Tabla de Contenidos: “…11.4 I/O-Status -- 11.5 Ein-/Ausgabe benutzerdefinierter Typen -- 11.6 Ausgabeformatierung -- 11.6.1 Stream-Formatierung -- 11.6.2 Formatierung im printf()-Stil -- 11.7 Streams -- 11.7.1 Standard-Streams -- 11.7.2 Datei-Streams -- 11.7.3 String-Streams -- 11.7.4 Speicher-Streams -- 11.7.5 Synchronisierte Streams -- 11.8 Ein-/Ausgaben im C-Stil -- 11.9 Dateisystem -- 11.9.1 Pfade -- 11.9.2 Dateien und Verzeichnisse -- 11.10 Ratschläge -- Kapitel 12: Container -- 12.1 Einführung -- 12.2 vector -- 12.2.1 Elemente -- 12.2.2 Bereichsüberprüfung -- 12.3 list -- 12.4 forward_list -- 12.5 map -- 12.6 unordered_map -- 12.7 Allokatoren -- 12.8 Ein Überblick über Container -- 12.9 Ratschläge -- Kapitel 13: Algorithmen -- 13.1 Einführung -- 13.2 Verwendung von Iteratoren -- 13.3 Iterator-Typen -- 13.3.1 Stream-Iteratoren -- 13.4 Verwendung von Prädikaten -- 13.5 Überblick über Algorithmen -- 13.6 Parallele Algorithmen -- 13.7 Ratschläge -- Kapitel 14: Bereiche (Ranges) -- 14.1 Einführung -- 14.2 Views -- 14.3 Generatoren -- 14.4 Pipelines -- 14.5 Überblick über Konzepte -- 14.5.1 Typkonzepte -- 14.5.2 Iterator-Konzepte -- 14.5.3 Bereichskonzepte -- 14.6 Ratschläge -- Kapitel 15: Zeiger und Container -- 15.1 Einführung -- 15.2 Zeiger -- 15.2.1 unique_ptr und shared_ptr -- 15.2.2 span -- 15.3 Container -- 15.3.1 array -- 15.3.2 bitset -- 15.3.3 pair -- 15.3.4 tuple -- 15.4 Alternativen -- 15.4.1 variant -- 15.4.2 optional -- 15.4.3 any -- 15.5 Ratschläge -- Kapitel 16: Utilities -- 16.1 Einführung -- 16.2 Zeit -- 16.2.1 Uhren -- 16.2.2 Kalender -- 16.2.3 Zeitzonen -- 16.3 Funktionsanpassung -- 16.3.1 Lambdas als Adapter -- 16.3.2 mem_fn() -- 16.3.3 function -- 16.4 Typfunktionen -- 16.4.1 Typprädikate -- 16.4.2 Bedingte Eigenschaften -- 16.4.3 Typgeneratoren -- 16.4.4 Assoziierte Typen -- 16.5 source_location -- 16.6 move() und forward() -- 16.7 Bitmanipulation…”

Tabla de Contenidos: “…. -- 7.1 Introduction and literature review -- 7.2 Materials and methods -- 7.2.1 Logistic regression -- 7.2.2 Gaussian naive Bayes -- 7.2.3 K-Nearest neighbors -- 7.2.4 Support vector classification -- 7.2.5 Radial basis function -- 7.2.6 Artificial neural network -- 7.2.7 Cart algorithm -- 7.2.8 Random forest -- 7.2.9 Gradient boosting machines -- 7.2.10 XGBoost -- 7.2.11 LightGBM -- 7.2.12 CatBoost -- 7.3 Experimental results -- 7.4 Conclusion and future work -- References -- 8 Healthcare cybersecurity challenges: a look at current and future trends -- 8.1 Introduction -- 8.2 The amount of prior works -- 8.3 Difficulties -- 8.3.1 Security assurance for remote work -- 8.3.2 Endpoint device administration -- 8.3.3 The role of humans in cybersecurity -- 8.3.4 A disregard for security -- 8.3.5 Ineffective risk assessment communication at the board level -- 8.3.6 Poor business continuity strategies -- 8.3.7 Ineffective incident response coordination -- 8.3.8 A tight budget and the requirement to provide healthcare services uninterrupted -- 8.3.9 Dangerous medical cyber-physical systems -- 8.4 A review of current and future trends in cybersecurity challenges in healthcare -- 8.5 Discussion -- 8.5.1 Cyber-physical medical systems -- 8.5.2 Data privacy, confidentiality, and consent -- 8.5.3 Cloud computing -- 8.5.4 Malware -- 8.5.5 Security of health application (or "app") -- 8.5.6 Insider danger -- 8.6 Cybersecurity tools, defenses, and mitigation techniques -- 8.6.1 Cryptographic systems or other technological advances -- 8.6.2 Governance and risk assessment -- 8.6.3 Laws or other regulations -- 8.6.4 A comprehensive strategy for proactive cybersecurity culture -- 8.6.5 Instruction and simulated settings…”

