Tabla de Contenidos: “….) -- 8.1.1 LENGUAJE MÁQUINA -- 8.1.2 LENGUAJES DE BAJO NIVEL O ENSAMBLADOR 8.1.3 LENGUAJES DE ALTO NIVEL -- 8.2.1 PROGRAMA -- 8.2.2 TRADUCTORES DE LENGUAJE -- 8.2.3 ALGORITMOS -- 8.2.4 VARIABLES 8.3.1 PROGRAMACIÓN MODULAR 8.3.2 PROGRAMACIÓN ESTRUCTURADA -- 8.4 ESTRUCTURAS BÁSICAS DE CONTROL -- 8.4.1 SECUENCIA 8.4.2 SELECCIÓN -- 8.4.4 ACUMULADORES 8.4.3 ITERACIÓN -- 8.6.1 SÍMBOLOS BÁSICOS DE UN DIAGRAMA DE FLUJO -- 8.6.2 REGLAS PARA HACER DIAGRAMAS DE FLUJO CONECTOR EN DIFERENTE PÁGINA -- 8.6.3 EJEMPLOS DE DIAGRAMAS DE FLUJO -- UNIDAD 9 PROGRAMACIÓN EN C++ -- 9.1 ESTRUCTURA DE UN PROGRAMA 9.2 ELEMENTOS DE UN PROGRAMA EN C++ -- 9.2 ELEMENTOS DE UN PROGRAMA EN C++ -- 9.2.1 COMENTARIOS 9.2.3 IDENTIFICADORES 9.2.2 PALABRAS RESERVADAS -- 9.2.2 PALABRAS RESERVADAS -- 9.2.3 IDENTIFICADORES -- 9.2.4 TIPOS DE DATOS -- 9.2.5 VARIABLES -- 9.2.6 CONSTANTES -- 9.2.7 LIBRERÍAS -- 9.2.8 OPERADORES 9.2.7 LIBRERÍAS -- 9.3 FUNCIONES -- 9.4 INSTRUCCIONES BÁSICAS DEL LENGUAJE C -- 9.5 CICLOS -- 9.6 ARREGLOS -- 9.6.1 VECTORES -- 9.6.2 MATRICES -- REFERENCIAS DOCUMENTALES -- SOBRE LA OBRA…”

Tabla de Contenidos: “…ENDIF -- 2.4.1.3 LA ESTRUCTURA DE DECISIÓN MÚLTIPLE SWITCH -- 2.4.1.4 LA FUNCIÓN MENU -- 2.4.2 ESTRUCTURAS DE REPETICIÓN -- 2.4.2.1 LA ESTRUCTURA WHILE -- 2.4.2.2 LA ESTRUCTURA DO...UNTIL -- 2.4.2.3 LA ESTRUCTURA FOR -- 2.4.3 APLICACIONES -- 2.4.3.1 APLICACIÓN 1: ORDENAMIENTO TERRITORIAL -- 2.4.3.2 APLICACIÓN 2: PUENTES -- 2.4.4 COMENTARIOS FINALES -- 2.4.5 EJERCICIOS -- 2.5 BIBLIOGRAFÍA -- UNIDAD 3: INTRODUCCIÓN A LA PROGRAMACIÓN - PARTE II -- 3.1 FUNCIONES -- 3.1.1 FUNCIONES DE BIBLIOTECA -- 3.1.2 FUNCIONES DEFINIDAS POR EL USUARIO -- 3.1.3 APLICACIÓN: UN EJEMPLO DE PROGRAMACIÓN PASO A PASO -- 3.1.4 EJERCICIOS -- 3.2 ARREGLOS -- 3.2.1 VECTORES -- 3.2.2 MATRICES -- 3.2.3 OPERACIONES MATEMÁTICAS -- 3.2.4 FUNCIONES QUE ACTÚAN SOBRE ARREGLOS -- 3.2.5 OPERADOR DOS PUNTOS (:) -- 3.2.6 APLICACIONES -- 3.2.6.1 APLICACIÓN 1: DIVISIÓN DE POLINOMIOS -- 3.2.6.2 APLICACIÓN 2: HIDROLOGÍA -- 3.2.6.3 APLICACIÓN 3: IMÁGENES SATELITALES -- 3.2.7 EJERCICIOS -- 3.3 LECTURA Y ESCRITURA DE ARCHIVOS -- 3.3.1 LECTURA/ESCRITURA ESTILO OCTAVE -- 3.3.1.1 FUNCIÓN SAVE -- 3.3.1.2 FUNCIÓN LOAD -- 3.3.2 LECTURA/ESCRITURA ESTILO C -- 3.3.2.1 FUNCIÓN FOPEN -- 3.3.2.2 FUNCIÓN FPRINTF -- 3.3.2.3 FUNCIÓN FSCANF -- 3.3.2.4 FUNCIÓN FCLOSE -- 3.3.3 APLICACIÓN: ESTUDIO DE TRÁNSITO -- 3.3.3.1 SOLUCIÓN -- 3.3.4 EJERCICIOS -- 3.4 GRÁFICOS CIENTÍFICOS -- 3.4.1 FUNCIONES GRÁFICAS 2D…”

