Tabla de Contenidos: “…From Safety to Resilience; 13 TAKING THINGS IN ONE'S STRIDE: COGNITIVE FEATURES OF TWO RESILIENT PERFORMANCES; Introduction; Example 1: Handling a 'Soft' Emergency; Example 2: Response to a Bus Bombing; Analysis; Conclusion; 14 EROSION OF MANAGERIAL RESILIENCE: FROM VASA TO NASA; Vasa to Columbia; Managerial Resilience; Safety Culture and Managerial Resilience; Measuring Managerial Resilience; Training Managerial Resilience…”

Tabla de Contenidos:

Tabla de Contenidos:

Tabla de Contenidos: “…Set benchmark runtime rulesConcluding Thoughts; Processors; Microprocessor Architecture; Clock Rates; Pipelining; Variable-length instructions; Branches; The Second Generation of RISC Processor Design; Caching; The Cache Hierarchy; Cache Organization and Operation; Associativity; Locality and "Cache-Busters"; Unit stride; Linked lists; Cache-aligned block copy problems; The Cache Size Anomaly; Process Scheduling; The System V Model: The Linux Model; Finding a process's priority; Adjusting a process's effective priority; Modifications for SMP systems…”

Tabla de Contenidos: “…Data type objectsCharacter codes; dtype constructors; dtype attributes; Creating a record data type; One-dimensional slicing and indexing; Manipulating array shapes; Stacking arrays; Splitting arrays; Array attributes; Converting arrays; Creating views and copies; Fancy indexing; Indexing with a list of locations; Indexing arrays with Booleans; Stride tricks for Sudoku; Broadcasting arrays; Summary; Chapter 3: Basic Data Analysis with NumPy; Introducing the dataset; Determining the daily temperature range; Looking for evidence of global warming; Comparing solar radiation versus temperature…”

Tabla de Contenidos: “…; walking; pace; walking mechanics; the four basic positions of a walk; the stride positions; the cross over positions; shoulder movement; arm movement; up and down movement of the body; walk cycles displaying different moods; external influences; two people walking together; running; chapter 6 animal walks and runs…”

Tabla de Contenidos: “…-- 11.1.3 Lookup model -- 11.2 Finessing lookups -- 11.2.1 Threaded indices and tag switching -- 11.2.2 Flow switching -- 11.2.3 Status of tag switching, flow switching, and multiprotocol label switching -- 11.3 Non-algorithmic techniques for prefix matching -- 11.3.1 Caching -- 11.3.2 Ternary content-addressable memories -- 11.4 Unibit tries -- 11.5 Multibit tries -- 11.5.1 Fixed-stride tries -- 11.5.2 Variable-stride tries -- 11.5.3 Incremental update -- 11.6 Level-compressed (LC) tries -- 11.7 Lulea-compressed tries -- 11.8 Tree bitmap -- 11.8.1 Tree bitmap ideas -- 11.8.2 Tree bitmap search algorithm -- 11.8.3 PopTrie: an alternate bitmap algorithm -- 11.9 Binary search on ranges -- 11.10 Binary search on ranges with Initial Lookup Table -- 11.11 Binary search on prefix lengths -- 11.12 Linear search on prefix lengths with hardware assist -- 11.12.1 Using Bloom Filters to compress prefix bitmaps -- 11.12.2 SAIL: Uncompressed Bitmaps up to a pivot level -- 11.13 Memory allocation in compressed schemes -- 11.13.1 Frame-based compaction -- 11.14 Fixed Function Lookup-chip models -- 11.15 Programmable Lookup Chips and P4 -- 11.15.1 The P4 language -- 11.15.2 IP Lookups in the P4 Model -- 11.16 Conclusions -- 11.17 Exercises -- 12 Packet classification -- 12.1 Why packet classification? …”