Tabla de Contenidos: “…Adding noise to images using diffusers -- Defining the UNet model -- Training the UNet model -- Defining the optimizer and learning schedule -- Using Hugging Face Accelerate to accelerate training -- Running the model training loop -- Generating realistic anime images using (reverse) diffusion -- Understanding text-to-image generation using diffusion -- Encoding text input into an embedding vector -- Ingesting additional text data in the (conditional) UNet model -- Using the Stable Diffusion model to generate images from text -- Summary -- Reference list -- Chapter 11: Deep Reinforcement Learning -- Reviewing RL concepts -- Types of RL algorithms -- Model-based -- Model-Free -- Discussing Q-learning -- Understanding deep Q-learning -- Using two separate DNNs -- Experience replay buffer -- Building a DQN model in PyTorch -- Initializing the main and target CNN models -- Defining the experience replay buffer -- Setting up the environment -- Defining the CNN optimization function -- Managing and running episodes -- Training the DQN model to learn Pong -- Summary -- Reference list -- Chapter 12: Model Training Optimizations -- Distributed training with PyTorch -- Training the MNIST model in a regular fashion -- Training the MNIST model in a distributed fashion -- Distributed training on GPUs with CUDA -- Automatic mixed precision training -- Regular model training on a GPU -- Mixed precision training on a GPU -- Summary -- Reference list -- Chapter 13: Operationalizing PyTorch Models into Production -- Model serving in PyTorch -- Creating a PyTorch model inference pipeline -- Saving and loading a trained model -- Building the inference pipeline -- Building a basic model server -- Writing a basic app using Flask -- Using Flask to build our model server -- Setting up model inference for Flask serving -- Building a Flask app to serve model…”

