Tabla de Contenidos: “…4.3.7 K-Means Method -- 4.3.8 Watershed Method -- 4.3.9 Comparison of Different Segmentation Techniques Based on the Advantages and Disadvantages -- 4.3.10 Comparison of Different Segmentation Techniques Based on Accuracy -- 4.3.11 Comparison of Region Based and Threshold Based Segmentation Techniques Based on Different Parameters -- 4.4 Machine Learning -- 4.4.1 Supervised Learning -- 4.4.2 Unsupervised Learning -- 4.4.3 Reinforcement Learning -- 4.4.4 K-Nearest Neighbour (KNN) -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Random Forest -- 4.5 Deep Learning (DL) -- 4.5.1 Convolutional Neural Networks (CNN) -- 4.5.1.1 Convolution Layer -- 4.5.1.2 Pooling Layer -- 4.5.1.3 Architecture of CNN -- 4.5.1.4 Comparison of Different Variations of CNN Techniques -- 4.5.2 Long Short-Term Memory (LSTM) -- 4.5.3 Artificial Neural Network (ANN) -- 4.5.4 Accuracy of Different Models Discussed Above -- 4.5.5 Accuracy of Other Different Techniques Being Used -- 4.6 Performance Metrics -- 4.6.1 Accuracy -- 4.6.2 Precision -- 4.6.3 Recall -- 4.6.4 Specificity -- 4.6.5 F1-Measure -- 4.7 Method Wise Trend of Using Techniques for Detection of Brain Tumor -- 4.8 Conclusion -- References -- Chapter 5 Advancements in Tumor Detection and Classification -- 5.1 Introduction -- 5.2 Imaging Techniques Used in Tumor Detection and Classification -- 5.2.1 X-Ray -- 5.2.2 CT Scan -- 5.2.3 MRI -- 5.2.4 Ultrasound -- 5.3 Molecular Biology Techniques -- 5.3.1 PCR -- 5.3.2 FISH -- 5.3.3 Next-Generation Sequencing -- 5.3.4 Western Blotting -- 5.4 Machine Learning and Artificial Intelligence -- 5.5 Tumor Classification -- 5.5.1 TNM Staging System -- 5.5.2 Histological Grading -- 5.5.3 Molecular Subtyping -- 5.6 Challenges and Future Directions -- References -- Chapter 6 Classification of Brain Tumor Using Machine Learning Techniques: A Comparative Study -- 6.1 Introduction…”

Tabla de Contenidos: “…Flash Memory -- 6.13.1 Uses of FRAM Devices -- 6.14 Conclusion and Upcoming Trends -- References -- Chapter 7 Applications of AI/ML Algorithms in VLSI Design and Technology -- 7.1 Introduction -- 7.2 Artificial Intelligence and Machine Learning -- 7.3 AI/ML Algorithms -- 7.4 Supervised Machine Learning (SML) -- 7.5 Classification Techniques -- 7.6 K-Nearest Neighbors (KNN) -- 7.7 Support Vector Machine (SVM) -- 7.8 Linearly Separable Classification -- 7.9 Decision Tree Classifier (DTC) -- 7.10 Performance Measures in Classification -- 7.11 Unsupervised Machine Learning (UML) -- 7.12 Hierarchical Clustering -- 7.13 Partitional Clustering -- 7.14 K-Means -- 7.15 Fuzzy (soft) Clustering -- 7.16 Cluster Validation Measures -- 7.17 Internal Clustering Validation Measures -- 7.18 External Clustering Validation Criteria -- 7.19 Limitation and Challenges - VLSI -- References -- Chapter 8 Advancement of Neuromorphic Computing Systems with Memristors -- 8.1 Introduction -- 8.1.1 Evolution in Neural Networks -- 8.1.2 Study Plan and Difficulties in Exhibiting Effective Neuromorphic Computing Systems -- 8.1.3 Hardware for Neuromorphic Systems -- 8.1.4 Device-Level Perspective -- 8.1.5 Electrical Circuits to Realize Neurons -- 8.1.6 Broad Applications of Neuromorphic Computing -- 8.2 Summary -- References -- Chapter 9 Neuromorphic Computing and Its Application -- 9.1 Introduction -- 9.2 Evolution of Neuroinspired Computing Chips -- 9.3 Science Behind Brain Physics -- 9.4 Limitations of Semiconductor Devices -- 9.5 Various Combination of Networks -- 9.5.1 ANN-SNN Hybrid -- 9.5.2 Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN) Hybrid -- 9.5.3 Deep Reinforcement Learning (DRL) Hybrid -- 9.5.4 Ensemble Hybrid -- 9.5.5 Different Types of Neural Networks -- 9.6 Artificial Intelligence…”