Tabla de Contenidos: “…-- Data flow diagrams -- Microsoft Threat Modeling Tool -- Identifying threats with STRIDE -- Spoofing -- Tampering -- Repudiation -- Information Disclosure -- Denial of Service -- Elevation of Privilege -- What are we going to do about it? …”

Tabla de Contenidos: “…Threat modeling for Azure Machine Learning -- Exploring the STRIDE methodology -- Getting started with the Microsoft Threat Modeling Tool -- Reviewing the shared responsibility model for cloud security -- Exploring the cloud provider responsibilities -- Reviewing customers' responsibilities -- Summary -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…Implementation in TensorFlow -- Deep belief networks -- Summary -- Chapter 5: Image Recognition -- Similarities between artificial and biological models -- Intuition and justification -- Convolutional layers -- Stride and padding in convolutional layers -- Pooling layers -- Dropout -- Convolutional layers in deep learning -- Convolutional layers in Theano -- A convolutional layer example with Keras to recognize digits -- A convolutional layer example with Keras for cifar10 -- Pre-training -- Summary -- Chapter 6: Recurrent Neural Networks and Language Models -- Recurrent neural networks -- RNN - how to implement and train -- Backpropagation through time -- Vanishing and exploding gradients -- Long short term memory -- Language modeling -- Word-based models -- N-grams -- Neural language models -- Character-based model -- Preprocessing and reading data -- LSTM network -- Training -- Sampling -- Example training -- Speech recognition -- Speech recognition pipeline -- Speech as input data -- Preprocessing -- Acoustic model -- Deep belief networks -- Recurrent neural networks -- CTC -- Attention-based models -- Decoding -- End-to-end models -- Summary -- Bibliography -- Chapter 7: Deep Learning for Board Games -- Early game playing AI -- Using the min-max algorithm to value game states -- Implementing a Python Tic-Tac-Toe game -- Learning a value function -- Training AI to master Go -- Upper confidence bounds applied to trees -- Deep learning in Monte Carlo Tree Search -- Quick recap on reinforcement learning -- Policy gradients for learning policy functions -- Policy gradients in AlphaGo -- Summary -- Chapter 8: Deep Learning for Computer Games -- A supervised learning approach to games -- Applying genetic algorithms to playing games -- Q-Learning -- Q-function -- Q-learning in action -- Dynamic games -- Experience replay -- Epsilon greedy…”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Introduction -- Chapter 1: The Building Blocks of Computer Vision -- What Is Computer Vision -- Applications -- Classification -- Object Detection and Localization -- Image Segmentation -- Anomaly Detection -- Video Analysis -- Channels -- Convolutional Neural Networks -- Receptive Field -- Local Receptive Field -- Global Receptive Field -- Pooling -- Max Pooling -- Average Pooling -- Global Average Pooling -- Calculation: Feature Map and Receptive Fields -- Kernel -- Stride -- Pooling -- Padding -- Input and Output -- Calculation of Receptive Field -- Understanding the CNN Architecture Type -- Understanding Types of Architecture -- AlexNet -- VGG -- ResNet -- Inception Architectures -- Working with Deep Learning Model Techniques -- Batch Normalization -- Dropouts -- Data Augmentation Techniques -- Introduction to PyTorch -- Installation -- Basic Start -- Summary -- Chapter 2: Image Classification -- Topics to Cover -- Defining the Problem -- Overview of the Approach -- Creating an Image Classification Pipeline -- First Basic Model -- Data -- Data Exploration -- Data Loader -- Define the Model -- The Training Process -- The Second Variation of Model -- The Third Variation of the Model -- The Fourth Variation of the Model -- Summary -- Chapter 3: Building an Object Detection Model -- Object Detection Using Boosted Cascade -- R-CNN -- The Region Proposal Network -- Fast Region-Based Convolutional Neural Network -- How the Region Proposal Network Works -- The Anchor Generation Layer -- The Region Proposal Layer -- Mask R-CNN -- Prerequisites -- YOLO -- YOLO V2/V3 -- Project Code Snippets -- Step 1: Getting Annotated Data -- Step 2: Fixing the Configuration File and Training -- The Model File -- Summary -- Chapter 4: Building an Image Segmentation Model…”

