Arama Sonuçları - "deep learning"

Deep Learning : Foundations and Concepts / Yazar: Bishop, Christopher M., Bishop, Hugh

Konular: “…Deep learning (Machine learning)…”
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Deep Learning : A Visual Approach. Yazar: Glassner, Andrew

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Deep Learning : Foundations and Concepts. Yazar: Bishop, Christopher M.

İçindekiler: “…Intro -- Preface -- Goals of the book -- Responsible use of technology -- Structure of the book -- References -- Exercises -- Mathematical notation -- Acknowledgements -- Contents -- 1 The Deep Learning Revolution -- 1.1. The Impact of Deep Learning -- 1.1.1 Medical diagnosis -- 1.1.2 Protein structure -- 1.1.3 Image synthesis -- 1.1.4 Large language models -- 1.2. …”
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Deep learning / Yazar: Goodfellow, Ian

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Deep learning : methods and applications / Yazar: Deng, Li

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Generative deep learning : Teaching machines to paint, write, compose, and play / Yazar: Foster, David

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Deep learning with Python / Yazar: Chollet, François

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Deep learning with Python / Yazar: Chollet, François

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Deep learning with Python / Yazar: Chollet, Francois

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Embedded Deep Learning Algorithms, Architectures and Circuits for Always-on Neural Network Processing / Yazar: Moons, Bert, Bankman, Daniel, Verhelst, Marian

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Deep learning for vision systems / Yazar: Elgendy, Mohamed

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The deep learning with keras workshop : Learn how to define and train neural network models with just few lines of code / Yazar: Moocarme, Matthew

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Handbook of Deep Learning Applications

İçindekiler: “…Designing a Neural Network from scratch for Big Data powered by Multi-node GPUs -- Deep Learning for Scene Understanding -- Deep Learning for Driverless Vehicles -- Deep Learning for Document Representation -- Deep learning for marine species recognition -- Deep molecular representation in Cheminformatics -- Deep Learning in eHealth -- Deep Learning for Brain Computer Interfaces -- Deep Learning in Gene Expression Modeling.…”
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Deep Learning-Based Face Analytics. Yazar: Ratha, Nalini K.

İçindekiler: “…7.7.2 IJB-A -- 7.7.3 IJB-B -- 7.7.4 IJB-C -- 7.8 Open Issues -- 7.9 Conclusion -- References -- 8 Evolution of Newborn Face Recognition -- 8.1 Introduction -- 8.1.1 Biometric Modalities for Newborns -- 8.1.2 Characteristics and Challenges of Newborn Face Recognition -- 8.2 Datasets for Newborn Face Recognition -- 8.2.1 Newborns Face Database -- 8.2.2 Newborns, Infants, and Toddler Longitudinal Face Database -- 8.2.3 Children Multimodal Biometric Database (CMDB) -- 8.3 Existing Techniques for Newborn Face Recognition -- 8.3.1 Handcrafted Feature Extraction Methods -- 8.3.2 Autoencoder Learning-Based Method -- 8.3.3 Class-Based Penalty in CNN Filter Learning -- 8.3.4 Learning Structure and Strength of CNN Filters -- 8.4 Results and Analysis of Existing Newborn Face Recognition Techniques -- 8.5 Conclusion -- References -- 9 Deep Feature Fusion for Face Analytics -- 9.1 Introduction -- 9.2 Feature Aggregation for Face Recognition -- 9.2.1 Metadata-Based Feature Aggregator Network (M-FAN) -- 9.2.2 Architecture -- 9.2.3 Gradient Backpropagation -- 9.2.4 Batch Training -- 9.2.5 Experiment Setup -- 9.2.6 Results on IJB-A -- 9.2.7 Results on Janus CS4 -- 9.3 Feature Enhancement for Facial Action Unit Recognition -- 9.3.1 Multi-modal Conditional Feature Enhancement (MCFE) -- 9.3.2 Feature Extraction -- 9.3.3 Deep Feature Enhancement -- 9.3.4 Training MCFE for AU Recognition -- 9.3.5 Datasets for Experimental Analysis -- 9.3.6 Experiment Settings -- 9.3.7 Results -- 9.4 Conclusion -- References -- 10 Deep Learning for Video Face Recognition -- 10.1 Introduction -- 10.2 Traditional Methods -- 10.3 Existing Deep Learning-based Approaches -- 10.3.1 Pairwise Distance-Based Methods -- 10.3.2 Pooling-Based Methods -- 10.4 Neural Aggregation Network -- 10.4.1 Feature Embedding Module -- 10.4.2 Aggregation Module -- 10.4.3 Network Training -- 10.5 Experiments.…”
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Deep Learning with Python, Second Edition. Yazar: Chollet, Francois

İçindekiler: “…Intro -- Deep Learning with Python -- Copyright -- dedication -- brief contents -- contents -- front matter -- preface -- acknowledgments -- about this book -- Who should read this book -- About the code -- liveBook discussion forum -- about the author -- about the cover illustration -- 1 What is deep learning? …”
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Pro Deep Learning with TensorFlow : A Mathematical Approach to Advanced Artificial Intelligence in Python / Yazar: Pattanayak, Santanu

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Deep learning-based face analytics /

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Deep learning for coders with fastai and PyTorch : Al applications without a PhD / Yazar: Howard, Jeremy

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Practical deep learning for cloud, mobile, and edge : real-world ai & computer-vision projects using Python, Keras & TensorFlow/ Yazar: Koul, Anirudh

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Deep Learning and Missing Data in Engineering Systems Yazar: Leke, Collins Achepsah, Marwala, Tshilidzi

İçindekiler: “…Introduction to Missing Data Estimation -- Introduction to Deep Learning -- Missing Data Estimation Using Bat Algorithm -- Missing Data Estimation Using Cuckoo Search Algorithm -- Missing Data Estimation Using Firefly Algorithm -- Missing Data Estimation Using Ant Colony Optimization Algorithm -- Missing Data Estimation Using Ant-Lion Optimizer Algorithm -- Missing Data Estimation Using Invasive Weed Optimization Algorithm -- Missing Data Estimation Using Swarm Intelligence Algorithms from Reduced Dimensions -- Missing Data Estimation Using Swarm Intelligence Algorithms: Deep Learning Framework Analysis -- Conclusion.…”
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