Tabla de Contenidos:

Tabla de Contenidos: “…Starting Nexpose -- Start a scan -- Exploitation -- Post-exploitation -- Infrastructure analysis -- Pillaging -- High-profile targets -- Data exfiltration -- Persistence -- Further penetration into infrastructure -- Cleanup -- Reporting -- Executive summary -- Technical report -- Penetration testing limitations and challenges -- Pentesting maturity and scoring model -- Realism -- Methodology -- Reporting -- Summary -- Chapter 2: Advanced Linux Exploitation -- Linux basics -- Linux commands -- Streams -- Redirection -- Linux directory structure -- Users and groups -- Permissions -- The chmod command -- The chown command -- The chroot command -- The power of the find command -- Jobs, cron, and crontab -- Security models -- Security controls -- Access control models -- Linux attack vectors -- Linux enumeration with LinEnum -- OS detection with Nmap -- Privilege escalation -- Linux privilege checker -- Linux kernel exploitation -- UserLand versus kernel land -- System calls -- Linux kernel subsystems -- Process -- Threads -- Security-Enhanced Linux -- Memory models and the address spaces -- Linux kernel vulnerabilities -- NULL pointer dereference -- Arbitrary kernel read/write -- Case study CVE-2016-2443 Qualcomm MSM debug fs kernel arbitrary write -- Memory corruption vulnerabilities -- Kernel stack vulnerabilities -- Kernel heap vulnerabilities -- Race conditions -- Logical and hardware-related bugs -- Case study CVE-2016-4484 - Cryptsetup Initrd root Shell -- Linux Exploit Suggester -- Buffer overflow prevention techniques -- Address space layout randomization -- Stack canaries -- Non-executable stack -- Linux return oriented programming -- Linux hardening -- Summary -- Chapter 3: Corporate Network and Database Exploitation -- Networking fundamentals -- Network topologies -- Bus topology -- Star topology -- Ring topology -- Tree topology…”

Tabla de Contenidos: “…-- Performance properties -- Performance testing - best practices -- Knowing your code and hot spots -- Profilers -- Instrumentation profilers -- Sampling profilers -- Summary -- Chapter 4: Data Structures -- Properties of computer memory -- STL containers -- Sequence containers -- Vector and array -- Deque -- List and forward_list -- The basic_string -- Associative containers -- Ordered sets and maps -- Unordered sets and maps -- Hash and equals -- Hash policy -- Container adaptors -- Priority queues -- Parallel arrays -- Summary -- Chapter 5: A Deeper Look at Iterators -- The iterator concept -- Iterator categories -- Pointer-mimicking syntax -- Iterators as generators -- Iterator traits -- Implementing a function using iterator categories…”

