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: “…5.4.1 Different deployment strategies for UAVs in healthcare -- 5.4.2 Logistical considerations -- 5.5 Security challenges and solutions -- 5.5.1 Security challenges associated with UAVs in healthcare -- 5.5.2 Potential solutions and mitigation strategies -- 5.5.3 Importance of regulatory compliance and adherence to safety standards -- 5.6 Regulatory and legal framework -- 5.6.1 Need for standardized regulations and guidelines to ensure safe and ethical use of UAVs -- 5.7 Conclusion and future scope -- References -- Chapter 6: Blockchain technologies using machine learning -- 6.1 Introduction -- 6.2 Understanding blockchain technologies -- 6.2.1 Introduction to blockchain -- 6.2.2 Key components of a blockchain network -- 6.2.3 Consensus mechanisms and their impact -- 6.2.4 Benefits and limitations of BCT -- 6.2.4.1 Benefits of BCT -- 6.2.4.2 Limitations of BCT -- 6.3 ML fundamentals -- 6.3.1 Overview of ML -- 6.3.2 Types of ML algorithms -- 6.3.2.1 Supervised learning algorithms -- 6.3.2.2 Unsupervised learning algorithms -- 6.3.2.3 Semi-supervised learning algorithms -- 6.3.2.4 Reinforcement learning algorithms -- 6.3.2.5 Deep learning algorithms -- 6.3.3 Data pre-processing and feature engineering -- 6.3.3.1 Data pre-processing -- 6.3.3.2 Feature engineering -- 6.4 Evaluating ML models -- 6.4.1 Common evaluation metrics -- 6.5 Synergies between blockchain and ML -- 6.5.1 Combining ML models on the blockchain -- 6.6 Applications of blockchain and ML integration -- 6.7 Challenges and limitations in BCT and ML integration -- 6.7.1 Scalability issues -- 6.7.2 Data availability and quality -- 6.7.3 Regulatory and legal challenges -- 6.7.4 Trusted oracles and data feeds -- 6.7.5 Energy efficiency concerns -- 6.8 Future prospects and research directions -- 6.8.1 Federated learning on blockchain networks…”

Tabla de Contenidos: “…3.3 Methodology of Designing Quantum Multiplexer (QMUX) -- 3.3.1 QMUX Using CSWAP Gates -- 3.3.1.1 Generalization -- 3.3.2 QMUX Using Controlled-R Gates -- 3.4 Analysis and Synthesis of Proposed Methodology -- 3.5 Complexity and Cost of Quantum Circuits -- 3.6 Conclusion -- References -- Chapter 4 Artificial Intelligence and Machine Learning Algorithms in Quantum Computing Domain -- 4.1 Introduction -- 4.1.1 Quantum Computing Convolutional Neural Network -- 4.2 Literature Survey -- 4.3 Quantum Algorithms Characteristics Used in Machine Learning Problems -- 4.3.1 Minimizing Quantum Algorithm -- 4.3.2 K-NN Algorithm -- 4.3.3 K-Means Algorithm -- 4.4 Tree Tensor Networking -- 4.5 TNN Implementation on IBM Quantum Processor -- 4.6 Neurotomography -- 4.7 Conclusion and Future Scope -- References -- Chapter 5 Building a Virtual Reality-Based Framework for the Education of Autistic Kids -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Proposed Work -- 5.3.1 Methodology -- 5.3.2 Work Flow of Neural Style Transfer -- 5.3.3 A-Frame -- 5.3.3.1 Setting Up the Virtual World and Adding Components -- 5.3.3.2 Adding Interactivity Through Raycasting -- 5.3.3.3 Animating the Components -- 5.3.4 Neural Style Transfer -- 5.3.4.1 Choosing the Content and Styling Image -- 5.3.4.2 Image Preprocessing and Generation of a Random Image -- 5.3.4.3 Model Design and Extraction of Content and Style -- 5.3.4.4 Loss Calculation -- 5.3.4.5 Model Optimization -- 5.4 Evaluation Metrics -- 5.5 Results -- 5.5.1 A-Frame -- 5.5.2 Neural Style Transfer -- 5.6 Conclusion -- References -- Chapter 6 Detection of Phishing URLs Using Machine Learning and Deep Learning Models Implementing a URL Feature Extractor -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Proposed Model -- 6.3.1 URL Feature Extractor -- 6.3.2 Dataset -- 6.3.3 Methodologies -- 6.3.3.1 AdaBoost Classifier…”

