Tabla de Contenidos: “…the graphic nature of charactersstrong silhouettes; weight and balance; how to design a 3D character; planning a scene; animating your characters; how to build and rig a simple 3D character; skin and bones; child of the joint; first get your body parts; putting bones in your man; setting up eye controls; setting up the legs; chapter 4 timing, anticipation, overshoot, follow-through and overlapping action with an animated character; timing; anticipation; how much anticipation; force; acting and anticipation; double takes!…”

Tabla de Contenidos: “…How to Draw for Storyboarding: Motion and Emotion; Only 99,999 to Go; From Stick Figures to Balloon People; Walt Stanchfield's Gesture Drawing Class; Caricature; Designing Interesting Characters; The Story Drive of Emotions; Drawing the Four Main Emotion Groups; Miscellaneous Drawing Tips; Drawing for Clarity and the Use of Clear Silhouettes; Mort Walker's The Lexicon of Comicana; Technical Aspects of Storyboards…”

Tabla de Contenidos: “…; 7 Quick techniques; A splash of color; Panorama power; The need for speed; Sunset silhouette; Making rainbows; A model village; Making notepaper; Writing in the sand; Rubber stamp it; Creating custom brushes; Guided Edit: Puzzle Effect; Bobblehead caricature; Letting the dust settle; Casting a divine light; Aging a photo in minutes; Making sticky tape; From day to night; Flashlight illumination; Instant candlelight; Complex reflections; Lifting the lid; Room with a view; The classic head-swap…”

Tabla de Contenidos: “…Windows 8 user interface style -- Influences -- Bauhaus style in the Windows 8 UI -- Enhance the functionality and the content, not the container -- Industrialize the software and user interface, and create projects, not products -- Use clear typography -- Take advantage of the grid system -- Prefer photos over drawings -- Select few and contrasting colors -- Strive for international language and employ essential iconography -- Characteristics of a Windows 8 app -- Silhouette -- Full screen -- Edges -- Comfort and touch -- Semantic Zoom -- Animations -- Different form factors -- Snapped and fill views -- Summary -- Quick reference -- 3. …”

Tabla de Contenidos: “…Cluster Analysis; Introduction; Algorithms for Cluster Analysis; Issues with K-Means Clustering Process; Hierarchical Clustering; Measures of Cluster and Clustering Quality; Silhouette Coefficient…”

Tabla de Contenidos: “…Why we want to have differentiable rendering -- How to make rendering differentiable -- What problems can be solved by using differentiable rendering -- The object pose estimation problem -- How it is coded -- An example of object pose estimation for both silhouette fitting and texture fitting -- Summary -- Chapter 5: Understanding Differentiable Volumetric Rendering -- Technical requirements -- Overview of volumetric rendering -- Understanding ray sampling -- Using volume sampling -- Exploring the ray marcher -- Differentiable volumetric rendering -- Reconstructing 3D models from multi-view images -- Summary -- Chapter 6: Exploring Neural Radiance Fields (NeRF) -- Technical requirements -- Understanding NeRF -- What is a radiance field? …”

Tabla de Contenidos: “…-- 8.2 Formen aus Feuer -- Kapitel 9 -- Arbeiten mit Funkenerzeugern -- 9.1 Wunderkerzen -- 9.2 Kleine Feuerwerksvulkane -- Kapitel 10 -- Bodenlinien -- 10.1 Mit der Taschenlampe anleuchten -- 10.2 Mit einem ferngesteuerten Auto malen -- 10.3 Mit der Malerrolle malen -- Kapitel 11 -- Lightpainting mit Model -- 11.1 Darstellung der Person als Silhouette -- Einsatz von Blitzlicht…”

Tabla de Contenidos: “…Animating Layers On and OffText Messaged Animation; Interlude: Walt's Principles; 6 Light Effects; HDR; Volumetric Light; Silhouettes; Bars of Light; Welding Sparks; Light Whips; Stage Lights; Galaxy Scene; Energy Cube; Faked Light Streaks; Searchlights; Sheen; The Glow Effect; Flaring Hot Spots; Laser Beams; Trapcode Shine; Interlude: High Dynamic Range; 7 Masks and Shapes; Mask Options; Instant Vignette; Sky Replacement; From Video to Shape Layer; The Vegas Effect; Luma Matte; Alpha Matte; Animating Masks; Feathered Vertices; Motion Paths from Illustrator; Trapcode 3D Stroke; 8 Particles…”