Tabla de Contenidos: “…2.3.4 Rational, complex numbers, and other numeric types -- 2.4 Flow control -- 2.4.1 For: The central pillar of iteration -- 2.4.2 Continue: Skipping the rest of the current iteration -- 2.4.3 While: Looping until a condition changes its state -- 2.4.4 Loop: The basis for Rust's looping constructs -- 2.4.5 Break: Aborting a loop -- 2.4.6 If, if else, and else: Conditional branching -- 2.4.7 Match: Type-aware pattern matching -- 2.5 Defining functions -- 2.6 Using references -- 2.7 Project: Rendering the Mandelbrot set -- 2.8 Advanced function definitions -- 2.8.1 Explicit lifetime annotations -- 2.8.2 Generic functions -- 2.9 Creating grep-lite -- 2.10 Making lists of things with arrays, slices, and vectors -- 2.10.1 Arrays -- 2.10.2 Slices -- 2.10.3 Vectors -- 2.11 Including third-party code -- 2.11.1 Adding support for regular expressions -- 2.11.2 Generating the third-party crate documentation locally -- 2.11.3 Managing Rust toolchains with rustup -- 2.12 Supporting command-line arguments -- 2.13 Reading from files -- 2.14 Reading from stdin -- Summary -- 3 Compound data types -- 3.1 Using plain functions to experiment with an API -- 3.2 Modeling files with struct -- 3.3 Adding methods to a struct with impl -- 3.3.1 Simplifying object creation by implementing new() -- 3.4 Returning errors -- 3.4.1 Modifying a known global variable -- 3.4.2 Making use of the Result return type -- 3.5 Defining and making use of an enum -- 3.5.1 Using an enum to manage internal state -- 3.6 Defining common behavior with traits -- 3.6.1 Creating a Read trait -- 3.6.2 Implementing std::fmt::Display for your own types -- 3.7 Exposing your types to the world -- 3.7.1 Protecting private data -- 3.8 Creating inline documentation for your projects -- 3.8.1 Using rustdoc to render docs for a single source file…”

Tabla de Contenidos: “…5.2.2.2 Limit and trend checking -- 5.2.2.3 Partial least squares regression -- 5.2.2.4 Bayesian network -- 5.2.2.5 Artificial neural network -- 5.2.2.6 K-means clustering -- 5.2.2.7 Attribute oriented induction -- 5.2.2.8 Hidden Markov model -- 5.3 Remaining Useful Life Identification of Wearing Components -- 5.3.1 Theoretical Background -- 5.3.2 Techniques Catalogue -- 5.3.3 Physical Modelling -- 5.3.3.1 Industrial automation -- 5.3.3.2 Fleet's maintenance -- 5.3.3.3 Eolic systems -- 5.3.3.4 Medical systems -- 5.3.4 Artificial Neural Networks -- 5.3.4.1 Deep neural networks -- 5.3.5 Life Expectancy Models -- 5.3.5.1 Time series analysis with attribute oriented induction -- 5.3.5.2 Application to a pump -- 5.3.5.3 Application to industrial forklifts -- 5.3.5.4 Application to a gearbox -- 5.3.6 Expert Systems -- 5.4 Alerting and Prediction of Failures -- 5.4.1 Theoretical Background -- 5.4.2 Techniques Catalogue -- 5.4.2.1 Nearest neighbour cold-deck imputation -- 5.4.2.2 Support vector machine -- 5.4.2.3 Linear discriminant analysis -- 5.4.2.4 Pattern mining -- 5.4.2.5 Temporal pattern mining -- 5.4.2.6 Principal component analysis -- 5.4.2.7 Hidden Semi-Markov model with Bayes classification -- 5.4.2.8 Autoencoders -- 5.4.2.9 Convolutional neural network with Gramian angular fields -- 5.4.2.10 Recurrent neural network with long-short-term memory -- 5.4.2.11 Change detection algorithm -- 5.4.2.12 Fisher's exact test -- 5.4.2.13 Bonferroni correction -- 5.4.2.14 Hypothesis testing using univariate parametric statistics -- 5.4.2.15 Hypothesis testing using univariate non-parametricstatistics -- 5.4.2.16 Mean, thresholds, normality tests -- 5.5 Examples -- 5.5.1 Usage Patterns/k-means -- 5.5.1.1 Data analysis -- 5.5.1.2 Results -- 5.5.1.2.1 Plotting -- 5.5.1.2.2 Replicability of results -- 5.5.1.2.3 Summary of results…”