Tabla de Contenidos: “…Validating that the model works -- Step 1 - Starting your model -- Step 2 - Analyzing an image with your model -- Step 3 - Stopping your model -- Summary -- Part 2: Applying CV to Real-World Use Cases -- Chapter 4: Using Identity Verification to Build a Contactless Hotel Check-In System -- Technical requirements -- Prerequisites -- Creating the image bucket -- Uploading the sample images -- Creating the profile table -- Introducing collections -- Creating a collection -- Describing a collection -- Deleting a collection -- Quick recap -- Describing the user journeys -- Registering a new user -- Authenticating a user -- Registering a new user with an ID card -- Updating the user profile -- Implementing the solution -- Checking image quality -- Indexing face information -- Search existing faces -- Quick recap -- Supporting ID cards -- Reading an ID card -- Using the CompareFaces API -- Quick recap -- Guidance for identity verification on AWS -- Solution overview -- Deployment process -- Cleanup -- Summary -- Chapter 5: Automating a Video Analysis Pipeline -- Technical requirements -- Creating the video bucket -- Uploading content to Amazon S3 -- Creating the person-tracking topic -- Subscribing a message queue to the person-tracking topic -- Creating the person-tracking publishing role -- Setting up IP cameras -- Quick recap -- Using IP cameras -- Installing OpenCV -- Installing additional modules -- Connecting with OpenCV -- Viewing the frame -- Uploading the frame -- Reporting frame metrics -- Quick recap -- Using the PersonTracking API -- Uploading the video to Amazon S3 -- Using the StartPersonTracking API -- Receiving the completion notification -- Using the GetPersonTracking API -- Reviewing the GetPersonTracking response -- Viewing the frame -- Quick recap -- Summary -- Chapter 6: Moderating Content with AWS AI Services -- Technical requirements…”

Tabla de Contenidos: “…-- Inside the head of the attention sublayer of a transformer -- Exploring emergence with ChatGPT -- Investigating the potential of downstream tasks -- Evaluating models with metrics -- Accuracy score -- F1-score…”

Tabla de Contenidos: “…-- Getting started with BigQuery -- Using BQML for feature transformations -- Manual preprocessing -- Building ML models with BQML -- Creating BQML models -- Hyperparameter tuning with BQML -- Evaluating trained models -- Doing inference with BQML -- User exercise -- Summary -- Chapter 7: Training Fully Custom ML Models with Vertex AI -- Technical requirements -- Building a basic deep learning model with TensorFlow -- Experiment - converting black-and-white images into color images -- Packaging a model to submit it to Vertex AI as a training job -- Monitoring model training progress -- Evaluating trained models -- Summary -- Chapter 8: ML Model Explainability -- What is Explainable AI and why is it important for MLOps practitioners? …”

Tabla de Contenidos: “…-- 9.2 Determination of Groundwater Potential (GWP) Parameters -- 9.2.1 Groundwater Potential (GWP) Parameters -- 9.2.2 Analysis of the Key GWP Parameters -- 9.3 GWP Determination: Methods and Techniques -- 9.4 GWP Output: Applications -- 9.5 GWP Research Gaps: Future Research Areas -- 9.6 Conclusion -- References -- Chapter 10 Fruit Leaf Classification Using Transfer Learning for Automation and Industrial Applications -- 10.1 Introduction -- 10.1.1 Overview of Fruit Leaf Classification and Its Relevance in Automation and Industrial Applications -- 10.1.2 Challenges of Building a Classification Model from Scratch -- 10.1.3 Introduction to Transfer Learning as a Solution -- 10.1.4 Overview of Popular Pre-Trained Models -- 10.1.4.1 Visual Geometry Group -- 10.1.4.2 Residual Network -- 10.1.4.3 Inception -- 10.2 Data Collection and Preprocessing -- 10.2.1 Importance of Data Collection and Preprocessing -- 10.2.2 Data Augmentation in Fruit Leaf Classification -- 10.2.3 Normalization and Resizing in Fruit Leaf Classification -- 10.3 Loading a Pre-Trained Model for Fruit Leaf Classification Using Transfer Learning -- 10.3.1 Code Examples for Implementing Transfer Learning Using TensorFlow -- 10.4 Training and Evaluation -- 10.4.1 Explanation of Training and Evaluation Process -- 10.4.2 Metrics for Measuring Model Performance -- 10.5 Applications in Automation and Industry -- 10.5.1 Benefits of Using Transfer Learning in Automation and Industrial Settings…”

