learning methodsAssembling large-scale model deploymentsComplete code samples and example notebooksAccompanying YouTube videos about the readerFor machine learning engineers familiar with Python and deep learning. about the author Andrew Ferlitsch is an expert on computer vision and deep learning at Google Cloud AI Developer Relations.He was formerly a principal research scientist for 20 years at Sharp Corporation of Japan, where he amassed 115 US patents and worked on emerging technologies in telepresence, augmented reality, digital signage, and autonomous vehicles.In his present role, he reaches out to developer communities, corporations and universities, teaching deep learning and evangelizing Google's AI technologies.
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Distributed Machine Learning Patterns
Practical patterns for scaling machine learning from your laptop to a distributed cluster. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projectsConstruct machine learning pipelines with data ingestion, distributed training, model serving, and moreAutomate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo WorkflowsMake trade offs between different patterns and approachesManage and monitor machine learning workloads at scale Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In Distributed Machine Learning Patterns, you'll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well.Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows.Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters.In it, you'll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well.Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows.Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. about the technology Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners.Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations.In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure. about the book Distributed Machine Learning Patterns is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud.Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic.Real-world scenarios provide clear examples of how to apply each pattern, alongside the potential trade offs for each approach.Once you've mastered these cutting edge techniques, you'll put them all into practice and finish up by building a comprehensive distributed machine learning system.
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Teaching as a Design Science : Building Pedagogical Patterns for Learning and Technology
Teaching is changing. It is no longer simply about passing on knowledge to the next generation.Teachers in the twenty-first century, in all educational sectors, have to cope with an ever-changing cultural and technological environment.Teaching is now a design science. Like other design professionals – architects, engineers, programmers – teachers have to work out creative and evidence-based ways of improving what they do.Yet teaching is not treated as a design profession. Every day, teachers design and test new ways of teaching, using learning technology to help their students.Sadly, their discoveries often remain local. By representing and communicating their best ideas as structured pedagogical patterns, teachers could develop this vital professional knowledge collectively. Teacher professional development has not embedded in the teacher’s everyday role the idea that they could discover something worth communicating to other teachers, or build on each others’ ideas.Could the culture change? From this unique perspective on the nature of teaching, Diana Laurillard argues that a twenty-first century education system needs teachers who work collaboratively to design effective and innovative teaching.
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What knitting patterns or crochet patterns are there?
There are countless knitting and crochet patterns available, ranging from simple scarves and hats to intricate sweaters and blankets. Some popular knitting patterns include cable knit, lace, and fair isle, while common crochet patterns include granny squares, amigurumi, and filet crochet. Additionally, there are patterns for accessories like socks and mittens, as well as home decor items such as pillows and throws. With the wide variety of patterns available, crafters of all skill levels can find something to suit their interests and abilities.
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What are interference patterns?
Interference patterns are a phenomenon that occurs when two or more waves overlap and interact with each other. This interaction results in the waves either reinforcing each other (constructive interference) or canceling each other out (destructive interference). The resulting pattern is a series of alternating bright and dark bands, which can be observed in various wave phenomena such as light, sound, and water waves. Interference patterns are a key concept in understanding wave behavior and are used in various applications such as in interferometry and diffraction.
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What are resulting patterns?
Resulting patterns are the observable outcomes or trends that emerge from a particular set of circumstances or actions. These patterns can be seen in various aspects of life, such as behavior, nature, or data. By analyzing resulting patterns, we can gain insights into underlying processes, make predictions, or understand the impact of certain factors on a system. Identifying resulting patterns can help us make informed decisions and better navigate complex situations.
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What are geometric patterns?
Geometric patterns are repetitive designs or motifs that are created using geometric shapes such as lines, circles, triangles, squares, and other geometric elements. These patterns can be found in various forms of art, architecture, textiles, and nature. Geometric patterns are characterized by their symmetry, order, and precision, and they are often used to create visually appealing and harmonious compositions.
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Design Patterns of Deep Learning with TensorFlow : Building a customer hyper-personalisation ecosystem using deep learning design patterns
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The Learning Power Approach : Teaching Learners to Teach Themselves
Become mind-fit for life! Learning is learnable! Educators can explicitly teach not just content, knowledge and skills, but also positive learning attitudes and habits of mind.Guy Claxton’s The Learning Power Approach will help teachers understand how ‘every lesson, every day’ shapes the way students see themselves as learners.This is the beauty of the Learning Power Approach: it is something all teachers can integrate into their daily practice.Claxton’s work helps us notice ‘what lies beneath’ the surface of our teaching and attend more closely to the way we shape dispositions and attitudes.Readers will find: • approaches focused on building character alongside knowledge acquisition • cognitive, social and neuroscientific supportive evidence • practical illustrations and strategies Written by a renowned cognitive scientist, this book offers a ground-breaking set of design principles for strengthening students’ learning muscles. "This is the beauty of the Learning Power Approach.It is not a program, not a subject, not an add-on - it is something all teachers can integrate into their practice on a daily basis.Claxton’s work helps us notice ‘what lies beneath’ the surface of our teaching and to attend much more closely to the way we shape dispositions and attitudes to learning." Kath Murdoch, Education Consultant University of Melbourne, Melbourne, Australia
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The Learning Power Approach : Teaching learners to teach themselves
In The Learning Power Approach: Teaching learners to teach themselves Guy Claxton sets out the design principles of a pedagogical formula that aims to strengthen students' learning muscles and develop their independence, initiative, determination, and love of learning. Foreword by Carol S. Dweck. Learning is learnable! Educators can explicitly teach not just content, knowledge, and skills, but also the positive habits of mind that will better prepare students to flourish both in school and in later life. And as `traditionalists' fight for rigour and knowledge, and `progressives' defend the increasing focus on character and well-being, Guy Claxton's Learning Power Approach (LPA) brings resolution to this phoney and unnecessary war by offering teachers a win-win pedagogical formula that delivers good academic results while simultaneously turbocharging students' independence, initiative, and love of learning. In this groundbreaking book Guy distils fifteen years' experience with his influential Building Learning Power method to provide a set of design principles for strengthening students' learning muscles, and - together with a wealth of practical strategies and the supporting evidence that underpins them - details the small tweaks to daily practice that will help teachers attend more closely to the ways in which they can shape their students' learning dispositions and attitudes. Complemented by engaging and informative classroom examples of the LPA in action - and drawing from research into the fields of mindset, metacognition, grit, and collaborative learning - The Learning Power Approach describes in detail the suite of beliefs, values, attitudes, and habits of mind that go in to making up learning power, and offers a thorough explanation of what its intentions and guiding principles are.Furthermore, in order to help those who are just setting out on their LPA journey, Guy presents teachers with an attractive menu of customisable strategies and activities to choose from as they begin to embed the LPA principles into their own classroom culture, and also includes at the end of each chapter a "Wondering" section that serves to prompt reflection, conversation, and action among teachers. Suitable for teachers and leaders in all educational settings,The Learning Power Approach carefully lays the groundwork for a series of books to follow that are specifically tailored to primary teaching, secondary teaching, and school leadership.
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Reflective Teaching and Learning in Further Education
This book looks at critical reflection as a key skill for all teachers in further education (FE) and an important part of the new Professional Standards.In particular the text explores the key themes of self-awareness, planning, managing behaviour and CPD in relation to reflective practice to demonstrate how it can support those areas of teaching that most often cause concern.The limitations and benefits of reflection are analysed and action research is identified as an important facet in developing professional reflective practice which can in turn enhance both the personal and professional life of FE teachers. Â
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What are design patterns?
Design patterns are reusable solutions to common problems that arise during software development. They provide a way to standardize and communicate best practices in software design and architecture. By using design patterns, developers can create more maintainable, flexible, and scalable code. Design patterns are not specific to a particular programming language or technology, making them widely applicable across different software development contexts.
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What are behavioral patterns?
Behavioral patterns refer to the recurring ways in which individuals or groups of people behave in certain situations. These patterns can include habits, routines, reactions, and responses to various stimuli. They are often influenced by a combination of internal factors such as personality, emotions, and beliefs, as well as external factors such as social norms, cultural influences, and environmental conditions. By recognizing and understanding behavioral patterns, individuals can gain insight into their own actions and those of others, and can potentially make positive changes to their behavior.
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What are intermediate inheritance patterns?
Intermediate inheritance patterns refer to genetic traits that are not expressed as a simple dominant or recessive trait, but rather show a blending of the two. In this pattern, the heterozygous individual (having one dominant and one recessive allele) will display a phenotype that is intermediate between the two homozygous phenotypes. This means that the trait is not fully dominant or fully recessive, but rather falls somewhere in between. Examples of intermediate inheritance patterns include incomplete dominance and codominance.
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What are strange search patterns?
Strange search patterns refer to unusual or unexpected trends in the way people conduct online searches. This could include sudden spikes in searches for a particular topic, unusual combinations of keywords, or a surge in searches related to a specific event or phenomenon. These patterns can provide insights into current events, public interest, or emerging trends, and are often analyzed by researchers, marketers, and data analysts to understand and respond to changing consumer behavior.
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