Tabla de Contenidos: “…Web Application Vulnerability Scanning -- Developing a Remediation Workflow -- Prioritizing Remediation -- Testing and Implementing Fixes -- Overcoming Barriers to Vulnerability Scanning -- Summary -- Exam Essentials -- Lab Exercises -- Activity 4.1: Installing a Vulnerability Scanner -- Activity 4.2: Running a Vulnerability Scan -- Activity 4.3: Developing a Penetration Test Vulnerability Scanning Plan -- Review Questions -- Chapter 5 Analyzing Vulnerability Scans -- Reviewing and Interpreting Scan Reports -- Understanding CVSS -- Validating Scan Results -- False Positives -- Documented Exceptions -- Understanding Informational Results -- Reconciling Scan Results with Other Data Sources -- Trend Analysis -- Common Vulnerabilities -- Server and Endpoint Vulnerabilities -- Network Vulnerabilities -- Virtualization Vulnerabilities -- Internet of Things (IoT) -- Web Application Vulnerabilities -- Summary -- Exam Essentials -- Lab Exercises -- Activity 5.1: Interpreting a Vulnerability Scan -- Activity 5.2: Analyzing a CVSS Vector -- Activity 5.3: Developing a Penetration Testing Plan -- Review Questions -- Chapter 6 Exploiting and Pivoting -- Exploits and Attacks -- Choosing Targets -- Enumeration -- Identifying the Right Exploit -- Exploit Resources -- Exploitation Toolkits -- Metasploit -- PowerSploit -- BloodHound -- Exploit Specifics -- RPC/DCOM -- PsExec -- PS Remoting/WinRM -- WMI -- Fileless Malware and Living Off the Land -- Scheduled Tasks and cron Jobs -- SMB -- DNS -- RDP -- Apple Remote Desktop -- VNC -- SSH -- Network Segmentation Testing and Exploits -- Leaked Keys -- Leveraging Exploits -- Common Post-Exploit Attacks -- Cross Compiling -- Privilege Escalation -- Social Engineering -- Escaping and Upgrading Limited Shells -- Persistence and Evasion -- Scheduled Jobs and Scheduled Tasks -- Inetd Modification -- Daemons and Services…”

Tabla de Contenidos: “…Chapter 15 Dealing with Common Datasets -- Understanding the Need for Standard Datasets -- Finding the Right Dataset -- Locating general dataset information -- Using library-specific datasets -- Loading a Dataset -- Working with toy datasets -- Creating custom data -- Fetching common datasets -- Manipulating Dataset Entries -- Determining the dataset content -- Creating a DataFrame -- Accessing specific records -- Part 5 Performing Simple Error Trapping -- Chapter 16 Handling Errors in Haskell -- Defining a Bug in Haskell -- Considering recursion -- Understanding laziness -- Using unsafe functions -- Considering implementation-specific issues -- Understanding the Haskell-Related Errors -- Fixing Haskell Errors Quickly -- Relying on standard debugging -- Understanding errors versus exceptions -- Chapter 17 Handling Errors in Python -- Defining a Bug in Python -- Considering the sources of errors -- Considering version differences -- Understanding the Python-Related Errors -- Dealing with late binding closures -- Using a variable -- Working with third-party libraries -- Fixing Python Errors Quickly -- Understanding the built-in exceptions -- Obtaining a list of exception arguments -- Considering functional style exception handling -- Part 6 The Part of Tens -- Chapter 18 Ten Must-Have Haskell Libraries -- binary -- Hascore -- vect -- vector -- aeson -- attoparsec -- bytestring -- stringsearch -- text -- moo -- Chapter 19 Ten (Plus) Must-Have Python Packages -- Gensim -- PyAudio -- PyQtGraph -- TkInter -- PrettyTable -- SQLAlchemy -- Toolz -- Cloudera Oryx -- funcy -- SciPy -- XGBoost -- Chapter 20 Ten Occupation Areas that Use Functional Programming -- Starting with Traditional Development -- Going with New Development -- Creating Your Own Development -- Finding a Forward-Thinking Business -- Doing Something Really Interesting…”

Tabla de Contenidos: “…. -- See also -- Chapter 11: Creating and Visualizing Word Vectors Using Word2Vec -- Introduction -- Acquiring data -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Importing the necessary libraries -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Preparing the data -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Building and training the model -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Visualizing further -- Getting ready -- How to do it... -- How it works…”

Tabla de Contenidos: “…Where Is Help? -- 1.3. Vectors and Matrixes -- 1.4. Matrix Operations -- 1.4.1. …”