Tabla de Contenidos: “…3 RTOS (Real-Time Operating System) -- 3.1 Critical Sections -- 3.2 Task Management -- 4 Assigning Task Priorities -- 5 Determining the Size of a Stack -- 5.1 The Idle Task -- 5.2 Priority Levels -- 5.3 The Ready List -- 6 Preemptive Scheduling -- 7 Scheduling Points -- 8 Round-Robin Scheduling -- 9 Context Switching -- 10 Interrupt Management -- 10.1 Handling CPU Interrupts -- 10.2 NonKernel-Aware Interrupt Service Routine (ISR) -- 10.3 Processors with Multiple Interrupt Priorities -- 10.4 All Interrupts Vector to a Common Location -- 10.5 Every Interrupt Vectors to a Unique Location -- 11 The Clock Tick (or System Tick) -- 11.1 Wait Lists -- 11.2 Time Management -- 12 Resource Management -- 12.1 Resource Management-Disable/Enable Interrupts -- 12.2 Resource Management-Semaphores -- 12.3 Resource Management-Notes on Semaphores -- 12.4 Resource Management-Priority Inversions -- 12.5 Resource Management-Mutual Exclusion Semaphores (Mutex) -- 12.6 Resource Management-Deadlocks (or Deadly Embrace) -- 13 Synchronization -- 13.1 Synchronization-Semaphores -- 13.2 Synchronization-Credit Tracking -- 14 Bilateral Rendez-vous -- 15 Message Passing -- 15.1 Messages -- 15.2 Message Queues -- 16 Flow Control -- 17 Clients and Servers -- 17.1 Memory Management -- 18 Summary -- 7 Open-Source Software -- 1 Linux -- 1.1 History of Linux -- 1.1.1 Reason for the Exponential Acceptance of Linux -- 1.1.2 Linux and Embedded Systems -- 1.2 How Embedded Linux is Different From Linux? …”

Tabla de Contenidos: “…. -- 6.1 Introduction -- 6.2 Related works -- 6.3 Proposed model -- 6.3.1 Tokenization -- 6.3.2 Stop words removal -- 6.3.3 Normalization -- 6.3.4 Feature extraction -- 6.3.4.1 CountVectorizer -- 6.3.4.2 Term frequency and inverse document frequency -- 6.3.4.3 Word2vec -- 6.3.4.4 Improved adaptive synthetic sampling (ADASYN) -- 6.4 Results and discussion -- 6.5 Conclusion -- References -- 7 Emotion detection from text data using machine learning for human behavior analysis -- 7.1 Introduction -- 7.1.1 Human behavior analysis -- 7.1.1.1 Theories of emotion -- 7.1.1.1.1 Social cognitive theory -- 7.1.1.2 Theories of personality -- 7.1.1.2.1 Social identity theory -- 7.1.1.2.2 Self-determination theory -- 7.1.2 Models of emotion -- 7.1.3 Affective computing for emotion detection -- 7.1.4 Natural language processing for emotion detection -- 7.1.4.1 Applications -- 7.1.4.2 Challenges -- 7.2 Available tools and resources -- 7.2.1 Tools for emotion detection -- 7.2.2 Datasets -- 7.2.3 Feature extraction tools -- 7.3 Methods and materials -- 7.3.1 Rule-based approaches -- 7.3.1.1 Lexicon-based approach -- 7.3.1.2 Keyword-based approach -- 7.3.1.3 Regular expressions -- 7.3.2 Statistical learning -- 7.3.2.1 Machine learning-based approaches -- 7.3.2.2 Deep learning methods -- 7.3.2.3 Contextual adaptation -- 7.3.2.4 Personalized bots -- 7.3.3 Explainable AI for emotion detection -- 7.4 Outlook -- 7.4.1 Ethical considerations -- 7.4.2 Limitations of NLP in emotion detection -- 7.5 Conclusion -- References -- 8 Optimization of effectual sentiment analysis in film reviews using machine learning techniques -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Proposed System -- 8.3.1 Hybrid algorithm Design -- 8.3.2 Solution Representation…”

