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Paperback. Zustand: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

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Paperback. Zustand: Very Good. 2nd ed. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Paperback. Zustand: Very Good. Deep Learning with TensorFlow 2 and Keras This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .…

Zustand: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Soft cover. Zustand: As New. 3rd Edition. book, cover and text in as new condition, binding tight and square, spine not creased, no damage, text clean and unmarked no names no stamps, book appear unused.

Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.

Zustand: good. Good: The book has been read but is in good condition. It has very minimal damage to the cover, including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. The binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, and no writing in the margins. There are no missing pages. See the seller's listing for full details and a description of any imperfections.…

Zustand: Very Good. Very Good condition. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.

Paperback. Zustand: New. Leverage the power of deep learning and Keras to develop smarter and more efficient data modelsKey FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook DescriptionKeras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy.The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learningWhat you will learnInstall and configure Keras in TensorFlowMaster neural network programming using the Keras libraryUnderstand the different Keras layersUse Keras to implement simple feed-forward neural networks, CNNs and RNNsWork with various datasets and models used for image and text classificationDevelop text summarization and reinforcement learning models using KerasWho this book is forKeras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.…

Zustand: New.

Zustand: New.

Zustand: New.

Zustand: New.

Digital. Zustand: New. Get to grips with the basics of Keras to implement fast and efficient deep-learning modelsAbout 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 KerasWho This Book Is ForIf 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 learningIn DetailThis 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 approachThis 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.…

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Contents 1 Introduction 2 Past and present work 3 Topography and general features 4 Vegetation 5 Discussion and findings 6 Systematic treatment 7 Utilitarian aspects of Ujani Wetland and other related studies 8 Biodiversity in Ujani Wetland Maharashtra 9 Threats to Ujani Wetland 10 Recommendations towards strategies for conservation and management of Ujani Wetland biodiversity Selected bibliography Index The Ujani Wetland is a manmade wetland that came into existence in June 1980 It was formed because of the construction of Ujani dam across the river Bhima which is a tributary of the river Krishna It is named after Ujani Village and lies between 18 04 24 North latitude and 75 07 15 East longitude on the border of Solapur Ahmednagar and Pune districts of Maharashtra It is situated about 150 km away from Pune on Pune-Solapur road The gross catchment area is 14856 km and total submerged area is 29000 ha Ujani Wetland has been identified by the Ministry of Environment and Forests Government of India amongst 115 wetlands under National Wetland Conservation programme The detailed floristic study of the entire area of this wetland has been done As a result 293 species and 16 infraspecific taxa belonging to 186 genera under 60 families of angiosperms and 2 species of Pteridophytes have been recorded It includes 9 endemic plants and several medicinal plants It also includes utilitarian aspects of Ujani Wetland and other related studies Co-relation between plants and birds are also studied Ujani Wetland is currently facing biotic interference and anthropogenic pressure Several proposals towards strategies for conservation and management of Ujani Wetland biodiversity have also been recommended in the book jacket 305 pp. maps, illus (illustrator).…

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: New. Leverage the power of deep learning and Keras to develop smarter and more efficient data modelsKey FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook DescriptionKeras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy.The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learningWhat you will learnInstall and configure Keras in TensorFlowMaster neural network programming using the Keras libraryUnderstand the different Keras layersUse Keras to implement simple feed-forward neural networks, CNNs and RNNsWork with various datasets and models used for image and text classificationDevelop text summarization and reinforcement learning models using KerasWho this book is forKeras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.…

Digital. Zustand: New. Get to grips with the basics of Keras to implement fast and efficient deep-learning modelsAbout 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 KerasWho This Book Is ForIf 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 learningIn DetailThis 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 approachThis 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.…

Taschenbuch. Zustand: Neu. Teacher Trainees and Environmental Education | Environmental Education | Sujit Pal | Taschenbuch | 344 S. | Englisch | 2017 | Scholars' Press | EAN 9786202302265 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Paperback. Zustand: Brand New. 344 pages. 8.66x5.91x0.78 inches. In Stock.

Zustand: New.

Taschenbuch. Zustand: Neu. Formation des enseignants et éducation à l'environnement | Éducation à l'environnement | Sujit Pal | Taschenbuch | Einband - flex.(Paperback) | Französisch | 2025 | Editions Notre Savoir | EAN 9786208541897 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 352 | Sprache: Französisch | Produktart: Bücher | Le voyage de l'éducation à l'environnement a commencé en même temps que le voyage de la civilisation. La clé d'une éducation environnementale réussie est l'enseignant. Si l'enseignant n'a pas les connaissances, les compétences et l'engagement nécessaires pour écologiser son programme, il est peu probable qu'il produise des élèves compétents en matière d'environnement. Les recherches menées sur cette question peuvent aider à concevoir un programme de formation des enseignants qui puisse répondre aux objectifs de l'éducation à l'environnement. Étant donné que les enseignants stagiaires d'aujourd'hui sont les enseignants de demain, il est nécessaire de savoir dans quelle mesure ils sont conscients de l'environnement et des problèmes environnementaux. La sensibilisation à l'environnement et l'action en faveur de l'environnement varient-elles d'un stagiaire à l'autre, en fonction des matières enseignées ? Leur lieu de résidence a-t-il un impact sur leur niveau de sensibilisation ou d'action ? Il a été mentionné à maintes reprises que la connaissance de l'environnement ne suffit pas à faire agir les gens. Il est tout aussi important de développer la volonté d'agir. Il convient donc d'étudier en profondeur la motivation vis-à-vis du comportement environnemental afin de comprendre l'action raisonnée des gens. C'est là que réside l'importance du présent ouvrage.…