Tabla de Contenidos: “…4.3.3 TANGENTE DESDE UN PUNTO EXTERIOR -- 4.3.4 PROPIEDAD FOCAL DE LA PARÁBOLA Y SUS APLICACIONES -- 4.3.5 APLICACIÓN DE ARCOS PARABÓLICOS EN LA CONSTRUCCIÓN -- RESUMEN DE CONTENIDOS DE LA UNIDAD IV -- ACTIVIDADES DE LA UNIDAD IV -- ACTIVIDADES DE AUTOEVALUACIÓN DE LA UNIDAD IV -- BIBLIOGRAFÍA DE LA UNIDAD IV -- UNIDAD V - LA HIPÉRBOLA -- ORIENTACIONES DE LA UNIDAD V -- COMPETENCIAS DE LA UNIDAD V -- ESQUEMA DE CONTENIDOS DE LA UNIDAD V -- 5.1 HIPÉRBOLA -- 5.1.1 DEFINICIÓN -- 5.1.2 FOCOS -- 5.1.3 VÉRTICES -- 5.1.4 CENTRO -- 5.1.5 EJE TRANSVERSO -- 5.1.6 EJE CONJUGADO -- 5.1.7 LADOS RECTOS -- 5.1.8 ASÍNTOTAS -- 5.1.9 EXCENTRICIDAD -- 5.2 ECUACIONES DE LA HIPÉRBOLA -- 5.2.1 ECUACIÓN ORDINARIA O CANÓNICA -- 5.2.2 ECUACIÓN GENERAL -- 5.3 PROPIEDADES, PROBLEMAS Y APLICACIONES -- 5.3.1 RECTA TANGENTE A UNA HIPÉRBOLA -- 5.3.2 PROPIEDAD DE REFLEXIÓN -- 5.4 IDENTIFICACIÓN DE CÓNICAS MEDIANTE SU ECUACIÓN GENERAL -- RESUMEN DE CONTENIDOS DE LA UNIDAD V -- ACTIVIDADES DE LA UNIDAD V -- ACTIVIDADES DE AUTOEVALUACIÓN DE LA UNIDAD V -- BIBLIOGRAFÍA DE LA UNIDAD V -- UNIDAD VI - RECTAS Y PLANOS EN EL ESPACIO -- ORIENTACIONES PARA EL ESTUDIO DE LA UNIDAD VI -- COMPETENCIAS DE LA UNIDAD VI -- ESQUEMA DE CONTENIDOS DE LA UNIDAD VI -- 6.1 SISTEMA DE COORDENADAS RECTANGULARES PARA EL ESPACIO -- 6.2 DISTANCIA ENTRE DOS PUNTOS Y PUNTO MEDIO DE UN SEGMENTO -- 6.3 VECTORES EN EL ESPACIO -- 6.4 ECUACIÓN DE UNA RECTA EN EL ESPACIO -- 6.5 POSICIONES RELATIVAS DE DOS RECTAS EN EL ESPACIO -- 6.6 PLANOS -- 6.6.1 ECUACIÓN DE UN PLANO -- 6.6.2 CONDICIONES GEOMÉTRICAS QUE DETERMINAN UN PLANO -- 6.6.3 POSICIÓN RELATIVA DE DOS PLANOS -- 6.6.4 POSICIÓN RELATIVA ENTRE UN PLANO Y UNA RECTA -- RESUMEN DE CONTENIDOS DE LA UNIDAD VI -- ACTIVIDADES DE LA UNIDAD VI -- ACTIVIDADES DE AUTOEVALUACIÓN DE LA UNIDAD VI -- BIBLIOGRAFÍA DE LA UNIDAD VI…”

Tabla de Contenidos: “…Geometric Mean -- Chapter 2 Linear Algebraic Systems -- 2.1 Problem Definition and Vector Spaces -- 2.1.1 Vector Spaces in Tomographic Radiometric Inversion -- 2.2 Rotations -- 2.3 Projection Matrixes and Data-Filtering -- 2.3.1 Projections and Commercial FM Radio -- 2.4 Singular Value Decomposition (SVD) and Subspaces -- 2.4.1 How to Choose the Rank of A? …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Brief Contents -- Contents -- Examples and Applications -- Preface -- Part I: The Linear Regression Model -- CHAPTER 1 Econometrics -- 1.1 Introduction -- 1.2 The Paradigm of Econometrics -- 1.3 The Practice of Econometrics -- 1.4 Microeconometrics and Macroeconometrics -- 1.5 Econometric Modeling -- 1.6 Plan of the Book -- 1.7 Preliminaries -- 1.7.1 Numerical Examples -- 1.7.2 Software and Replication -- 1.7.3 Notational Conventions -- CHAPTER 2 The Linear Regression Model -- 2.1 Introduction -- 2.2 The Linear Regression Model -- 2.3 Assumptions of the Linear Regression Model -- 2.3.1 Linearity of the Regression Model -- 2.3.2 Full Rank -- 2.3.3 Regression -- 2.3.4 Homoscedastic and Nonautocorrelated Disturbances -- 2.3.5 Data Generating Process for the Regressors -- 2.3.6 Normality -- 2.3.7 Independence and Exogeneity -- 2.4 Summary and Conclusions -- CHAPTER 3 Least Squares Regression -- 3.1 Introduction -- 3.2 Least Squares Regression -- 3.2.1 The Least Squares Coefficient Vector -- 3.2.2 Application: An Investment Equation -- 3.2.3 Algebraic Aspects of the Least Squares Solution -- 3.2.4 Projection -- 3.3 Partitioned Regression and Partial Regression -- 3.4 Partial Regression and Partial Correlation Coefficients -- 3.5 Goodness of Fit and the Analysis of Variance -- 3.5.1 The Adjusted R-Squared and a Measure of Fit -- 3.5.2 R-Squared and the Constant Term in the Model -- 3.5.3 Comparing Models -- 3.6 Linearly Transformed Regression -- 3.7 Summary and Conclusions -- CHAPTER 4 Estimating the Regression Model by Least Squares -- 4.1 Introduction -- 4.2 Motivating Least Squares -- 4.2.1 Population Orthogonality Conditions -- 4.2.2 Minimum Mean Squared Error Predictor -- 4.2.3 Minimum Variance Linear Unbiased Estimation -- 4.3 Statistical Properties of the Least Squares Estimator…”

Tabla de Contenidos: “…. -- 2.3.4 Control Programming -- 2.3.5 Other Useful Functions -- 2.3.6 Calculus* -- 2.4 Vectors and Matrices in R -- 2.4.1 Vectors -- 2.4.2 Matrices -- 2.5 Data Entering and Reading from Files -- 2.5.1 Data Entering -- 2.5.2 Reading Data from External Files -- 2.6 Working with Packages -- 2.7 R Session Management -- 2.8 Further Reading -- 2.9 Complements, Problems, and Programs -- Chapter 3 Data Preparation and Other Tricks -- 3.1 Introduction -- 3.2 Manipulation with Complex Format Files -- 3.3 Reading Datasets of Foreign Formats -- 3.4 Displaying R Objects -- 3.5 Manipulation Using R Functions -- 3.6 Working with Time and Date -- 3.7 Text Manipulations -- 3.8 Scripts and Text Editors for R -- 3.8.1 Text Editors for Linuxians -- 3.9 Further Reading -- 3.10 Complements, Problems, and Programs -- Chapter 4 Exploratory Data Analysis…”

Tabla de Contenidos: “…12.5 HTML Files -- 12.6 Tar Files -- 12.7 GZip Files -- 12.8 Zip Files -- 12.9 Image Files: Rasterized, Vectorized, and/or Compressed -- 12.10 It's All Bytes at the End of the Day -- 12.11 Integers -- 12.12 Floats -- 12.13 Text Data -- 12.14 Further Reading -- 12.15 Glossary -- Chapter 13 Big Data -- 13.1 What Is Big Data? …”

Tabla de Contenidos: “…8.6.3 Technologies of Industry 5.0 -- 8.6.4 Limitations of Industry 5.0 -- 8.6.5 Future Direction -- 8.7 Conclusion -- References -- Chapter 9 The Role of Cutting-Edge Technologies in Revolutionary Industry 5.0 -- 9.1 Introduction -- 9.2 Industry 5.0 As Industrial Revolution -- 9.2.1 About Industry 5.0 -- 9.2.2 Industry 5.0: The Cutting-Edge Technology -- 9.2.3 Industry 5.0: The Way to Enable Technologies -- 9.2.3.1 Individualized Human-Machine Interaction -- 9.2.3.2 Bio-Inspired Technologies and Smart Materials -- 9.2.3.3 Digital Twins as Well as Simulation -- 9.2.3.4 Transmission of Data, Analysis and Storage -- 9.2.3.5 Artificial Intelligence -- 9.2.3.6 Efficiency of Technology, Storage, Renewables, and Autonomy -- 9.3 The Role Of Workers In The Industry 5.0 -- 9.3.1 Work-Related Stress and Disease -- 9.3.2 Emerging Risks -- 9.3.2.1 Wearables the Smart Ones -- 9.3.2.2 Cobots and Robots -- 9.3.2.3 Virtual Reality/Augmented Reality -- 9.3.2.4 ExoSkeleton -- 9.3.2.5 Digital Twin -- 9.3.2.6 Wireless Communication Technology -- 9.4 Path Toward Society 5.0 -- 9.5 Conclusion -- References -- Chapter 10 Managing Industry 5.0: The Next Frontier for Artificial Intelligence and Machine Learning Algorithms -- 10.1 Introduction -- 10.2 Supervised Learning -- 10.2.1 Decision Tree -- 10.2.2 Naïve Bayes -- 10.2.3 Support Vector Machine -- 10.3 Unsupervised Learning -- 10.4 Neural Networks Workflow -- 10.4.1 Principal Component Analysis -- 10.4.2 K-Means Clustering -- 10.5 Semi-Supervised Learning -- 10.5.1 Transductive Support Vector Machines -- 10.5.2 Generative Models -- 10.5.3 Self-Training -- 10.6 Reinforcement Learning -- 10.7 Multitasking Learning -- 10.8 Ensemble Learning -- 10.8.1 Boosting -- 10.8.2 Bagging -- 10.9 Deep Learning (Neural Network) -- 10.9.1 Supervised Neural Network -- 10.9.2 Unsupervised Neural Network -- 10.9.3 Reinforced Neural Network…”

