LEADER 03773nam 2200421 450 001 9910826422503321 005 20200302094444.0 010 $a1-78995-271-9 035 $a(CKB)4100000010011028 035 $a(MiAaPQ)EBC6005548 035 $a(CaSebORM)9781789956177 035 $a(PPN)242831710 035 $a(EXLCZ)994100000010011028 100 $a20200302d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced deep learning with Python $edesign and implement advanced next-generation AI solutions using TensorFlow and Pytorch /$fIvan Vasilev 205 $a1st edition 210 1$aBirmingham, England ;$aMumbai :$cPackt,$d[2019] 210 4$dİ2019 215 $a1 online resource (456 pages) 311 $a1-78995-617-X 320 $aIncludes bibliographical references. 330 $aGain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you'll need to understand everything from how neural networks work to training CNN models. In this book, you'll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You'll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you'll focus on variational autoencoders and GANs. You'll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You'll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you'll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you'll understand how to apply deep learning to autonomous vehicles. By the end of this book, you'll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to furthe... 606 $aPython (Computer program language) 615 0$aPython (Computer program language) 676 $a005.133 700 $aVasilev$b Ivan$01691144 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826422503321 996 $aAdvanced deep learning with Python$94067331 997 $aUNINA LEADER 04353nam 22005893 450 001 9911020213203321 005 20250225080317.0 010 $a9781394270392 010 $a1394270399 010 $a9781394270378 010 $a1394270372 010 $a9781394270385 010 $a1394270380 035 $a(CKB)37529492100041 035 $a(MiAaPQ)EBC31907281 035 $a(Au-PeEL)EBL31907281 035 $a(OCoLC)1500135867 035 $a(Perlego)4893422 035 $a(Exl-AI)31907281 035 $a(FI-CZ)9781394270378 035 $a(UkBuK)3321769 035 $a(EXLCZ)9937529492100041 100 $a20250225d2025 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEnvironmental Monitoring Using Artificial Intelligence 205 $a1st ed. 210 1$cWiley$d2025 215 $a1 online resource (423 pages) 311 08$a9781394270361 311 08$a1394270364 327 $aCover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Transformative Trends in AI for Environmental Monitoring: Challenges, Applications -- 1.1 Introduction -- 1.2 Literature Verticals -- 1.3 Key Methodologies in Literature Review -- 1.4 Most Common Methods in Environmental Monitoring -- 1.5 AI Architectures for Environmental Monitoring -- 1.6 Applications of AI in Environmental Monitoring -- 1.7 Challenges and Limitations of Using AI in Environment Modeling -- 1.8 Future Directions -- 1.9 Conclusion -- Acknowledgements -- References -- Chapter 2 Fundamentals of AI and NLP in Environmental Analysis -- 2.1 Introduction -- 2.2 AI and NLP Techniques -- 2.2.1 Artificial Neural Network (ANN) -- 2.2.2 Support Vector Machine (SVM) -- 2.2.3 Linear Regression (LR) -- 2.2.4 Random Forests (RF) and Decision Trees (DT) -- 2.2.5 Adaptive Neuro-Fuzzy Inference System (ANFIS) -- 2.2.6 Batch-Normalization (BN) -- 2.2.7 Convolutional Neural Networks (CNNs) -- 2.2.8 Deep Neural Networks (DNNs) -- 2.2.9 Genetic Algorithm (GA)$7Generated by AI. 330 8 $aEnvironmental Monitoring Using Artificial Intelligence is a vital resource for anyone looking to leverage cutting-edge technologies in artificial intelligence and sensor systems to effectively address environmental challenges, offering innovative solutions and insights essential for creating a sustainable future. Environmental Monitoring Using Artificial Intelligence provides a comprehensive exploration of the cutting-edge technologies transforming environmental monitoring. This book bridges the gap between artificial intelligence (AI), natural language processing (NLP), and sensor-based systems, highlighting their potential to revolutionize the way we address pressing environmental challenges. Each chapter presents innovative case studies, real-world applications, and the latest research on how these technologies are being utilized to monitor and manage ecosystems, water resources, air quality, and urban sustainability. From advanced sensor networks to machine learning models, this book covers a broad spectrum of topics, including smart water solutions, biodiversity conservation, waste management, and agricultural sustainability. It offers an interdisciplinary approach, making it an essential resource for environmental engineers, data scientists, researchers, and policymakers. Whether you're exploring smart city innovations, renewable energy monitoring, or AI-driven solutions for environmental protection, Environmental Monitoring Using Artificial Intelligence equips readers with the knowledge and tools to leverage technology for a sustainable future. 606 $aArtificial intelligence$7Generated by AI 606 $aEnvironmental monitoring$7Generated by AI 615 0$aArtificial intelligence 615 0$aEnvironmental monitoring 676 $a363.7063028563 700 $aSuresh$b A$01406426 701 $aThirupathi$b Devi$01887922 701 $aDeepa$b N$01838136 701 $aBashir$b Ali Kashif$01762152 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020213203321 996 $aEnvironmental Monitoring Using Artificial Intelligence$94526040 997 $aUNINA