Tabla de Contenidos: “….) -- 6.3.1 Energía cinética -- 6.3.2 El momento de inercia Ii -- 6.3.3 El tensor de inercia I -- 6.3.4 Teorema de los ejes paralelos -- 6.3.5 Cálculo del momento de inercia para un eslabón rectangular -- 6.3.6 Cálculo de I para eslabones cilíndricos -- 6.3.7 Conversión del momento de inercia -- 6.3.8 Expresión general de la energía cinética -- 6.3.9 Cálculo de la matriz de inercia en Matlab© -- 6.3.10 Energía potencial -- 6.3.11 Cálculo de fuerza o torque derivados de la energía potencial en Matlab© -- 6.4 CONSTRUCCIÓN DE LA ECUACIÓN (....) -- 6.4.1 Ecuación general de movimiento de Euler-Lagrange -- 6.5 ECUACIONES EULER-LAGRANGE (...) -- 6.5.1 Un ejemplo ilustrativo (...) -- 6.5.2 Ecuación de movimiento del robot planar de dos grados de libertad -- 6.6 ANÁLISIS DE LA ECUACIÓN DE MOVIMIENTO EULER-LAGRANGE -- 6.6.1 Efectos derivados de la inercia -- 6.6.2 Efectos de la aceleración -- 6.6.3 Expresiones para el efecto de fuerzas centrífugas y de Coriolis -- 6.6.4 Efecto de las fuerzas centrífugas (...) -- 6.6.5 Efectos de las fuerzas de Coriolis (...) -- 6.7 SIMULACIÓN DEL ROBOT PLANAR (...) -- 6.7.1 Simulación del sistema planar (...) -- 6.8 COMENTARIOS FINALES (...) -- 6.9 DETERMINACIÓN DE LA ECUACIÓN DE MOVIMIENTO (...): -- 6.9.1 Determinación de la energía cinética -- 6.9.2 Determinación de la energía potencial -- 6.9.3 La ecuación de movimiento: método de Uicker-Paul -- 6.9.4 Construcción de la ecuación de movimiento (...) -- 6.9.5 Expresión matricial de las ecuaciones de movimiento -- 6.9.6 Ejemplo de la determinación del modelo dinámico (...) -- 6.10 LECTURAS RECOMENDADAS -- 6.11 EJERCICIOS RECOMENDADOS…”

Tabla de Contenidos: “…-- 3.2 A Survey of Programming Languages for Data Science -- 3.3 Python Crash Course -- 3.4 Strings -- 3.5 Defining Functions -- 3.6 Python's Technical Libraries -- 3.7 Other Python Resources -- 3.8 Further Reading -- 3.9 Glossary -- Interlude: My Personal Toolkit -- Chapter 4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning -- 4.1 The Worst Dataset in the World -- 4.2 How to Identify Pathologies -- 4.3 Problems with Data Content -- 4.4 Formatting Issues -- 4.5 Example Formatting Script -- 4.6 Regular Expressions -- 4.7 Life in the Trenches -- 4.8 Glossary -- Chapter 5 Visualizations and Simple Metrics -- 5.1 A Note on Python's Visualization Tools -- 5.2 Example Code -- 5.3 Pie Charts -- 5.4 Bar Charts -- 5.5 Histograms -- 5.6 Means, Standard Deviations, Medians, and Quantiles -- 5.7 Boxplots -- 5.8 Scatterplots -- 5.9 Scatterplots with Logarithmic Axes -- 5.10 Scatter Matrices -- 5.11 Heatmaps -- 5.12 Correlations -- 5.13 Anscombe's Quartet and the Limits of Numbers -- 5.14 Time Series -- 5.15 Further Reading -- 5.16 Glossary…”

“…What you will learn Pre-process data to make it ready to use for machine learning Create data visualizations with Matplotlib Use scikit-learn to perform dimension reduction using principal component analysis (PCA) Solve classification and regression problems Get predictions using the XGBoost library Process images and create machine learning models to decode them Process human language for prediction and classification Use TensorBoard to monitor training metrics in real time Find the best hyperparameters for your model with AutoML Who this book is for Data Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to m..…”