Tabla de Contenidos: “…. -- Chapter 6: Recurrent Neural Networks -- Introduction -- Vanishing and exploding gradients -- Long Short Term Memory (LSTM) -- Gated Recurrent Units (GRUs) and Peephole LSTM -- Operating on sequences of vectors -- Neural machine translation - training a seq2seq RNN -- Getting ready -- How to do it... -- How it works... -- Neural machine translation - inference on a seq2seq RNN -- How to do it... -- How it works... -- All you need is attention - another example of a seq2seq RNN -- How to do it... -- How it works... -- There's more... -- Learning to write as Shakespeare with RNNs -- How to do it... -- How it works... -- First iteration -- After a few iterations -- There's more... -- Learning to predict future Bitcoin value with RNNs -- How to do it... -- How it works... -- There's more... -- Many-to-one and many-to-many RNN examples -- How to do it... -- How it works... -- Chapter 7: Unsupervised Learning -- Introduction -- Principal component analysis -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also…”

Tabla de Contenidos: “…-- A bit of history -- R's points of strength -- Open source inside -- Plugin ready -- Data visualization friendly -- Installing R and writing R code -- Downloading R -- R installation for Windows and macOS -- R installation for Linux OS -- Main components of a base R installation -- Possible alternatives to write and run R code -- RStudio (all OSs) -- The Jupyter Notebook (all OSs) -- Visual Studio (Windows users only) -- R foundational notions -- A preliminary R session -- Executing R interactively through the R console -- Creating an R script -- Executing an R script -- Vectors -- Lists -- Creating lists -- Subsetting lists -- Data frames -- Functions -- R's weaknesses and how to overcome them -- Learning R effectively and minimizing the effort -- The tidyverse -- Leveraging the R community to learn R -- Where to find the R community -- Engaging with the community to learn R -- Handling large datasets with R -- Further references -- Summary -- Chapter 2: A First Primer on Data Mining Analysing Your Bank Account Data -- Acquiring and preparing your banking data -- Data model -- Summarizing your data with pivot-like tables -- A gentle introduction to the pipe operator -- An even more gentle introduction to the dplyr package -- Installing the necessary packages and loading your data into R -- Installing and loading the necessary packages -- Importing your data into R -- Defining the monthly and daily sum of expenses -- Visualizing your data with ggplot2 -- Basic data visualization principles -- Less but better -- Not every chart is good for your message -- Scatter plot -- Line chart -- Bar plot -- Other advanced charts…”

Tabla de Contenidos: “…Sampling by time window -- Extracting audio signatures -- Building a song analyzer -- Selling data science is all about selling cupcakes -- Using Cassandra -- Using the Play framework -- Building a recommender -- The PageRank algorithm -- Building a Graph of Frequency Co-occurrence -- Running PageRank -- Building personalized playlists -- Expanding our cupcake factory -- Building a playlist service -- Leveraging the Spark job server -- User interface -- Summary -- Chapter 9: News Dictionary and Real-Time Tagging System -- The mechanical Turk -- Human intelligence tasks -- Bootstrapping a classification model -- Learning from Stack Exchange -- Building text features -- Training a Naive Bayes model -- Laziness, impatience, and hubris -- Designing a Spark Streaming application -- A tale of two architectures -- The CAP theorem -- The Greeks are here to help -- Importance of the Lambda architecture -- Importance of the Kappa architecture -- Consuming data streams -- Creating a GDELT data stream -- Creating a Kafka topic -- Publishing content to a Kafka topic -- Consuming Kafka from Spark Streaming -- Creating a Twitter data stream -- Processing Twitter data -- Extracting URLs and hashtags -- Keeping popular hashtags -- Expanding shortened URLs -- Fetching HTML content -- Using Elasticsearch as a caching layer -- Classifying data -- Training a Naive Bayes model -- Thread safety -- Predict the GDELT data -- Our Twitter mechanical Turk -- Summary -- Chapter 10: Story De-duplication and Mutation -- Detecting near duplicates -- First steps with hashing -- Standing on the shoulders of the Internet giants -- Simhashing -- The hamming weight -- Detecting near duplicates in GDELT -- Indexing the GDELT database -- Persisting our RDDs -- Building a REST API -- Area of improvement -- Building stories -- Building term frequency vectors…”