Tabla de Contenidos: “…Mathematical foundations -- Vectors, matrices, and beyond: a linear algebra primer -- Vectors: one-dimensional data -- Matrices: two-dimensional data -- Rank 3 tensors -- Rank 4 tensors -- Calculus in five minutes: derivatives and finding maxima -- Appendix B. …”

Tabla de Contenidos: “…6.10.14 Case 4: Image-Based Features Extraction in Pavement Cracking Evaluation -- 6.10.15 Automatic Extraction of Crack Features -- 6.10.16 Extraction of Crack Skeleton Using Shearlet Complex Method -- 6.10.17 Calculate Crack Width Feature Using External Multiplication Method -- 6.10.18 Detection of Crack Starting Feature (Crack Core) Using EPA Emperor Penguin Metaheuristic Algorithm -- 6.10.19 Selection of Crack Root Feature Based on Geodetic Distance -- 6.10.20 Determining Coordinates of the Crack Core as the Optimal Center at the Failure Level using EPA Method -- 6.10.21 Development of New Features for Crack Evaluation Based on Graph Energy -- 6.10.22 Crack Homogeneity Feature Based on Graph Energy Theory -- 6.10.23 Spall Type 1 Feature: Crack Based on Graph Energy Theory in Crack Width Mode -- 6.10.24 General Crack Index Based on Graph Energy Theory -- 6.11 Summary and Conclusion -- 6.12 Questions and Exercises -- Further Reading -- Chapter 7 Feature Prioritization and Selection Methods -- 7.1 Introduction -- 7.2 A Variety of Features Selection Methods -- 7.2.1 Filter Methods -- 7.2.2 Correlation Criteria -- 7.2.3 Mutual Information (MI) -- 7.2.4 Wrapper Methods -- 7.2.5 Sequential Feature Selection (SFS) Algorithm -- 7.2.6 Heuristic Search Algorithm (HAS) -- 7.2.7 Embedded Methods -- 7.2.8 Hybrid Methods -- 7.2.9 Feature Selection Using the Fuzzy Entropy Method -- 7.2.10 Hybrid-Based Feature Selection Using the Hierarchical Fuzzy Entropy Method -- 7.2.11 Step 1: Measure Similarity Index and Evaluate Features -- 7.2.12 Step 2: Final Feature Vector -- 7.3 Classification Algorithm Based on Modified Support Vectors for Feature Selection - CDFESVM -- 7.3.1 Methods for Determining the Fuzzy Membership Function in Feature Selection -- 7.4 Summary and Conclusion -- 7.5Questions and Exercises -- Further Reading…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Foreword -- Acknowledgments -- General Introduction -- Chapter 1 Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning -- 1.1 Learning Styles for Machine Learning -- 1.1.1 Supervised Learning -- 1.1.1.1 Overfitting and Underfitting -- 1.1.1.2 K-Folds Cross-Validation -- 1.1.1.3 Train/Test Split -- 1.1.1.4 Confusion Matrix -- 1.1.1.5 Loss Functions -- 1.1.2 Unsupervised Learning -- 1.1.3 Semi-Supervised Learning -- 1.1.4 Reinforcement Learning -- 1.2 Essential Python Tools for Machine Learning -- 1.2.1 Data Manipulation with Python -- 1.2.2 Python Machine Learning Libraries -- 1.2.2.1 Scikit-learn -- 1.2.2.2 TensorFlow -- 1.2.2.3 Keras -- 1.2.2.4 PyTorch -- 1.2.3 Jupyter Notebook and JupyterLab -- 1.3 HephAIstos for Running Machine Learning on CPUs, GPUs, and QPUs -- 1.3.1 Installation -- 1.3.2 HephAIstos Function -- 1.4 Where to Find the Datasets and Code Examples -- Further Reading -- Chapter 2 Feature Engineering Techniques in Machine Learning -- 2.1 Feature Rescaling: Structured Continuous Numeric Data -- 2.1.1 Data Transformation -- 2.1.1.1 StandardScaler -- 2.1.1.2 MinMaxScaler -- 2.1.1.3 MaxAbsScaler -- 2.1.1.4 RobustScaler -- 2.1.1.5 Normalizer: Unit Vector Normalization -- 2.1.1.6 Other Options -- 2.1.1.7 Transformation to Improve Normal Distribution -- 2.1.1.8 Quantile Transformation -- 2.1.2 Example: Rescaling Applied to an SVM Model -- 2.2 Strategies to Work with Categorical (Discrete) Data -- 2.2.1 Ordinal Encoding -- 2.2.2 One-Hot Encoding -- 2.2.3 Label Encoding -- 2.2.4 Helmert Encoding -- 2.2.5 Binary Encoding -- 2.2.6 Frequency Encoding -- 2.2.7 Mean Encoding -- 2.2.8 Sum Encoding -- 2.2.9 Weight of Evidence Encoding -- 2.2.10 Probability Ratio Encoding -- 2.2.11 Hashing Encoding -- 2.2.12 Backward Difference Encoding…”

Tabla de Contenidos: “…ESTRUCTURA DE DATOS -- 2.1 ESTRUCTURAS ESTÁTICAS -- 2.1.1 ARRAYS O VECTORES -- 2.1.2 ARRAYS MULTIDIMENSIONALES O MATRICES -- 2.1.3 LAS CADENAS DE CARACTERES -- 2.2 ESTRUCTURAS DINÁMICAS -- 2.2.1 PILAS -- 2.2.2 COLAS -- 2.3 TIPOS ABSTRACTOS DE DATOS -- 3. …”

Tabla de Contenidos: “…Los gráficos: color, semiótica, tamaño y otros -- 6. Mapas de bits y vectores. Especificaciones…”

Tabla de Contenidos: “…Restricted Boltzmann Machines -- Energy function -- Encoding and decoding -- Contrastive divergence (CD-k) -- Stacked/continuous RBM -- RBM versus Boltzmann Machines -- Recurrent neural networks (RNN/LSTM) -- Cells in RNN and unrolling -- Backpropagation through time -- Vanishing gradient and LTSM -- Cells and gates in LTSM -- Step 1 - The forget gate -- Step 2 - Updating memory/cell state -- Step 3 - The output gate -- Practical examples -- TensorFlow setup and key concepts -- Handwritten digits recognition -- Summary -- Chapter 4: Deep Learning in Computer Vision -- Origins of CNNs -- Convolutional Neural Networks -- Data transformations -- Input preprocessing -- Data augmentation -- Network layers -- Convolution layer -- Pooling or subsampling layer -- Fully connected or dense layer -- Network initialization -- Regularization -- Loss functions -- Model visualization -- Handwritten digit classification example -- Fine-tuning CNNs -- Popular CNN architectures -- AlexNet -- Visual Geometry Group -- GoogLeNet -- ResNet -- Summary -- Chapter 5: NLP - Vector Representation -- Traditional NLP -- Bag of words -- Weighting the terms tf-idf -- Deep learning NLP -- Motivation and distributed representation -- Word embeddings -- Idea of word embeddings -- Advantages of distributed representation -- Problems of distributed representation -- Commonly used pre-trained word embeddings -- Word2Vec -- Basic idea of Word2Vec -- The word windows -- Generating training data -- Negative sampling -- Hierarchical softmax -- Other hyperparameters -- Skip-Gram model -- The input layer -- The hidden layer -- The output layer -- The loss function -- Continuous Bag-of-Words model -- Training a Word2Vec using TensorFlow -- Using existing pre-trained Word2Vec embeddings -- Word2Vec from Google News -- Using the pre-trained Word2Vec embeddings -- Understanding GloVe -- FastText…”