Tabla de Contenidos: “…-- 1.1.2 A Brief History of Gs -- 1.2 mmWave Spectrum, Challenges, and Opportunities -- 1.3 Framework Level Requirements for mmWave Wireless Links -- 1.4 Circuit Aspects -- 1.5 Outline of the Book -- Acknowledgement -- References -- Chapter 2 5G Circuits from Requirements to System Models and Analysis -- 2.1 RF Requirements Governed by 5G System Targets -- 2.2 Radio Spectrum and Standardization -- 2.3 System Scalability -- 2.4 Communication System Model for RF System Analysis -- 2.5 System-Level RF Performance Model -- 2.5.1 Transmitter, Receiver, Antenna Array and Transceiver Architectures for RF and Hybrid Beamforming -- 2.6 Radio Propagation and Link Budget -- 2.6.1 Radio Propagation Model -- 2.6.2 Link Budgeting -- 2.7 Multiuser Multibeam Analysis -- 2.8 Conclusion -- Acknowledgement -- References -- Chapter 3 Millimetre-Wave Beam-Space MIMO System for 5G Applications -- 3.1 Introduction -- 3.2 Beam-Space Massive MIMO System -- 3.2.1 System Model -- 3.2.2 Saleh-Valenzuela Channel Model -- 3.3 Array Response Vector -- 3.3.1 mmWave Beam-Space Massive (mWBSM)-MIMO System -- 3.4 Discrete Lens Antenna Array -- 3.5 Beam Selection Algorithm -- 3.6 Mean Sum Assignment-Based Beam User Association -- 3.6.1 Performance Evaluation -- 3.7 Conclusion -- References -- Part II: Oscillator &amp -- Amplifier -- Chapter 4 Gain-Bandwidth Enhancement Techniques for mmWave Fully-Integrated Amplifiers -- 4.1 RLC Tank -- 4.1.1 RC Low-Pass (LP) Filter -- 4.1.2 RLC Band-Pass (BP) Filter -- 4.2 Coupled Resonators -- 4.2.1 Bode-Fano (B-F) Limit -- 4.2.2 Capacitively Coupled Resonators -- 4.2.3 Inductively Coupled Resonators…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Introduction -- A brief outline of this book -- Guide to the reader -- Contributors -- Acknowledgements -- Chapter 1 Desirability -- 1.1 Introduction -- 1.2 Reasoning about and with sets of desirable gambles -- 1.2.1 Rationality criteria -- 1.2.2 Assessments avoiding partial or sure loss -- 1.2.3 Coherent sets of desirable gambles -- 1.2.4 Natural extension -- 1.2.5 Desirability relative to subspaces with arbitrary vector orderings -- 1.3 Deriving and combining sets of desirable gambles -- 1.3.1 Gamble space transformations -- 1.3.2 Derived coherent sets of desirable gambles -- 1.3.3 Conditional sets of desirable gambles -- 1.3.4 Marginal sets of desirable gambles -- 1.3.5 Combining sets of desirable gambles -- 1.4 Partial preference orders -- 1.4.1 Strict preference -- 1.4.2 Nonstrict preference -- 1.4.3 Nonstrict preferences implied by strict ones -- 1.4.4 Strict preferences implied by nonstrict ones -- 1.5 Maximally committal sets of strictly desirable gambles -- 1.6 Relationships with other, nonequivalent models -- 1.6.1 Linear previsions -- 1.6.2 Credal sets -- 1.6.3 To lower and upper previsions -- 1.6.4 Simplified variants of desirability -- 1.6.5 From lower previsions -- 1.6.6 Conditional lower previsions -- 1.7 Further reading -- Acknowledgements -- Chapter 2 Lower previsions -- 2.1 Introduction -- 2.2 Coherent lower previsions -- 2.2.1 Avoiding sure loss and coherence -- 2.2.2 Linear previsions -- 2.2.3 Sets of desirable gambles -- 2.2.4 Natural extension -- 2.3 Conditional lower previsions -- 2.3.1 Coherence of a finite number of conditional lower previsions -- 2.3.2 Natural extension of conditional lower previsions -- 2.3.3 Coherence of an unconditional and a conditional lower prevision -- 2.3.4 Updating with the regular extension -- 2.4 Further reading -- 2.4.1 The work of Williams…”