Tabla de Contenidos: “…schedule_timeout() -- Conclusion -- 12 Memory Management -- Pages -- Zones -- Getting Pages -- Getting Zeroed Pages -- Freeing Pages -- kmalloc() -- gfp_mask Flags -- kfree() -- vmalloc() -- Slab Layer -- Design of the Slab Layer -- Slab Allocator Interface -- Statically Allocating on the Stack -- Single-Page Kernel Stacks -- Playing Fair on the Stack -- High Memory Mappings -- Permanent Mappings -- Temporary Mappings -- Per-CPU Allocations -- The New percpu Interface -- Per-CPU Data at Compile-Time -- Per-CPU Data at Runtime -- Reasons for Using Per-CPU Data -- Picking an Allocation Method -- Conclusion -- 13 The Virtual Filesystem -- Common Filesystem Interface -- Filesystem Abstraction Layer -- Unix Filesystems -- VFS Objects and Their Data Structures -- The Superblock Object -- Superblock Operations -- The Inode Object -- Inode Operations -- The Dentry Object -- Dentry State -- The Dentry Cache -- Dentry Operations -- The File Object -- File Operations -- Data Structures Associated with Filesystems -- Data Structures Associated with a Process -- Conclusion -- 14 The Block I/O Layer -- Anatomy of a Block Device -- Buffers and Buffer Heads -- The bio Structure -- I/O vectors -- The Old Versus the New -- Request Queues -- I/O Schedulers -- The Job of an I/O Scheduler -- The Linus Elevator -- The Deadline I/O Scheduler -- The Anticipatory I/O Scheduler -- The Complete Fair Queuing I/O Scheduler -- The Noop I/O Scheduler -- I/O Scheduler Selection -- Conclusion -- 15 The Process Address Space -- Address Spaces -- The Memory Descriptor -- Allocating a Memory Descriptor -- Destroying a Memory Descriptor -- The mm_struct and Kernel Threads -- Virtual Memory Areas -- VMA Flags -- VMA Operations -- Lists and Trees of Memory Areas -- Memory Areas in Real Life -- Manipulating Memory Areas -- find_vma() -- find_vma_prev() -- find_vma_intersection()…”

Tabla de Contenidos: “…. -- Computing the distance between two color vectors -- Using OpenCV functions -- The functor or function object -- The OpenCV base class for algorithms -- See also -- Segmenting an image with the GrabCut algorithm -- How to do it... -- How it works... -- See also -- Converting color representations -- Getting ready -- How to do it... -- How it works... -- See also -- Representing colors with hue, saturation, and brightness -- How to do it... -- How it works... -- There's more... -- Using colors for detection - skin tone detection -- Chapter 4: Counting the Pixels with Histograms -- Computing the image histogram -- Getting started -- How to do it... -- How it works... -- There's more... -- Computing histograms of color images -- See also -- Applying lookup tables to modify the image's appearance -- How to do it... -- How it works... -- There's more... -- Stretching a histogram to improve the image contrast -- Applying a lookup table on color images -- Equalizing the image histogram -- How to do it... -- How it works... -- Backprojecting a histogram to detect specific image content -- How to do it... -- How it works... -- There's more... -- Backprojecting color histograms -- Using the mean shift algorithm to find an object -- How to do it... -- How it works... -- See also -- Retrieving similar images using histogram comparison -- How to do it... -- How it works... -- See also -- Counting pixels with integral images -- How to do it... -- How it works... -- There's more... -- Adaptive thresholding -- Visual tracking using histograms -- See also -- Chapter 5: Transforming Images with Morphological Operations -- Eroding and dilating images using morphological filters -- Getting ready…”

