Tabla de Contenidos: “…Evaluating machine learning models -- Training, validation, and test split -- Simple holdout validation -- K-fold validation -- K-fold validation with shuffling -- Data representativeness -- Time sensitivity -- Data redundancy -- Data preprocessing and feature engineering -- Vectorization -- Value normalization -- Handling missing values -- Feature engineering -- Overfitting and underfitting -- Getting more data -- Reducing the size of the network -- Applying weight regularization -- Dropout -- Underfitting -- Workflow of a machine learning project -- Problem definition and dataset creation -- Measure of success -- Evaluation protocol -- Prepare your data -- Baseline model -- Large model enough to overfit -- Applying regularization -- Learning rate picking strategies -- Summary -- Chapter 5: Deep Learning for Computer Vision -- Introduction to neural networks -- MNIST - getting data -- Building a CNN model from scratch -- Conv2d -- Pooling -- Nonlinear activation - ReLU -- View -- Linear layer -- Training the model -- Classifying dogs and cats - CNN from scratch -- Classifying dogs and cats using transfer learning -- Creating and exploring a VGG16 model -- Freezing the layers -- Fine-tuning VGG16 -- Training the VGG16 model -- Calculating pre-convoluted features -- Understanding what a CNN model learns -- Visualizing outputs from intermediate layers -- Visualizing weights of the CNN layer -- Summary -- Chapter 6: Deep Learning with Sequence Data and Text -- Working with text data -- Tokenization -- Converting text into characters -- Converting text into words -- N-gram representation -- Vectorization -- One-hot encoding -- Word embedding -- Training word embedding by building a sentiment classifier -- Downloading IMDB data and performing text tokenization -- torchtext.data -- torchtext.datasets -- Building vocabulary -- Generate batches of vectors…”

“…It’s a hot and growing field, and up-to-speed ML developers are in demand. …”

Tabla de Contenidos: “…; The Work Experience; ON THE P2P PATH: Hot Spots Movement; The Work Environment; Summary; Chapter 10: Implications for Leadership; Organization Formation; ON THE P2P PATH: ROWE; Human Resources and Organization Development; Questioning Traditional Leadership; Leadership as a Dyad Exchange Structure; Summary; Moving Forward; ON THE P2P PATH: Paul Polman at Unilever; Notes; Acknowledgments; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; R; S; T; U; W; About the Author…”

Tabla de Contenidos: “…Compression and CodecsDiscreet cleaner; Final Cut Pro; Film Versus Video; MPEG-4; QTVR; Miscellaneous QuickTime Tutorials and Information; Appendix D Web Exhibition Venues; Other Venues; Appendix E Bibliography; Index…”

Tabla de Contenidos: “…2002): OUT OF THE DESERT""; ""Filling Shadows: PANACHE IN A FLASH""; ""Capturing a Moment: D ROCK, HAIRSTYLIST""; ""Toolbox Essentials: SAVING ME ON A RAINY DAY""; ""Fashion Stylist Team: ASSEMBLING YOUR A-TEAM""…”

Tabla de Contenidos: “…Table of Contents; Foreword; Preface; Audience; Organization of This Book; Part I, Replication; Part II, Monitoring and Disaster Recovery; Part III, High Availability Environments; Conventions Used in This Book; Using Code Examples; We'd Like to Hear from You; Safari® Books Online; Acknowledgments; Part I. …”

Tabla de Contenidos: “…9.5 Channel Emulation9.6 Testing 802.11n MIMO Devices; Appendix A: A Standards Guide; A.1 FCC Part 15; A.2 IEEE 802.11; A.3 Wi-Fi® Alliance; A.4 CTIA; A.5 IETF BMWG; Appendix B: Selected Bibliography; Index; A; B; C; D; E; F; G; H; I; J; L; M; N; O; P; Q; R; S; T; U; V; W…”

Tabla de Contenidos: “…6.1.5 VxVM/VxFS command differences -- 6.1.6 VxVM/VxFS device drivers and kernel extensions -- 6.1.7 Installation and packaging -- 6.1.8 The 64-bit kernel -- 6.1.9 Debugging information -- 6.1.10 Dynamic MultiPathing (VxDMP) -- 6.2 AIX LVM, JFS/JFS2 and VxVM, VxFS compared -- 6.2.1 Logical volume concepts -- 6.2.2 Volume layouts -- 6.2.3 Backup -- 6.2.4 Hot spare management -- 6.2.5 JFS/JFS2 and VxFS comparison -- Chapter 7. …”

