Sujit Pal

Le voyage de l' ducation l'environnement a commenc en m me temps que le voyage de la civilisation. La cl d'une ducation environnementale r ussie est l'enseignant. Si l'enseignant n'a pas les connaissances, les comp tences et l'engagement n cessaires pour cologiser son programme, il est peu probable qu'il produise des l ves comp tents en mati re d'environnement. Les recherches men es sur cette question peuvent aider concevoir un programme de formation des enseignants qui puisse r pondre aux objectifs de l' ducation l'environnement. tant donn que les enseignants stagiaires d'aujourd'hui sont les enseignants de demain, il est n cessaire de savoir dans quelle mesure ils sont conscients de l'environnement et des probl mes environnementaux. La sensibilisation l'environnement et l'action en faveur de l'environnement varient-elles d'un stagiaire l'autre, en fonction des mati res enseign es ? Leur lieu de r sidence a-t-il un impact sur leur niveau de sensibilisation ou d'action ? Il a t mentionn maintes reprises que la connaissance de l'environnement ne suffit pas faire agir les gens. Il est tout aussi important de d velopper la volont d'agir. Il convient donc d' tudier en profondeur la motivation vis- -vis du comportement environnemental afin de comprendre l'action raisonn e des gens. C'est l que r side l'importance du pr sent ouvrage.

A jornada da Educa o Ambiental foi iniciada juntamente com a jornada da civiliza o. A chave do sucesso da educa o ambiental o professor. Se o professor n o tiver os conhecimentos, as compet ncias e o empenho necess rios para ambientalizar o seu curr culo, pouco prov vel que se produzam alunos com literacia ambiental. A investiga o realizada sobre esta quest o pode ajudar a conceber um programa de forma o de professores que possa cumprir os objectivos da educa o ambiental. Uma vez que os formandos de hoje s o os professores de amanh , necess rio saber at que ponto est o conscientes do ambiente e dos problemas ambientais. Se os formandos de diferentes reas disciplinares variam na sua consci ncia ou a o ambiental? Se o seu local de resid ncia tem algum impacto no seu n vel de sensibiliza o ou de a o? Tem sido repetidamente referido que o conhecimento do ambiente, por si s , n o suficiente para levar as pessoas a agir. igualmente importante desenvolver a vontade de agir. Assim, a motiva o em rela o ao comportamento ambiental deve ser estudada em profundidade para compreender a a o fundamentada das pessoas. aqui que reside a import ncia do presente livro.

Il viaggio dell'educazione ambientale iniziato insieme al viaggio della civilt . La chiave del successo dell'educazione ambientale l'insegnante in classe. Se gli insegnanti non hanno le conoscenze, le capacit e l'impegno per ambientalizzare il loro programma di studi, improbabile che si producano studenti alfabetizzati dal punto di vista ambientale. La ricerca condotta su questo tema pu aiutare a progettare un programma di formazione degli insegnanti in grado di raggiungere gli obiettivi dell'educazione ambientale. Poich gli insegnanti in formazione di oggi sono gli insegnanti di domani, necessario sapere fino a che punto sono consapevoli dell'ambiente e dei problemi ambientali. Se gli insegnanti in formazione provenienti da contesti disciplinari diversi variano nella loro consapevolezza o azione ambientale? Il luogo di residenza ha un impatto sul loro livello di consapevolezza o di azione? Pi volte stato detto che la conoscenza dell'ambiente da sola non sufficiente a far agire le persone. altrettanto importante sviluppare la volont di agire. Pertanto, la motivazione nei confronti del comportamento ambientale dovrebbe essere studiata in modo approfondito per comprendere l'azione motivata delle persone. Qui sta l'importanza del presente libro.

Die Reise der Umwelterziehung begann zusammen mit der Reise der Zivilisation. Der Schl ssel zu einer erfolgreichen Umwelterziehung ist der Klassenlehrer. Wenn der Lehrer nicht ber das Wissen, die F higkeiten und das Engagement verf gt, um seinen Lehrplan umweltfreundlich zu gestalten, ist es unwahrscheinlich, dass umweltbewusste Sch ler heranwachsen. Die zu diesem Thema durchgef hrten Untersuchungen k nnen bei der Gestaltung eines Lehrerausbildungsprogramms helfen, das die Ziele der Umwelterziehung erf llen kann. Da die Lehreranw rter von heute die Lehrer von morgen sind, muss man wissen, inwieweit sie sich der Umwelt und der Umweltprobleme bewusst sind. Ob sich das Umweltbewusstsein oder -handeln der Lehreranw rter aus verschiedenen Fachbereichen unterscheidet? Ob ihr Wohnort einen Einfluss auf ihr Umweltbewusstsein oder ihr Handeln hat? Es wurde immer wieder darauf hingewiesen, dass Umweltwissen allein nicht ausreicht, um Menschen zum Handeln zu bewegen. Ebenso wichtig ist es, den Willen zum Handeln zu entwickeln. Daher sollte die Motivation f r umweltbewusstes Verhalten eingehend untersucht werden, um das durchdachte Handeln der Menschen zu verstehen. Hierin liegt die Bedeutung des vorliegenden Buches.

Build cutting edge machine and deep learning systems for the lab, production, and mobile devices.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and deep learning techniquesBook DescriptionDeep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.What you will learnLearn how to use the popular GNNs with TensorFlow to carry out graph mining tasksDiscover the world of transformers, from pretraining to fine-tuning to evaluating themApply self-supervised learning to natural language processing, computer vision, and audio signal processingCombine probabilistic and deep learning models using TensorFlow ProbabilityTrain your models on the cloud and put TF to work in real environmentsBuild machine learning and deep learning systems with TensorFlow 2.x and the Keras APIWho this book is forThis hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems.Some machine learning knowledge would be useful. We don't assume TF knowledge.

Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devicesKey FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook DescriptionDeep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho this book is forThis book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.

Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book • Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games • See how various deep-learning models and practical use-cases can be implemented using Keras • A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn • Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm • Fine-tune a neural network to improve the quality of results • Use deep learning for image and audio processing • Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases • Identify problems for which Recurrent Neural Network (RNN) solutions are suitable • Explore the process required to implement Autoencoders • Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.