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Deep Learning with TensorFlow and Keras - 3rd Edition : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. by Kapoor, Amita

Table of Contents: “…-- Introduction to neural networks -- Perceptron -- Our first example of TensorFlow code -- Multi-layer perceptron: our first example of a network -- Problems in training the perceptron and solution -- Activation function: sigmoid -- Activation function: tanh -- Activation function: ReLU -- Two additional activation functions: ELU and Leaky ReLU -- Activation functions -- In short: what are neural networks after all? …”
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Deep Learning with Python, Second Edition. by Chollet, Francois

Table of Contents: “…-- 3.3 Keras and TensorFlow: A brief history -- 3.4 Setting up a deep learning workspace -- 3.4.1 Jupyter notebooks: The preferred way to run deep learning experiments -- 3.4.2 Using Colaboratory -- 3.5 First steps with TensorFlow -- 3.5.1 Constant tensors and variables -- 3.5.2 Tensor operations: Doing math in TensorFlow -- 3.5.3 A second look at the GradientTape API -- 3.5.4 An end-to-end example: A linear classifier in pure TensorFlow -- 3.6 Anatomy of a neural network: Understanding core Keras APIs -- 3.6.1 Layers: The building blocks of deep learning -- 3.6.2 From layers to models -- 3.6.3 The "compile" step: Configuring the learning process -- 3.6.4 Picking a loss function -- 3.6.5 Understanding the fit() method -- 3.6.6 Monitoring loss and metrics on validation data -- 3.6.7 Inference: Using a model after training -- Summary -- 4 Getting started with neural networks: Classification and regression -- 4.1 Classifying movie reviews: A binary classification example -- 4.1.1 The IMDB dataset -- 4.1.2 Preparing the data -- 4.1.3 Building your model -- 4.1.4 Validating your approach.…”
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Neural Network Computer Vision with OpenCV 5. by Nuti, Gopi Krishna

Table of Contents: “…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. by Bishop, Christopher M.

Table of Contents: “…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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Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications FICTA 2016, Volume 1 /

Table of Contents: “…Minimization of Energy Consumption Using X-Layer Network Transformation Model for IEEE 802.15.4-Based MWSNs -- Chapter 75. …”
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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 /

Table of Contents: “…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 /

Table of Contents: “…An Intelligent Packet Filtering Based on Bi-Layer Particle Swarm Optimization with Reduced Search Space -- Chapter 63. …”
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Intelligent and Fuzzy Techniques : Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21-23 2020. by Kahraman, Cengiz

Table of Contents: “…Comparison of Fuzzy C-Means and K-Means Clustering Performance: An Application on Household Budget Survey Data -- Abstract -- 1 Introduction -- 2 Methods -- 3 Results -- 3.1 Descriptive Statistics -- 3.2 Distribution Analysis -- 3.3 Normality Tests -- 3.4 Classification Results -- 4 Conclusions and Future Work -- References -- A Hybrid Approach for Clustering and Selecting of Cloud Services Based on User Preferences Evaluation -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Cloud Architecture -- 3.2 Selection Layer Functionality -- 4 Case Study -- 5 Conclusion -- References -- Basket Patterns in Turkey: A Clustering of FMCG Baskets Using Consumer Panel Data -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Data -- 5 Model Building and Comparison -- 6 Findings -- 7 Conclusion and Discussion -- Acknowledgement -- References -- Journey Segmentation of Turkish Tobacco Users Using Sequence Clustering Techniques -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Data -- 5 Model Building and Comparison -- 6 Findings -- 7 Conclusion and Discussion -- Acknowledgement -- References -- A Survey on Spherical Fuzzy Sets and Clustering the Literature -- Abstract -- 1 Introduction -- 2 Review Methodology and Results -- 3 Conclusion -- References -- Picture Fuzzy Sets and Spherical Fuzzy Sets -- Picture Fuzzy Linear Assignment Method and Its Application to Selection of Pest House Location -- Abstract -- 1 Introduction -- 2 Picture Fuzzy Sets: Preliminaries -- 3 Picture Fuzzy Linear Assignment Method -- 4 An Application to Pest House Location Selection -- 5 Conclusion -- References -- Simple Additive Weighting and Weighted Product Methods Using Picture Fuzzy Sets -- Abstract -- 1 Introduction -- 2 Definitions of Picture Fuzzy Sets -- 3 The Proposed Methods.…”
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