Tabla de Contenidos: “…"'My Books End Up in the Trash'" / A.D. Coleman. "'I'm Not Really a Photographer'" / A.D. …”

Tabla de Contenidos: “…; Who This Book Is For; Conventions Used in This Book; Using Code Examples; We'd Like to Hear from You; Safari® Books Online; Acknowledgments; Jason Brittain's Acknowledgments; Ian Darwin's Acknowledgments; Getting Started with Tomcat; Installing Tomcat; Installing Tomcat on Linux; Installing Tomcat from an Apache multiplatform binary release; Installing Tomcat from this book's Linux RPM packages; Installing Tomcat from the JPackage.org Linux RPM packages; Installing Tomcat on Solaris…”

Tabla de Contenidos: “…C.3 Linear algebra -- C.3.1 Multiplication -- C.3.2 Matrix inverse -- C.3.3 Normal equation -- Appendix D. Introduction to Pandas -- D.1 Pandas -- D.1.1 DataFrame -- D.1.2 Series -- D.1.3 Index -- D.1.4 Accessing rows -- D.1.5 Splitting a DataFrame -- D.2 Operations -- D.2.1 Element-wise operations -- D.2.2 Filtering -- D.2.3 String operations -- D.2.4 Summarizing operations -- D.2.5 Missing values -- D.2.6 Sorting -- D.2.7 Grouping -- Appendix E. …”

Tabla de Contenidos: “…Memory Channel Interleave and Imbalanced Memory ConfigurationsPower and Performance States; CKE Power Savings; Self-Refresh; Voltage/Frequency; DDR Thermal Management; Monitoring Temperature; Memory Throttling; CPU DDRIO; Workload Behavior; Memory Reliability Features; CPU I/Os; CPU Interconnect; Link Power States; PCIe; Link Power States; Link Frequency/Voltage; Link Width; Hot Add; D-states; Summary; Chapter 4: Platform Power Management; Platform Overview; Common Platform Components; Integration; CPU Integration; Chipset Integration; Microservers and Server SoCs; Platform Manageability…”

Tabla de Contenidos: “…-- Value - the LLM advantage -- Impact - changing the landscape -- NLP and LLMs in the business world -- Business sectors -- Customer interactions and service - the early adopter -- Change management driven by AI's impact -- Behavioral trends induced by AI and LLMs - the social aspect -- Personal assistants becoming indispensable -- Ease in communication and bridging language barriers -- Ethical implications of delegated decisions -- Ethics and risks - growing concerns around the implementation of AI -- Summary -- Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts -- Overview of our experts -- Nitzan Mekel-Bobrov, PhD -- David Sontag, PhD -- John D. Halamka, M.D., M.S. -- Xavier Amatriain, PhD -- Melanie Garson, PhD -- Our questions and the experts' answers -- Nitzan Mekel-Bobrov -- Q1.1 - Future of LLM - hybrid learning paradigms: In light of the evolving landscape of learning schemes, what do you envision as the next breakthrough in combining different learning paradigms within LLMs? …”