Tabla de Contenidos: “…-- Übungen -- Kapitel 3: Klassifikation -- MNIST -- Trainieren eines binären Klassifikators -- Qualitätsmaße -- Messen der Genauigkeit über Kreuzvalidierung -- Konfusionsmatrix -- Relevanz und Sensitivität -- Die Wechselbeziehung zwischen Relevanz und Sensitivität -- Die ROC-Kurve -- Klassifikatoren mit mehreren Kategorien -- Fehleranalyse -- Klassifikation mit mehreren Labels -- Klassifikation mit mehreren Ausgaben -- Übungen -- Kapitel 4: Trainieren von Modellen -- Lineare Regression -- Die Normalengleichung -- Komplexität der Berechnung -- Das Gradientenverfahren -- Batch-Gradientenverfahren -- Stochastisches Gradientenverfahren -- Mini-Batch-Gradientenverfahren -- Polynomielle Regression -- Lernkurven -- Regularisierte lineare Modelle -- Ridge-Regression -- Lasso-Regression -- Elastic Net -- Early Stopping -- Logistische Regression -- Abschätzen von Wahrscheinlichkeiten -- Trainieren und Kostenfunktion -- Entscheidungsgrenzen -- Softmax-Regression -- Übungen -- Kapitel 5: Support Vector Machines -- Lineare Klassifikation mit SVMs -- Soft-Margin-Klassifikation -- Nichtlineare SVM-Klassifikation -- Polynomieller Kernel -- Ähnlichkeitsbasierte Merkmale -- Der gaußsche RBF-Kernel -- Komplexität der Berechnung -- SVM-Regression -- Hinter den Kulissen -- Entscheidungsfunktion und Vorhersagen -- Zielfunktionen beim Trainieren -- Quadratische Programme -- Das duale Problem -- Kernel-SVM -- Online-SVMs -- Übungen -- Kapitel 6: Entscheidungsbäume -- Trainieren und Visualisieren eines Entscheidungsbaums -- Vorhersagen treffen -- Schätzen von Wahrscheinlichkeiten für Kategorien -- Der CART-Trainingsalgorithmus -- Komplexität der Berechnung -- Gini-Unreinheit oder Entropie? …”

Tabla de Contenidos: “…3.2.4 Rehabilitation Robot for Gait Training -- 3.3 Solutions and Methods for the Rehabilitation Process -- 3.3.1 Gait Analysis -- 3.3.2 Methods Based on Deep Learning -- 3.3.3 Use of Convolutional Neural Networks -- 3.4 Proposed System -- 3.4.1 Detection of Motion and Rehabilitation Mechanism -- 3.4.2 Data Collection Using Wearable Sensors -- 3.4.3 Raspberry Pi -- 3.4.4 Pre-Processing of the Data -- 3.5 Analysis of the Data -- 3.5.1 Feature Extraction -- 3.5.2 Machine Learning Approach -- 3.5.3 Remote Rehabilitation Mode -- 3.6 Results and Discussion -- 3.7 Conclusion -- References -- 4 Smart Sensors for Activity Recognition -- 4.1 Introduction -- 4.2 Wearable Biosensors for Activity Recognition -- 4.3 Smartphones for Activity Recognition -- 4.3.1 Early Analysis Activity Recognition -- 4.3.2 Similar Approaches Activity Recognition -- 4.3.3 Multi-Sensor Approaches Activity Recognition -- 4.3.4 Fitness Systems in Activity Recognition -- 4.3.5 Human-Computer Interaction Processes in Activity Recognition -- 4.3.6 Healthcare Monitoring in Activity Recognition -- 4.4 Machine Learning Techniques -- 4.4.1 Decision Trees Algorithms for Activity Reorganization -- 4.4.2 Adaptive Boost Algorithms for Activity Reorganization -- 4.4.3 Random Forest Algorithms for Activity Reorganization -- 4.4.4 Support Vector Machine (SVM) Algorithms for Activity Reorganization -- 4.5 Other Applications -- 4.6 Limitations -- 4.6.1 Policy Implications and Recommendations -- 4.7 Discussion -- 4.8 Conclusion -- References -- 5 Use of Assistive Techniques for the Visually Impaired People -- 5.1 Introduction -- 5.2 Rehabilitation Procedure -- 5.3 Development of Applications for Visually Impaired -- 5.4 Academic Research and Development for Assisting Visually Impaired -- 5.5 Conclusion -- References -- 6 IoT-Assisted Smart Device for Blind People -- 6.1 Introduction…”