Tabla de Contenidos: “…Cover; ROTOSCOPING: Techniques and Tools for the Aspiring Artist; Copyright; CONTENTS; ACKNOWLEDGMENTS; INTRODUCTION; 1 ORIGINS OF ROTO; 1.1 Origins of Roto; 1.2 Modern Roto; 2 DEFINING THE TERMS; 3 ROTOSCOPING SOFTWARE; 3.1 Roto Tools; 3.2 Keyboard Shortcuts; 3.2.1 Creating Spline/Type; 3.2.2 Editing Controls; 3.2.3 Timeline Controls; 3.2.4 Transformation (Object or Sub-Object); 3.2.5 Viewer Controls; 3.3 Silhouette; 3.3.1 User Interface; 3.4 After Effects; 3.5 Mocha; 4 PRE-SHOP WARM-UP; 4.1 Establish Specifics; 4.1.1 Shot Length; 4.1.2 Define the Focus Object; 4.1.3 Matte Usage…”

Tabla de Contenidos: “…Predicting stock prices with an ARIMA model -- Chapter 11: Supervised Machine Learning -- Introduction -- Fitting a linear regression model with lm -- Summarizing linear model fits -- Using linear regression to predict unknown values -- Measuring the performance of the regression model -- Performing a multiple regression analysis -- Selecting the best-fitted regression model with stepwise regression -- Applying the Gaussian model for generalized linear regression -- Performing a logistic regression analysis -- Building a classification model with recursive partitioning trees -- Visualizing a recursive partitioning tree -- Measuring model performance with a confusion matrix -- Measuring prediction performance using ROCR -- Chapter 12: Unsupervised Machine Learning -- Introduction -- Clustering data with hierarchical clustering -- Cutting tree into clusters -- Clustering data with the k-means method -- Clustering data with the density-based method -- Extracting silhouette information from clustering -- Comparing clustering methods -- Recognizing digits using the density-based clustering method -- Grouping similar text documents with k-means clustering methods -- Performing dimension reduction with Principal Component Analysis (PCA) -- Determining the number of principal components using a scree plot -- Determining the number of principal components using the Kaiser method -- Visualizing multivariate data using a biplot -- Index…”

Tabla de Contenidos: “…-- Performing naïve Bayes classification -- Classification metrics -- Understanding decision trees -- Measuring purity -- Exploring the Titanic dataset -- Dummy variables -- Diving deep into UL -- When to use UL -- k-means clustering -- The Silhouette Coefficient -- Feature extraction and PCA -- Summary -- Chapter 12: Introduction to Transfer Learning and Pre-Trained Models -- Understanding pre-trained models -- Benefits of using pre-trained models -- Commonly used pre-trained models -- Decoding BERT's pre-training -- TL -- Different types of TL -- Inductive TL -- Transductive TL -- Unsupervised TL - feature extraction -- TL with BERT and GPT -- Examples of TL -- Example - Fine-tuning a pre-trained model for text classification -- Summary -- Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift -- Understanding algorithmic bias -- Types of bias -- Sources of algorithmic bias -- Measuring bias -- Consequences of unaddressed bias and the importance of fairness -- Mitigating algorithmic bias -- Mitigation during data preprocessing -- Mitigation during model in-processing -- Mitigation during model postprocessing -- Bias in LLMs -- Uncovering bias in GPT-2 -- Emerging techniques in bias and fairness in ML -- Understanding model drift and decay -- Model drift -- Data drift -- Mitigating drift -- Understanding the context -- Continuous monitoring -- Regular model retraining -- Implementing feedback systems -- Model adaptation techniques -- Summary -- Chapter 14: AI Governance -- Mastering data governance -- Current hurdles in data governance -- Data management: crafting the bedrock -- Data ingestion - the gateway to information -- Data integration - from collection to delivery -- Data warehouses and entity resolution…”

Tabla de Contenidos: “…Voting classifier -- Summary -- Chapter 9: Clustering Fundamentals -- Clustering basics -- k-NN -- Gaussian mixture -- Finding the optimal number of components -- K-means -- Finding the optimal number of clusters -- Optimizing the inertia -- Silhouette score -- Calinski-Harabasz index -- Cluster instability -- Evaluation methods based on the ground truth -- Homogeneity -- Completeness -- Adjusted Rand Index -- Summary -- Chapter 10: Advanced Clustering -- DBSCAN -- Spectral Clustering -- Online Clustering -- Mini-batch K-means -- BIRCH -- Biclustering -- Summary -- Chapter 11: Hierarchical Clustering -- Hierarchical strategies -- Agglomerative Clustering -- Dendrograms -- Agglomerative Clustering in scikit-learn -- Connectivity constraints -- Summary -- Chapter 12: Introducing Recommendation Systems -- Naive user-based systems -- Implementing a user-based system with scikit-learn -- Content-based systems -- Model-free (or memory-based) collaborative filtering -- Model-based collaborative filtering -- Singular value decomposition strategy -- Alternating least squares strategy -- ALS with Apache Spark MLlib -- Summary -- Chapter 13: Introducing Natural Language Processing -- NLTK and built-in corpora -- Corpora examples -- The Bag-of-Words strategy -- Tokenizing -- Sentence tokenizing -- Word tokenizing -- Stopword removal -- Language detection -- Stemming -- Vectorizing -- Count vectorizing -- N-grams -- TF-IDF vectorizing -- Part-of-Speech -- Named Entity Recognition -- A sample text classifier based on the Reuters corpus -- Summary -- Chapter 14: Topic Modeling and Sentiment Analysis in NLP -- Topic modeling -- Latent Semantic Analysis -- Probabilistic Latent Semantic Analysis -- Latent Dirichlet Allocation -- Introducing Word2vec with Gensim -- Sentiment analysis -- VADER sentiment analysis with NLTK -- Summary…”

Tabla de Contenidos: “…Visualizing the goodness of fit -- Computing MSE and median absolute error -- Evaluating clusters with the mean silhouette coefficient -- Comparing results with a dummy classifier -- Determining MAPE and MPE -- Comparing with a dummy regressor -- Calculating the mean absolute error and the residual sum of squares -- Examining the kappa of classification -- Taking a look at the Matthews correlation coefficient -- Chapter 11: Analyzing Images -- Introduction -- Setting up OpenCV -- Applying Scale-Invariant Feature Transform (SIFT) -- Detecting features with SURF -- Quantizing colors -- Denoising images -- Extracting patches from an image -- Detecting faces with Haar cascades -- Searching for bright stars -- Extracting metadata from images -- Extracting texture features from images -- Applying hierarchical clustering on images -- Segmenting images with spectral clustering -- Chapter 12: Parallelism and Performance -- Introduction -- Just-in-time compiling with Numba -- Speeding up numerical expressions with Numexpr -- Running multiple threads with the threading module -- Launching multiple tasks with the concurrent.futures module -- Accessing resources asynchronously with the asyncio module -- Distributed processing with execnet -- Profiling memory usage -- Calculating the mean, variance, skewness, and kurtosis on the fly -- Caching with a least recently used cache -- Caching HTTP requests -- Streaming counting with the Count-min sketch -- Harnessing the power of the GPU with OpenCL -- Appendix A: Glossary -- Appendix B: Function Reference -- IPython -- Matplotlib -- NumPy -- pandas -- Scikit-learn -- SciPy -- Seaborn -- Statsmodels -- Appendix C: Online Resources -- IPython notebooks and open data -- Mathematics and statistics -- Appendix D: Tips and Tricks for Command-Line and Miscellaneous Tools -- IPython notebooks -- Command-line tools…”

Tabla de Contenidos: “…-- Clustering data with K-Means algorithm -- Estimating the number of clusters with Mean Shift algorithm -- Estimating the quality of clustering with silhouette scores -- What are Gaussian Mixture Models? …”

Tabla de Contenidos: “…Bayes' theorem -- Naive Bayes classifiers -- Naive Bayes in scikit-learn -- Bernoulli naive Bayes -- Multinomial naive Bayes -- Gaussian naive Bayes -- References -- Summary -- Chapter 7: Support Vector Machines -- Linear support vector machines -- scikit-learn implementation -- Linear classification -- Kernel-based classification -- Radial Basis Function -- Polynomial kernel -- Sigmoid kernel -- Custom kernels -- Non-linear examples -- Controlled support vector machines -- Support vector regression -- References -- Summary -- Chapter 8: Decision Trees and Ensemble Learning -- Binary decision trees -- Binary decisions -- Impurity measures -- Gini impurity index -- Cross-entropy impurity index -- Misclassification impurity index -- Feature importance -- Decision tree classification with scikit-learn -- Ensemble learning -- Random forests -- Feature importance in random forests -- AdaBoost -- Gradient tree boosting -- Voting classifier -- References -- Summary -- Chapter 9: Clustering Fundamentals -- Clustering basics -- K-means -- Finding the optimal number of clusters -- Optimizing the inertia -- Silhouette score -- Calinski-Harabasz index -- Cluster instability -- DBSCAN -- Spectral clustering -- Evaluation methods based on the ground truth -- Homogeneity -- Completeness -- Adjusted rand index -- References -- Summary -- Chapter 10: Hierarchical Clustering -- Hierarchical strategies -- Agglomerative clustering -- Dendrograms -- Agglomerative clustering in scikit-learn -- Connectivity constraints -- References -- Summary -- Chapter 11: Introduction to Recommendation Systems -- Naive user-based systems -- User-based system implementation with scikit-learn -- Content-based systems -- Model-free (or memory-based) collaborative filtering -- Model-based collaborative filtering -- Singular Value Decomposition strategy -- Alternating least squares strategy…”