Tabla de Contenidos: “…Industrial Papers (Invited) -- Data Mining as an Automated Service -- Trends and Challenges in the Industrial Applications of KDD -- Stream Mining I -- Finding Event-Oriented Patterns in Long Temporal Sequences -- Mining Frequent Episodes for Relating Financial Events and Stock Trends -- Graph Mining -- An Efficient Algorithm of Frequent Connected Subgraph Extraction -- Classifier Construction by Graph-Based Induction for Graph-Structured Data -- Clustering I -- Comparison of the Performance of Center-Based Clustering Algorithms -- Automatic Extraction of Clusters from Hierarchical Clustering Representations -- Text Mining -- Large Scale Unstructured Document Classification Using Unlabeled Data and Syntactic Information -- Extracting Shared Topics of Multiple Documents -- An Empirical Study on Dimensionality Optimization in Text Mining for Linguistic Knowledge Acquisition -- A Semi-supervised Algorithm for Pattern Discovery in Information Extraction from Textual Data -- Bio Mining -- Mining Patterns of Dyspepsia Symptoms Across Time Points Using Constraint Association Rules -- Predicting Protein Structural Class from Closed Protein Sequences -- Learning Rules to Extract Protein Interactions from Biomedical Text -- Predicting Protein Interactions in Human by Homologous Interactions in Yeast -- Web Mining -- Mining the Customer’s Up-To-Moment Preferences for E-commerce Recommendation -- A Graph-Based Optimization Algorithm for Website Topology Using Interesting Association Rules -- A Markovian Approach for Web User Profiling and Clustering -- Extracting User Interests from Bookmarks on the Web -- Stream Mining II -- Mining Frequent Instances on Workflows -- Real Time Video Data Mining for Surveillance Video Streams -- Distinguishing Causal and Acausal Temporal Relations -- Bayesian Networks -- Online Bayes Point Machines -- Exploiting Hierarchical Domain Values for Bayesian Learning -- A New Restricted Bayesian Network Classifier -- Clustering II -- AGRID: An Efficient Algorithm for Clustering Large High-Dimensional Datasets -- Multi-level Clustering and Reasoning about Its Clusters Using Region Connection Calculus -- An Efficient Cell-Based Clustering Method for Handling Large, High-Dimensional Data -- Association Rules I -- Enhancing SWF for Incremental Association Mining by Itemset Maintenance -- Reducing Rule Covers with Deterministic Error Bounds -- Evolutionary Approach for Mining Association Rules on Dynamic Databases -- Semi-structured Data Mining -- Position Coded Pre-order Linked WAP-Tree for Web Log Sequential Pattern Mining -- An Integrated System of Mining HTML Texts and Filtering Structured Documents -- A New Sequential Mining Approach to XML Document Similarity Computation -- Classification I -- Optimization of Fuzzy Rules for Classification Using Genetic Algorithm -- Fast Pattern Selection for Support Vector Classifiers -- Averaged Boosting: A Noise-Robust Ensemble Method -- Improving Performance of Decision Tree Algorithms with Multi-edited Nearest Neighbor Rule -- Data Analysis -- HOT: Hypergraph-Based Outlier Test for Categorical Data -- A Method for Aggregating Partitions, Applications in K.D.D. -- Efficiently Computing Iceberg Cubes with Complex Constraints through Bounding -- Extraction of Tag Tree Patterns with Contractible Variables from Irregular Semistructured Data -- Association Rules II -- Step-by-Step Regression: A More Efficient Alternative for Polynomial Multiple Linear Regression in Stream Cube -- Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining -- Mining Open Source Software (OSS) Data Using Association Rules Network -- Parallel FP-Growth on PC Cluster -- Feature Selection -- Active Feature Selection Using Classes -- Electricity Based External Similarity of Categorical Attributes -- Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers -- Dealing with Relative Similarity in Clustering: An Indiscernibility Based Approach -- Stream Mining III -- Considering Correlation between Variables to Improve Spatiotemporal Forecasting -- Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach -- When to Update the Sequential Patterns of Stream Data? …”

Tabla de Contenidos: “…-- Self-classifying decision trees and AI tools -- Entropy -- One hot encoding -- Random forests -- Grid searching and A* (A-Star) -- The A* algorithm -- D* (D-Star or Dynamic A*) -- GPS path finding does not use a map! …”

Tabla de Contenidos: “…Advanced Programming Techniques -- Simulating physics with Box2D -- Exploring the concepts of Box2D -- Understanding rigid bodies -- Choosing body types -- Using shapes -- Using fixtures -- Simulating physics in the world -- Physics body editor…”

Tabla de Contenidos: “…Planning and Implementing a Basic SOHO Network Using Network Segmentation -- Determining Requirements -- Switches and Bridges at the Data Link Layer -- Hubs at the Physical Layer -- Environmental Considerations -- Summary -- Exam Essentials -- Written Lab -- Review Questions -- Chapter 6 Introduction to the Internet Protocol -- Introducing TCP/IP -- A Brief History of TCP/IP -- TCP/IP and the DoD Model -- The Process/Application Layer Protocols -- The Host-to-Host Layer Protocols -- The Internet Layer Protocols -- Data Encapsulation -- Summary -- Exam Essentials -- Written Lab -- Review Questions -- Chapter 7 IP Addressing -- IP Terminology -- The Hierarchical IP Addressing Scheme -- Network Addressing -- Private IP Addresses (RFC 1918) -- IPv4 Address Types -- Layer 2 Broadcasts -- Layer 3 Broadcasts -- Unicast Address -- Multicast Address (Class D) -- Internet Protocol Version 6 (IPv6) -- Why Do We Need IPv6? …”

“…Reconnues comme hauts lieux de biodiversité (hot spots), les zones humides africaines sont sources de nombreux services écosystémiques. …”

Tabla de Contenidos: