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Deep Learning with Python, Second Edition. Yazar: Chollet, Francois

İçindekiler: “…6.2.5 Regularize and tune your model -- 6.3 Deploy the model -- 6.3.1 Explain your work to stakeholders and set expectations -- 6.3.2 Ship an inference model -- 6.3.3 Monitor your model in the wild -- 6.3.4 Maintain your model -- Summary -- 7 Working with Keras: A deep dive -- 7.1 A spectrum of workflows -- 7.2 Different ways to build Keras models -- 7.2.1 The Sequential model -- 7.2.2 The Functional API -- 7.2.3 Subclassing the Model class -- 7.2.4 Mixing and matching different components -- 7.2.5 Remember: Use the right tool for the job -- 7.3 Using built-in training and evaluation loops -- 7.3.1 Writing your own metrics -- 7.3.2 Using callbacks -- 7.3.3 Writing your own callbacks -- 7.3.4 Monitoring and visualization with TensorBoard -- 7.4 Writing your own training and evaluation loops -- 7.4.1 Training versus inference -- 7.4.2 Low-level usage of metrics -- 7.4.3 A complete training and evaluation loop -- 7.4.4 Make it fast with tf.function -- 7.4.5 Leveraging fit() with a custom training loop -- Summary -- 8 Introduction to deep learning for computer vision -- 8.1 Introduction to convnets -- 8.1.1 The convolution operation -- 8.1.2 The max-pooling operation -- 8.2 Training a convnet from scratch on a small dataset -- 8.2.1 The relevance of deep learning for small-data problems -- 8.2.2 Downloading the data -- 8.2.3 Building the model -- 8.2.4 Data preprocessing -- 8.2.5 Using data augmentation -- 8.3 Leveraging a pretrained model -- 8.3.1 Feature extraction with a pretrained model -- 8.3.2 Fine-tuning a pretrained model -- Summary -- 9 Advanced deep learning for computer vision -- 9.1 Three essential computer vision tasks -- 9.2 An image segmentation example -- 9.3 Modern convnet architecture patterns -- 9.3.1 Modularity, hierarchy, and reuse -- 9.3.2 Residual connections -- 9.3.3 Batch normalization -- 9.3.4 Depthwise separable convolutions.…”
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Neural Network Computer Vision with OpenCV 5. Yazar: Nuti, Gopi Krishna

İçindekiler: “…OpenCV DNN Module -- Introduction -- Structure -- Objectives -- Deep learning frameworks -- TensorFlow -- PyTorch -- Keras -- Inference for computer vision -- Local inferencing -- Local CPUs -- Local GPUs -- Cloud -- Edge computing -- OpenCV DNN module -- History -- Features and limitations -- Capabilities -- Limitations -- Considerations -- Supported layers -- Unsupported layers and operations -- Important classes -- Conclusion -- Exercises -- 7. …”
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Deep Learning : Foundations and Concepts. Yazar: Bishop, Christopher M.

İçindekiler: “…The Bias-Variance Trade-off -- Exercises -- 5 Single-layer Networks: Classification -- 5.1. Discriminant Functions -- 5.1.1 Two classes -- 5.1.2 Multiple classes -- 5.1.3 1-of-K coding -- 5.1.4 Least squares for classification -- 5.2. …”
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Advances in Informatics and Computing in Civil and Construction Engineering Proceedings of the 35th CIB W78 2018 Conference: IT in Design, Construction, and Management /

İçindekiler: “…Education, Training, and Learning with Technologies -- BIM4VET, towards BIM training recommendation for AEC professionals -- Teaching effective collaborative information delivery and management in response to a BIM mandate -- A Story of Online Construction Masters' Project: Is An Active Online Independent Study Course Possible? …”
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Proceedings of the Second International Conference on Computer and Communication Technologies IC3T 2015, Volume 2 /

İçindekiler: “…An Intelligent Packet Filtering Based on Bi-Layer Particle Swarm Optimization with Reduced Search Space -- Chapter 63. …”
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Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications FICTA 2016, Volume 1 /

İçindekiler: “…Text Document Classification with PCA and One-Class SVM -- Chapter 12. Data Mining Approach to Predict and Analyze the Cardiovascular Disease -- Chapter 13. …”
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