Tabla de Contenidos: “…Restricted access to productive and financial resources -- Laws guarantee women's access to land and non-land assets, yet exclude some groups of women -- Women's access to financial services has made great strides -- Deeply entrenched social norms and specific legal restrictions hamper women's workplace rights -- Restricted civil liberties -- Nationality laws uphold gender inequality in citizenship rights -- Women's political voice has improved at the national level since 2017 -- While legal frameworks generally protect women's freedom of movement, social practices continue to limit women's mobility -- Legal pluralism continues to hamper women's access to justice -- References -- Notes -- Annex A. …”

Tabla de Contenidos: “…Data Types -- Identifiers and Keywords -- Integral Types -- Integers -- Booleans -- Floating-Point Types -- Floating-Point Numbers -- Complex Numbers -- Decimal Numbers -- Strings -- Comparing Strings -- Slicing and Striding Strings -- String Operators and Methods -- String Formatting with the str.format() Method -- Character Encodings -- Examples -- quadratic.py -- csv2html.py -- Summary -- Exercises -- Chapter 3. …”

Tabla de Contenidos: “…Notes -- References -- Chapter 5: Unsupervised Learning -- K -means Clustering -- Hierarchical Clustering -- Association Rule Mining -- K -Nearest Neighbors -- Summary -- Exercise -- References -- Chapter 6: Supervised Learning -- Introduction to Artificial Neural Networks -- Forward and Backward Propagation Methods -- Architectural Types in ANN -- Hyperparameters for Tuning the ANN -- An Example of ANN Classification -- Introduction to Ensemble Learning Techniques -- Random Forest Ensemble Learning -- Introduction to AdaBoost Ensemble Learning -- Introduction to Extreme Gradient Boosting (XGB) -- Cross-Validation -- Summary -- Exercise -- References -- Chapter 7: Natural Language Processing for Analyzing Unstructured Data -- Terminology for NLP -- Installing NLTK and Other Libraries -- Tokenization -- Stemming -- Stopwords -- Part of Speech Tagging -- Bag-of-Words (BOW) -- n- grams -- Sentiment and Emotion Classification -- Summary -- Exercise -- References -- Chapter 8: Predictive Analytics Using Deep Neural Networks -- Introduction to Deep Learning -- The Deep Neural Networks and Its Architectural Variants -- Multilayer Perceptron (MLP) -- Convolutional Neural Networks (CNN) -- Recurrent Neural Networks (RNN) -- AlexNet -- VGGNet -- Inception -- ResNet and GoogLeNet -- Hyperparameters of DNN and Strategies for Tuning Them -- Activation Function -- Regularization -- Number of Hidden Layers -- Number of Neurons Per Layer -- Learning Rate -- Optimizer -- Batch Size -- Epoch -- Weight and Biases Initialization -- Grid Search -- Random Search -- Deep Belief Networks (DBN) -- Analyzing the Boston Housing Dataset Using DNN -- Summary -- Exercise -- References -- Chapter 9: Convolutional Neural Networks (CNN) for Predictive Analytics -- Convolution Layer -- Padding and Strides -- ReLU LAYER -- Pooling Layer -- Fully Connected Layer…”