Tabla de Contenidos: “…Basic questions for data exploration -- Dataset 1 - Yelp -- Dataframes -- Series -- Exploration tips for qualitative data -- Dataset 2 - titanic -- Summary -- Chapter 4: Basic Mathematics -- Mathematics as a discipline -- Basic symbols and terminology -- Vectors and matrices -- Quick exercises -- Answers -- Arithmetic symbols -- Summation -- Proportional -- Dot product -- Graphs -- Logarithms/exponents -- Set theory -- Linear algebra -- Matrix multiplication -- How to multiply matrices -- Summary -- Chapter 5: Impossible or Improbable - A Gentle Introduction to Probability -- Basic definitions -- Probability -- Bayesian versus Frequentist -- Frequentist approach -- The law of large numbers -- Compound events -- Conditional probability -- The rules of probability -- The addition rule -- Mutual exclusivity -- The multiplication rule -- Independence -- Complementary events -- A bit deeper -- Summary -- Chapter 6: Advanced Probability -- Collectively exhaustive events -- Bayesian ideas revisited -- Bayes theorem -- More applications of Bayes theorem -- Example - Titanic -- Example - medical studies -- Random variables -- Discrete random variables -- Types of discrete random variables -- Summary -- Chapter 7: Basic Statistics -- What are statistics? …”

Tabla de Contenidos: “…Calling Assembly from Python -- Summary -- Exercises -- Chapter 10: Multiply and Divide -- Multiplication -- Examples -- Division -- Division by Zero and Overflow -- Example -- Example: Matrix Multiplication -- Vectors and Matrices -- Multiplying 3x3 Integer Matrices -- Accessing Matrix Elements -- Register Usage -- Summary -- Exercises -- Chapter 11: Floating-Point Operations -- About Floating Point Numbers -- About Normalization and NaNs -- Recognizing Rounding Errors -- Defining Floating Point Numbers -- About Floating Point Registers -- The Status and Control Register -- Defining the Function Call Protocol -- Loading and Saving FPU Registers -- Performing Basic Arithmetic -- Calculating Distance Between Points -- Performing Floating-Point Conversions -- Floating-Point Sign Injection -- Comparing Floating-Point Numbers -- Example -- Summary -- Exercises -- Chapter 12: Optimizing Code -- Optimizing the Uppercase Routine -- Simplifying the Range Comparison -- Restricting the Problem Domain -- Tips for Optimizing Code -- Avoiding Branch Instructions -- Moving Code Out of Loops -- Avoiding Expensive Instructions -- Use Macros -- Loop Unrolling -- Delay Preserving Registers in Functions -- Keeping Data Small -- Beware of Overheating -- Summary -- Exercises -- Chapter 13: Reading and Understanding Code -- Browsing Linux &amp -- GCC Code -- Comparing Strings -- About the Algorithm -- Macros and Kernel Options -- Code Created by GCC -- Reverse Engineering and Ghidra -- Summary -- Exercises -- Chapter 14: Hacking Code -- Buffer Overrun Hack -- Causes of Buffer Overrun -- Stealing Credit Card Numbers -- Stepping Through the Stack -- Mitigating Buffer Overrun Vulnerabilities -- Do Not Use strcpy -- PIE Is Good -- Poor Stack Canaries Are the First to Go -- Preventing Code Running on the Stack -- Tradeoffs of Buffer Overflow Mitigation Techniques -- Summary…”