Tabla de Contenidos: “…7.3 Linear Feature Extraction -- 7.3.1 Feature Extraction Based on the Bhattacharyya Distance with Gaussian Distributions -- 7.3.2 Feature Extraction Based on InterIntra Class Distance -- 7.4 References -- 8 Unsupervised Learning -- 8.1 Feature Reduction -- 8.1.1 Principal Component Analysis -- 8.1.2 Multidimensional Scaling -- 8.1.3 Kernel Principal Component Analysis -- 8.2 Clustering -- 8.2.1 Hierarchical Clustering -- 8.2.2 K-Means Clustering -- 8.2.3 Mixture of Gaussians -- 8.2.4 Mixture of probabilistic PCA -- 8.2.5 Self-Organizing Maps -- 8.2.6 Generative Topographic Mapping -- 8.3 References -- 9 Worked Out Examples -- 9.1 Example on Image Classification with PRTools -- 9.1.1 Example on Image Classification -- 9.1.2 Example on Face Classification -- 9.1.3 Example on Silhouette Classification -- 9.2 Boston Housing Classification Problem -- 9.2.1 Dataset Description -- 9.2.2 Simple Classification Methods -- 9.2.3 Feature Extraction -- 9.2.4 Feature Selection -- 9.2.5 Complex Classifiers -- 9.2.6 Conclusions -- 9.3 Time-of-Flight Estimation of an Acoustic Tone Burst -- 9.3.1 Models of the Observed Waveform -- 9.3.2 Heuristic Methods for Determining the ToF -- 9.3.3 Curve Fitting -- 9.3.4 Matched Filtering -- 9.3.5 ML Estimation Using Covariance Models for the Reflections -- 9.3.6 Optimization and Evaluation -- 9.4 Online Level Estimation in a Hydraulic System -- 9.4.1 Linearized Kalman Filte -- 9.4.2 Extended Kalman Filtering -- 9.4.3 Particle Filtering -- 9.4.4 Discussion -- 9.5 References -- Appendix A Topics Selected from Functional Analysis -- A.1 Linear Spaces -- A.1.1 Normed Linear Spaces -- A.1.2 Euclidean Spaces or Inner Product Spaces -- A.2 Metric Spaces -- A.3 Orthonormal Systems and Fourier Series -- A.4 Linear Operators -- A.5 Selected Bibliography -- Appendix B Topics Selected from Linear Algebra and Matrix Theory…”

Tabla de Contenidos: “…Using Autodesk Maya with the Maya Software Renderer -- Switching to the V-Ray Renderer and Adding Fog -- Case Study 3: Lighting an Animated Animal Character -- Using Autodesk Maya with the Maya Software Renderer -- Switching to the Arnold Renderer and Adding a Sky Shader -- Sidebar: Creating Skies in 3D -- Epilogue: The Future of 3D Lighting -- Appendix: Visual Lighting Glossary -- 0-Point Lighting -- 1-Point Lighting -- 2-Point Lighting -- 3-Point Lighting -- Ambient Light -- Area Light -- Back Light -- Background Light -- Bounced Light -- Color Bleed -- Color Temperature -- Butterfly Lighting -- Depth Map Shadow -- Diffuse -- Directional Light -- Environment Light -- Eye Light -- Fill Light -- Final Gather (GI) -- Fresnel Reflection -- Glamour Lighting -- Hair Light -- Hard Lighting -- High-key -- IBL (Image-based Lighting) -- Key Light -- Kicker (Light) -- Lighting Ratio -- Light Ray -- Loop Lighting -- Low-key -- Mesh Light -- Naturalistic Lighting -- Path Tracing (GI) -- PBR (Physically-Based Rendering) -- Photometric Light -- Photon -- Photon Mapping (GI) -- Point Light -- Radiosity (GI) -- Ray Tracing -- Ray Trace Shadow -- Refraction -- Rembrandt Lighting -- Renderer -- Rim Light -- Shader (Material) -- Secondary Diffuse Illumination -- Silhouette Lighting -- Sky System -- Soft Lighting -- Specularity -- Split Lighting -- Spot Light -- Stylistic Lighting -- Utility Light -- Volume Light -- Appendix: Common Question Index -- Index…”

Tabla de Contenidos: “…Quantifying the quality of clustering via silhouette plots…”

Tabla de Contenidos: “…-- Optimizing K-Means Performance -- Deciding the Number of Clusters by Inertia and Silhouette Score Analysis -- Checking Cluster Health -- Multitask Learning Model -- What Is Multitask Learning ? …”

Tabla de Contenidos: “…-- Choosing an optimal number for K and cluster validation -- The Silhouette Coefficient…”