Tabla de Contenidos: “…-- Principles -- Open Web Application Security Project -- NIST's Secure Software Development Framework -- MITRE frameworks -- Software development lifecycles -- Microsoft's Security Development Lifecycle -- Confidentiality, integrity, and availability -- Summary -- Self-assessment questions -- Answers -- Chapter 2: Designing a Secure Functional Model -- Requirements gathering and specification -- Non-functional requirements and security -- Capturing scenarios -- Textual use cases and misuse cases -- Graphical use cases and misuse cases -- Graphical use case diagram -- Graphical misuse case diagram -- Example enterprise secure functional model -- Purchase of tickets via self-service -- Trying to purchase tickets beyond the patron limit -- Summary -- Self-assessment questions -- Answers -- Chapter 3: Designing a Secure Object Model -- Identify objects and relationships -- Class diagrams -- Stereotypes -- Invariants -- Example of the enterprise secure object model -- Summary -- Self-assessment questions -- Answers -- Chapter 4: Designing a Secure Dynamic Model -- Technical requirements -- Object behavior -- Modeling interactions between objects -- UML sequence diagrams -- UML activity diagrams -- Constraints -- Example of the enterprise secure dynamic model -- Summary -- Self-assessment questions -- Answers -- Chapter 5: Designing a Secure System Model -- Partitions -- Modeling interactions between partitions -- UML component diagrams -- Patterns -- Example - developing an enterprise secure system model -- Summary -- Self-assessment questions -- Answers -- Chapter 6: Threat Modeling -- Threat model overview -- The STRIDE threat model -- The DREAD threat model…”

Tabla de Contenidos: “…Preparing our data for image classification -- Training a neural network -- Scaling a dataset to improve model accuracy -- Understanding the impact of varying the batch size -- Batch size of 32 -- Batch size of 10,000 -- Understanding the impact of varying the loss optimizer -- Building a deeper neural network -- Understanding the impact of batch normalization -- Very small input values without batch normalization -- Very small input values with batch normalization -- The concept of overfitting -- Impact of adding dropout -- Impact of regularization -- L1 regularization -- L2 regularization -- Summary -- Questions -- Section 2: Object Classification and Detection -- Chapter 4: Introducing Convolutional Neural Networks -- The problem with traditional deep neural networks -- Building blocks of a CNN -- Convolution -- Filters -- Strides and padding -- Strides -- Padding -- Pooling -- Putting them all together -- How convolution and pooling help in image translation -- Implementing a CNN -- Classifying images using deep CNNs -- Visualizing the outcome of feature learning -- Building a CNN for classifying real-world images -- Impact on the number of images used for training -- Summary -- Questions -- Chapter 5: Transfer Learning for Image Classification -- Introducing transfer learning -- Understanding the VGG16 architecture -- Implementing VGG16 -- Understanding the ResNet architecture -- Implementing ResNet18 -- Implementing facial keypoint detection -- 2D and 3D facial keypoint detection -- Implementing age estimation and gender classification -- Introducing the torch_snippets library -- Summary -- Questions -- Chapter 6: Practical Aspects of Image Classification -- Generating CAMs -- Understanding the impact of data augmentation and batch normalization -- Coding up road sign detection -- Practical aspects to take care of during model implementation…”

Tabla de Contenidos: “…-- Types of optimizers -- Gradient descent -- Different variants of gradient descent -- Algorithms to optimize gradient descent -- Which optimizer to choose -- Optimization with an example -- Summary -- Chapter 4: Convolutional Neural Networks -- An overview and the intuition of CNN -- Single Conv Layer Computation -- CNN in TensorFlow -- Image loading in TensorFlow -- Convolution operations -- Convolution on an image -- Strides -- Pooling -- Max pool -- Example code -- Average pool -- Image classification with convolutional networks -- Defining a tensor for input images and the first convolution layer -- Input tensor -- First convolution layer -- Second convolution layer -- Third convolution layer…”