Tabla de Contenidos: “…-- Summary -- Chapter 2: Types of Data -- Structured versus unstructured data -- Quantitative versus qualitative data -- Digging deeper -- The four levels of data -- The nominal level -- Measures of center -- The ordinal level -- The interval level -- The ratio level -- Data is in the eye of the beholder -- Summary -- Questions and answers -- Chapter 3: The Five Steps of Data Science -- Introduction to data science -- Overview of the five steps -- Exploring the data -- Guiding questions for data exploration -- DataFrames -- Series -- Exploration tips for qualitative data -- Summary -- Chapter 4: Basic Mathematics -- Basic symbols and terminology -- Vectors and matrices -- Arithmetic symbols -- Summation -- Logarithms/exponents -- Set theory -- Linear algebra -- Matrix multiplication -- How to multiply matrices together -- Summary -- Chapter 5: Impossible or Improbable - A Gentle Introduction to Probability -- Basic definitions -- What do we mean by "probability"? …”

Tabla de Contenidos: “…-- A Model for Ransomware Simulation -- Asymmetric Cryptography -- Remote Key Generation -- Targeting Files -- Requesting the Ransom -- Maintaining C2 -- Final Thoughts -- Command and Control Part V: Creating a Covert C2 Solution -- Introducing the Onion Router -- The Torrc File -- Configuring a C2 Agent to Use the Tor Network -- Bridges -- New Strategies in Stealth and Deployment -- VBA Redux: Alternative Command-Line Attack Vectors -- PowerShell -- FTP -- Windows Scripting Host (WSH) -- BITSadmin -- Simple Payload Obfuscation -- Alternative Strategies in Antivirus Evasion -- The Attack -- Gun Design Engineer Answers Your Questions…”

Tabla de Contenidos: “…flatMapValues -- repartitionAndSortWithinPartitions -- coalesce -- foldByKey -- aggregateByKey -- combineByKey -- Advanced actions -- Approximate actions -- Asynchronous actions -- Miscellaneous actions -- Shared variable -- Broadcast variable -- Properties of the broadcast variable -- Lifecycle of a broadcast variable -- Map-side join using broadcast variable -- Accumulators -- Driver program -- Summary -- Chapter 8: Working with Spark SQL -- SQLContext and HiveContext -- Initializing SparkSession -- Reading CSV using SparkSession -- Dataframe and dataset -- SchemaRDD -- Dataframe -- Dataset -- Creating a dataset using encoders -- Creating a dataset using StructType -- Unified dataframe and dataset API -- Data persistence -- Spark SQL operations -- Untyped dataset operation -- Temporary view -- Global temporary view -- Spark UDF -- Spark UDAF -- Untyped UDAF -- Type-safe UDAF: -- Hive integration -- Table Persistence -- Summary -- Chapter 9: Near Real-Time Processing with Spark Streaming -- Introducing Spark Streaming -- Understanding micro batching -- Getting started with Spark Streaming jobs -- Streaming sources -- fileStream -- Kafka -- Streaming transformations -- Stateless transformation -- Stateful transformation -- Checkpointing -- Windowing -- Transform operation -- Fault tolerance and reliability -- Data receiver stage -- File streams -- Advanced streaming sources -- Transformation stage -- Output stage -- Structured Streaming -- Recap of the use case -- Structured streaming - programming model -- Built-in input sources and sinks -- Input sources -- Built-in Sinks -- Summary -- Chapter 10: Machine Learning Analytics with Spark MLlib -- Introduction to machine learning -- Concepts of machine learning -- Datatypes -- Machine learning work flow -- Pipelines -- Operations on feature vectors -- Feature extractors -- Feature transformers…”