Tabla de Contenidos: “…1.17 CPU caches -- 1.18 Multiprogrammed computer systems -- 1.19 Timesharing systems -- 1.20 Mean response with FCFS and processor-sharing scheduling -- 1.21 Analysis of open and closed queueing network models -- 1.22 Bottleneck analysis and balanced job bounds -- 1.23 Performance analyses of I/O subsystems -- 1.24 Vector supercomputers -- 1.25 Parallel computers -- 1.25.1 The ILLIAC IV computer -- 1.25.2 Thinking Machines Connection Machine -- 1.25.3 Kendall Square Research's KSR-1 -- 1.25.4 Goodyear Massively Parallel Processor - MPP -- 1.25.5 MasPar -- 1.25.6 NCUBE -- 1.25.7 Meiko -- 1.25.8 SUPRENUM -- 1.25.9 Parsytec -- 1.25.10 Intel Personal SuperComputer - iPSC -- 1.25.11 IBM's BlueGene supercomputer -- 1.25.12 Tesla Dojo supercomputer for AI training -- 1.26 The future of supercomputing -- 1.27 Microprocessor CPUs, GPUs, FPGAs, and ASICs -- 1.28 RISCV and other microprocessors -- 1.29 The IBM PC and its compatibles -- 1.29.1 Experience with IBM workstations -- 1.30 Storage studies by Alan Jay Smith at Berkeley -- 1.31 Prefetching -- 1.32 Database buffers -- 1.33 Checkpointing in processing large jobs -- 1.34 Computer related rule of thumb -- 1.34.1 Amdahl rules in developing S/360 computers -- 1.34.2 Amdahl's law in the era of multicore -- 1.34.3 Amazon optimal configurations for x86-based EC2 instances -- 1.34.4 Kung's law -- 1.34.5 Brooks' law -- 1.34.6 Patterson et al.'…”

Tabla de Contenidos:

Tabla de Contenidos: “…Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- 1 An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare -- 1.1 Introduction -- 1.1.1 Ontology-Based Information Extraction -- 1.1.2 Ontology-Based Knowledge Representation -- 1.2 Related Work -- 1.3 Motivation -- 1.4 Feature Extraction -- 1.4.1 Vector Space Model -- 1.4.2 Latent Semantic Indexing (LSI) -- 1.4.3 Clustering Techniques -- 1.4.4 Topic Modeling -- p(w|d) -- 1.5 Ontology Development -- 1.5.1 Ontology-Based Semantic Indexing (OnSI) Model -- 1.5.2 Ontology Development -- 1.5.3 OnSI Model Evaluation -- 1.5.4 Metrics Analysis -- 1.6 Dataset Description -- 1.7 Results and Discussions -- 1.7.1 Discussion 1 -- 1.7.2 Discussion 2 -- 1.7.3 Discussion 3 -- 1.8 Applications -- 1.9 Conclusion -- 1.10 Future Work -- References -- 2 Semantic Web for Effective Healthcare Systems: Impact and Challenges -- 2.1 Introduction -- 2.2 Overview of the Website in Healthcare -- 2.2.1 What Is Website? …”

Tabla de Contenidos: “…D Derivation of EFIE From the Vector Analog of Green's Theorem…”

Tabla de Contenidos:

Tabla de Contenidos: “…Introduction -- 1.1 Feature extraction -- 1.2 Shape feature extraction -- 1.3 Statistical feature extraction -- 1.4 Multiscale texture features extraction-wavelet-based method -- 1.5 Cluster features extraction -- 1.6 Classifiers -- 1.6.1 Neural networks -- 1.6.2 k-nearest neighbor classifiers -- 1.6.3 Nearest neighbor classifiers based on Euclidean distance -- 1.6.4 Support vector machines -- 2. Proposed method -- 2.1 Dataset -- 2.2 Feature selection -- 2.3 Transfer learning -- 2.3.1 Using AlexNet for transfer learning -- 3. …”

