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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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Pathways to excellence developing and cultivating leaders for the classroom and beyond /

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

Table of Contents: “…A Brief History of Machine Learning -- 1.3.1 Single-layer networks -- 1.3.2 Backpropagation -- 1.3.3 Deep networks -- 2 Probabilities -- 2.1. …”
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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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Intelligent and Fuzzy Techniques : Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21-23 2020. by Kahraman, Cengiz

Table of Contents: “…Intro -- Preface -- Contents -- Invited Speakers' Papers -- Fuzzy Meets Privacy: A Short Overview -- 1 Introduction -- 2 Data Protection Methods Based on Fuzzy Techniques -- 3 Information Loss Measures Based on Fuzzy Techniques -- 4 Disclosure Risk Measures Based on Aggregation Functions: WM, OWA and the Choquet Integral -- 5 Conclusion and Future Work -- References -- Intelligent Planning of Spatial Analysis Process Based on Contexts -- Abstract -- 1 Introduction -- 2 Review of Related Publications -- 3 Management of Analysis Process -- 4 Conceptual Model of Knowledge -- 5 Optimal Number of Contexts -- 6 Conclusion -- Acknowledgments -- References -- The Method of Finding the Base Set of Intuitionistic Fuzzy Graph -- Abstract -- 1 Introduction -- 2 Basic Concepts and Definitions of Intuitionistic Fuzzy Graphs -- 3 Base Set of Intuitionistic Fuzzy Graph -- 4 The Method for Finding Base Set -- 5 Conclusion -- Acknowledgments -- References -- Intuitionistic Fuzzy Assessments of the Abdominal Aorta and Its Branches -- Abstract -- 1 Introduction -- 2 Proposed Assessment Model -- 2.1 Intuitionistic Fuzzy Estimations -- 2.2 Intuitionistic Fuzzy Threshold Values -- 3 Conclusion -- Acknowledgment -- References -- Mathematical Philosophy and Fuzzy Logic -- Abstract -- 1 Mathematical Philosophy and Mathematics in General -- 2 Probability and Fuzzy Logic in Industrial Engineering -- 3 Conclusion -- References -- Clustering -- Segmentation of Retail Consumers with Soft Clustering Approach -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Managerial Implications -- 5 Conclusion and Limitations -- References -- Segmentation Analysis of Companies' Natural Gas Consumption by Soft Clustering -- 1 Introduction -- 2 Fuzzy C-Means Clustering -- 3 Case Study -- 4 Conclusions and Future Directions -- References.…”
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