Tabla de Contenidos: “…Advanced driver assistance systems (ADAS) -- Summary -- Chapter 3: Exploring ML Algorithms -- Technical requirements -- How machines learn -- Overview of ML algorithms -- Consideration for choosing ML algorithms -- Algorithms for classification and regression problems -- Linear regression algorithms -- Logistic regression algorithms -- Decision tree algorithms -- Random forest algorithm -- Gradient boosting machine and XGBoost algorithms -- K-nearest neighbor algorithm -- Multi-layer perceptron (MLP) networks -- Algorithms for clustering -- Algorithms for time series analysis -- ARIMA algorithm -- DeepAR algorithm -- Algorithms for recommendation -- Collaborative filtering algorithm -- Multi-armed bandit/contextual bandit algorithm -- Algorithms for computer vision problems -- Convolutional neural networks -- ResNet -- Algorithms for natural language processing (NLP) problems -- Word2Vec -- BERT -- Generative AI algorithms -- Generative adversarial network -- Generative pre-trained transformer (GPT) -- Large Language Model -- Diffusion model -- Hands-on exercise -- Problem statement -- Dataset description -- Setting up a Jupyter Notebook environment -- Running the exercise -- Summary -- Chapter 4: Data Management for ML -- Technical requirements -- Data management considerations for ML -- Data management architecture for ML -- Data storage and management -- AWS Lake Formation -- Data ingestion -- Kinesis Firehose -- AWS Glue -- AWS Lambda -- Data cataloging -- AWS Glue Data Catalog -- Custom data catalog solution -- Data processing -- ML data versioning -- S3 partitions -- Versioned S3 buckets -- Purpose-built data version tools -- ML feature stores -- Data serving for client consumption -- Consumption via API -- Consumption via data copy -- Special databases for ML -- Vector databases -- Graph databases -- Data pipelines…”

Tabla de Contenidos: “…Compromising biometric authentication -- Password cracking with GANs -- Malware detection evasion -- GANs in cryptography and stenography -- Generating web attack payloads with GANs -- Generating adversarial attack payloads -- Defenses and mitigations -- Securing GANs -- GAN-assisted adversarial attacks -- Deepfakes, malicious content, and misinformation -- Summary -- Chapter 13: LLM Foundations for Adversarial AI -- A brief introduction to LLMs -- Developing AI applications with LLMs -- Hello LLM with Python -- Hello LLM with LangChain -- Bringing your own data -- How LLMs change Adversarial AI -- Summary -- Chapter 14: Adversarial Attacks with Prompts -- Adversarial inputs and prompt injection -- Direct prompt injection -- Prompt override -- Style injection -- Role-playing -- Impersonation -- Other jailbreaking techniques -- Automated gradient-based prompt injection -- Risks from bringing your own data -- Indirect prompt injection -- Data exfiltration with prompt injection -- Privilege escalation with prompt injection -- RCE with prompt injection -- Defenses and mitigations -- LLM platform defenses -- Application-level defenses -- Summary -- Chapter 15: Poisoning Attacks and LLMs -- Poisoning embeddings in RAG -- Attack scenarios -- Poisoning during embedding generation -- Direct embeddings poisoning -- Advanced embeddings poisoning -- Query embeddings manipulation -- Defenses and mitigations -- Poisoning attacks on fine-tuning LLMs -- Introduction to fine-tuning LLMs -- Fine-tuning poisoning attack scenarios -- Fine-tuning attack vectors -- Poisoning ChatGPT 3.5 with fine-tuning -- Defenses and mitigations against poisoning attacks in fine-tuning -- Summary -- Chapter 16: Advanced Generative AI Scenarios -- Supply-chain attacks in LLMs -- Publishing a poisoned LLM on Hugging Face -- Publishing a tampered LLM on Hugging Face…”

Tabla de Contenidos: “…Blender Tools and Concepts -- Prerequisites -- About Blender -- Blender Version -- Initial Settings -- Load Previous Version Settings -- Context Sensitivity -- Selection -- Select All -- Select Linked -- Parent, Child, Siblings -- Select Under Cursor -- Draw Selection -- Transformation -- Transform Orientation -- Transformation Pivot -- Options -- Transforming without Gizmos -- Reset Transforms -- Hide and Unhide -- Data Objects -- Users -- Auto Cleanup -- Fake User -- Remove Unused -- Default Scene -- 3D Viewport Regions -- Object Origin -- Apply Transforms -- Snapping -- Snap During Transform -- Snap -- 3D Cursor -- Symmetry -- Outliner -- Collections -- Properties -- Modifiers -- Modifier Stack -- Units -- Import, Link, Append -- Import -- Link -- Append -- Animation -- Actions -- Dope Sheet -- Timeline -- Graph Editor -- Drivers -- Nonlinear Animation -- Quick Favorites -- Preferences -- Operators -- Blender Python API -- Add-ons -- 5. 3D World and Transformation -- World Space -- Vectors and Translation -- Euler Rotation -- Rotation Order -- Gimbal Lock -- Quaternion Rotation -- Scale -- Parenting, Local and Pose Space -- Order of Execution -- 6. …”