Tabla de Contenidos: “…Front Cover -- Machine Learning for Future Fiber-Optic Communication Systems -- Copyright -- Contents -- Contributors -- Preface -- Acknowledgments -- 1 Introduction to machine learning techniques: An optical communication's perspective -- 1.1 Introduction -- 1.2 Supervised learning -- 1.2.1 Artificial neural networks (ANNs) -- 1.2.2 Choice of activation functions -- 1.2.3 Choice of loss functions -- 1.2.4 Support vector machines (SVMs) -- 1.2.5 K-nearest neighbors (KNN) -- 1.3 Unsupervised learning -- 1.3.1 K-means clustering -- 1.3.2 Expectation-maximization (EM) algorithm -- 1.3.3 Principal component analysis (PCA) -- 1.3.4 Independent component analysis (ICA) -- 1.4 Reinforcement learning (RL) -- 1.5 Deep learning techniques -- 1.5.1 Deep learning vs. conventional machine learning -- 1.5.2 Deep neural networks (DNNs) -- 1.5.3 Convolutional neural networks (CNNs) -- 1.5.4 Recurrent neural networks (RNNs) -- 1.5.5 Generative adversarial networks (GANs) -- 1.6 Future role of ML in optical communications -- 1.7 Online resources for ML algorithms -- 1.8 Conclusions -- 1.A -- References -- 2 Machine learning for long-haul optical systems -- 2.1 Introduction -- 2.2 Application of machine learning in perturbation-based nonlinearity compensation -- 2.2.1 Wide &amp -- deep neural network -- 2.2.2 Data collection and pre-processing -- 2.2.3 Training results -- 2.2.4 Results and discussion -- 2.3 Application of machine learning in digital backpropagation -- 2.3.1 Physics-based machine-learning models -- 2.3.2 Single-polarization systems -- 2.3.3 Dual-polarization systems -- 2.3.4 Subband processing via filter banks -- 2.3.5 Training and application examples -- 2.4 Outlook of machine learning in long-haul systems -- References -- 3 Machine learning for short reach optical fiber systems -- 3.1 Introduction to optical systems for short reach…”

Tabla de Contenidos: “…. -- 4.2. CAMPO BINARIO Y VECTORES EN ESPACIO BINARIO. -- 4.2.1. ARITMÉTICA BOOLEANA. -- 4.3. …”

Tabla de Contenidos: “…Outlining the Nelson-Aalen Additive Model's Confidence Interval -- Discerning the Survival Hazard -- Discerning the Cumulative Survival Hazard -- Baseline Survival Hazard -- Conclusion -- Reference -- Chapter 8: Medical Records Categorization -- Medical Records -- Context of the Chapter -- Categorization with Linear Discriminant Analysis -- Descriptive Analysis -- Preprocessing the Medical Records Data -- Carrying Out a Regular Expression -- Carrying Out Word Vectorization -- Executing the Linear Discriminant Analysis Model to Classify Patients' Medical Records -- Considering the Linear Discriminant Analysis Model's Performance -- Conclusion -- Chapter 9: A Case for Psychology: Factoring and Clustering Personality Dimensions -- Personality Dimensions -- Questionnaires -- Likert Scale -- Scale Reliability -- Spearman-Brown Reliability Testing Strategy -- Carrying Out the Cronbach's Reliability Testing Strategy -- Carrying Out the Factor Model -- Carrying Out the Bartlett Sphericity Test -- Carrying Out the Kaiser-Meyer-Olkin Test -- Discerning K with a Scree Plot -- Carrying Out Eigenvalue Rotation -- Varimax Rotation -- Discerning Proportional Variance and Cumulative Variances -- Carrying Out Cluster Analysis -- Carrying Out Principal Component Analysis -- Returning K-Means Labels -- Discerning K-Means Cluster Centers -- Conclusion -- Index…”