Tabla de Contenidos: “…Fault tree analysis -- Threat modeling -- STRIDE threat model -- DREAD threat model -- Trustworthiness of an IIoT system -- Industrial big data pipeline and architectures -- Industrial IoT security architecture -- Business viewpoint -- Usage viewpoint -- Functional viewpoint -- Implementation viewpoint -- IIoT architecture patterns -- Pattern 1 - Three-tier architectural model -- Pattern 2 - Layered databus architecture -- Building blocks of industrial IoT security architecture -- A four-tier IIoT security model -- Summary -- Chapter 3: IIoT Identity and Access Management -- A primer on identity and access control -- Identification -- Authentication -- Authorization -- Account management -- Distinguishing features of IAM in IIoT -- Diversity of IIoT endpoints -- Resource-constrained and brownfield considerations -- Physical safety and reliability -- Autonomy and scalability -- Event logging is a rarity -- Subscription-based models -- Increasing sophistication of identity attacks -- Risk-based access control policy -- Identity management across the device lifecycle -- Authentication and authorization frameworks for IIoT -- Password-based authentication -- Biometrics -- Multi-factor authentication -- Key-based authentication -- Symmetric keys -- Asymmetric keys -- Zero-knowledge keys -- Certificate-based authentication -- Trust models - public key infrastructures and digital certificates -- PKI certificate standards for IIoT -- ITU-T X.509 -- IEEE 1609.2 -- Certificate management in IIoT deployments -- Extending the OAuth 2.0 authorization framework for IoT access control -- IEEE 802.1x -- Identity support in messaging protocols -- MQTT -- CoAP -- DDS -- REST -- Monitoring and management capabilities -- Activity logging support -- Revocation support and OCSP -- Building an IAM strategy for IIoT deployment -- Risk-based policy management -- Summary…”

Tabla de Contenidos: “…-- Code for visualizing an image -- Dropout -- Input layer -- Convolutional layer -- Convolutional layers in Keras -- Pooling layer -- Practical example - image classification -- Image augmentation -- Summary -- Chapter 3: Build Your First CNN and Performance Optimization -- CNN architectures and drawbacks of DNNs -- Convolutional operations -- Pooling, stride, and padding operations -- Fully connected layer -- Convolution and pooling operations in TensorFlow -- Applying pooling operations in TensorFlow -- Convolution operations in TensorFlow -- Training a CNN -- Weight and bias initialization -- Regularization -- Activation functions -- Using sigmoid -- Using tanh -- Using ReLU -- Building, training, and evaluating our first CNN -- Dataset description -- Step 1 - Loading the required packages -- Step 2 - Loading the training/test images to generate train/test set -- Step 3- Defining CNN hyperparameters…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credit -- Contributors -- Table of Contents -- Preface -- Part 1: Introduction to Neural Networks -- Chapter 1: Machine Learning - an Introduction -- Technical requirements -- Introduction to ML -- Different ML approaches -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Components of an ML solution -- Neural networks -- Introducing PyTorch -- Summary -- Chapter 2: Neural Networks -- Technical requirements -- The need for NNs -- The math of NNs -- Linear algebra -- An introduction to probability -- Differential calculus -- An introduction to NNs -- Units - the smallest NN building block -- Layers as operations -- Multi-layer NNs -- Activation functions -- The universal approximation theorem -- Training NNs -- GD -- Backpropagation -- A code example of an NN for the XOR function -- Summary -- Chapter 3: Deep Learning Fundamentals -- Technical requirements -- Introduction to DL -- Fundamental DL concepts -- Feature learning -- The reasons for DL's popularity -- Deep neural networks -- Training deep neural networks -- Improved activation functions -- DNN regularization -- Applications of DL -- Introducing popular DL libraries -- Classifying digits with Keras -- Classifying digits with PyTorch -- Summary -- Part 2: Deep Neural Networks for Computer Vision -- Chapter 4: Computer Vision with Convolutional Networks -- Technical requirements -- Intuition and justification for CNNs -- Convolutional layers -- A coding example of the convolution operation -- Cross-channel and depthwise convolutions -- Stride and padding in convolutional layers -- Pooling layers -- The structure of a convolutional network -- Classifying images with PyTorch and Keras -- Convolutional layers in deep learning libraries -- Data augmentation -- Classifying images with PyTorch -- Classifying images with Keras…”