Tabla de Contenidos: “…Networks for sequence data -- RNNs and LSTMs -- Building a better optimizer -- Gradient descent to ADAM -- Xavier initialization -- Summary -- References -- Chapter 4: Teaching Networks to Generate Digits -- The MNIST database -- Retrieving and loading the MNIST dataset in TensorFlow -- Restricted Boltzmann Machines: generating pixels with statistical mechanics -- Hopfield networks and energy equations for neural networks -- Modeling data with uncertainty with Restricted Boltzmann Machines -- Contrastive divergence: Approximating a gradient -- Stacking Restricted Boltzmann Machines to generate images: the Deep Belief Network -- Creating an RBM using the TensorFlow Keras layers API -- Creating a DBN with the Keras Model API -- Summary -- References -- Chapter 5: Painting Pictures with Neural Networks Using VAEs -- Creating separable encodings of images -- The variational objective -- The reparameterization trick -- Inverse Autoregressive Flow -- Importing CIFAR -- Creating the network from TensorFlow 2 -- Summary -- References -- Chapter 6: Image Generation with GANs -- The taxonomy of generative models -- Generative adversarial networks -- The generator model -- Training GANs -- Non-saturating generator cost -- Maximum likelihood game -- Vanilla GAN -- Improved GANs -- Deep Convolutional GAN -- Vector arithmetic -- Conditional GAN -- Wasserstein GAN -- Progressive GAN -- The overall method -- Progressive growth-smooth fade-in -- Minibatch standard deviation -- Equalized learning rate -- Pixelwise normalization -- TensorFlow Hub implementation -- Challenges -- Training instability -- Mode collapse -- Uninformative loss and evaluation metrics -- Summary -- References -- Chapter 7: Style Transfer with GANs -- Paired style transfer using pix2pix GAN -- The U-Net generator -- The Patch-GAN discriminator -- Loss -- Training pix2pix -- Use cases…”

Tabla de Contenidos: “…Snowball stemming -- Lancaster stemming -- Lovins stemming -- Dawson stemming -- Lemmatization -- N-grams -- Feature extraction -- One hot encoding -- TF-IDF -- CountVectorizer -- Word2Vec -- CBOW -- Skip-Gram model -- Applying NLP techniques -- Text classification -- Introduction to Naive Bayes' algorithm -- Random Forest -- Naive Bayes' text classification code example -- Implementing sentiment analysis -- Frequently asked questions -- Summary -- Chapter 7: Fuzzy Systems -- Fuzzy logic fundamentals -- Fuzzy sets and membership functions -- Attributes and notations of crisp sets -- Operations on crisp sets -- Properties of crisp sets -- Fuzzification -- Defuzzification -- Defuzzification methods -- Fuzzy inference -- ANFIS network -- Adaptive network -- ANFIS architecture and hybrid learning algorithm -- Fuzzy C-means clustering -- NEFCLASS -- Frequently asked questions -- Summary -- Chapter 8: Genetic Programming -- Genetic algorithms structure -- KEEL framework -- Encog machine learning framework -- Encog development environment setup -- Encog API structure -- Introduction to the Weka framework -- Weka Explorer features -- Preprocess -- Classify -- Attribute search with genetic algorithms in Weka -- Frequently asked questions -- Summary -- Chapter 9: Swarm Intelligence -- Swarm intelligence -- Self-organization -- Stigmergy -- Division of labor -- Advantages of collective intelligent systems -- Design principles for developing SI systems -- The particle swarm optimization model -- PSO implementation considerations -- Ant colony optimization model -- MASON Library -- MASON Layered Architecture -- Opt4J library -- Applications in big data analytics -- Handling dynamical data -- Multi-objective optimization -- Frequently asked questions -- Summary -- Chapter 10: Reinforcement Learning -- Reinforcement learning algorithms concept…”

Tabla de Contenidos: “…. -- See also -- Chapter 2: Working with Collections -- Introduction -- Using a vector -- How to do it... -- How it works... -- There's more... -- Using a string -- How to do it... -- How it works... -- There's more... -- Accessing collections as iterators -- How to do it... -- How it works... -- There's more... -- See also -- Using a VecDeque -- How to do it... -- How it works... -- There's more... -- Using a HashMap -- How to do it... -- How it works... -- There's more... -- Using a HashSet -- How to do it... -- How it works... -- There's more…”