“…Ce plafonnement de la circulation traduit-il l'approche de la saturation (découplage entre évolutions des trafics et des revenus dans les régions les plus denses ou au-delà d'un certain niveau de vie?) …”

“…Les études sur le métabolisme dans les cultures en rotation sont conduites pour déterminer la nature et la quantité des résidus de pesticide absorbé dans les cultures alternées, destinées à servir dans l’alimentation humaine ou animale. …”

“…Ils visent plutôt à compléter les principes existants du CAD pour l’évaluation de l’aide, mettant l’accent sur les domaines qui méritent une attention toute particulière, la nature des activités entreprises et le réseau multi-acteurs, aux composantes étroitement liées les unes aux autres, par lequel la communauté internationale apporte son assistance humanitaire. …”

“…If you are new to Cassandra but well-versed in RDBMS modeling and design, then it is natural to model data in the same way in Cassandra, resulting in poorly performing applications and losing the real purpose of Cassandra. …”

“…Despite the powerful and versatile nature of the tool enveloping strong support for nonlinear development and the ability to handle large projects efficiently, it is a complex tool and often regarded as ?…”

“…Write a cool program to manage inventories in your backpack In Detail Kids are always the most fast-paced and enthusiastic learners, and are naturally willing to build stuff that looks like magic at the end (when it works!). …”

Tabla de Contenidos: “…Chapter 5: Unsupervised Learning - Clustering and Dimensionality Reduction -- Dimensionality reduction -- Unsupervised dimensionality reduction -- Principal Component Analysis -- Truncated SVD -- Truncated SVD for categorical and sparse data -- Random projection -- Cluster analysis -- Hierarchical methods -- K-means -- Choosing K in K-Means -- DBSCAN -- Clustering for supervised learning -- Clusters as features -- Clustering as dimensionality reduction -- Supervised learning via clustering -- Evaluation -- Manual evaluation -- Supervised evaluation -- Unsupervised Evaluation -- Summary -- Chapter 6: Working with Text - Natural Language Processing and Information Retrieval -- Natural Language Processing and information retrieval -- Vector Space Model - Bag of Words and TF-IDF -- Vector space model implementation -- Indexing and Apache Lucene -- Natural Language Processing tools -- Stanford CoreNLP -- Customizing Apache Lucene -- Machine learning for texts -- Unsupervised learning for texts -- Latent Semantic Analysis -- Text clustering -- Word embeddings -- Supervised learning for texts -- Text classification -- Learning to rank for information retrieval -- Reranking with Lucene -- Summary -- Chapter 7: Extreme Gradient Boosting -- Gradient Boosting Machines and XGBoost -- Installing XGBoost -- XGBoost in practice -- XGBoost for classification -- Parameter tuning -- Text features -- Feature importance -- XGBoost for regression -- XGBoost for learning to rank -- Summary -- Chapter 8: Deep Learning with DeepLearning4J -- Neural Networks and DeepLearning4J -- ND4J - N-dimensional arrays for Java -- Neural networks in DeepLearning4J -- Convolutional Neural Networks -- Deep learning for cats versus dogs -- Reading the data -- Creating the model -- Monitoring the performance -- Data augmentation -- Running DeepLearning4J on GPU -- Summary…”

“…What You Will Learn Program idiomatic Haskell that's also surprisingly efficient Improve performance of your code with data parallelism, inlining, and strictness annotations Profile your programs to identify space leaks and missed opportunities for optimization Find out how to choose the most efficient data and control structures Optimize the Glasgow Haskell Compiler and runtime system for specific programs See how to smoothly drop to lower abstractions wherever necessary Execute programming for the GPU with Accelerate Implement programming to easily scale to the cloud with Cloud Haskell In Detail Haskell, with its power to optimize the code and its high performance, is a natural candidate for high performance programming. …”

“…What You Will Learn Perform live analysis on victim or suspect Windows systems locally or remotely Understand the different natures and acquisition techniques of volatile and non-volatile data. …”

“…Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. …”

“…Writing tasks, applying plugins, and creating build logic will be your second nature. Style and approach This step-by-step guide aims to cover the fundamentals of Gradle and focuses on providing the practical skills required to develop web application. …”

“…Worse, we keep doing these things, over and over again, naturally and instinctively. But it doesn’t have to be that way. …”

“…Master the principles to make applications robust, scalable and responsive About This Book Implement concurrent applications using the Java 9 Concurrency API and its new components Improve the performance of your applications and process more data at the same time, taking advantage of all of your resources Construct real-world examples related to machine learning, data mining, natural language processing, and more Who This Book Is For This book is for competent Java developers who have basic understanding of concurrency, but knowledge of effective implementation of concurrent programs or usage of streams for making processes more efficient is not required What You Will Learn Master the principles that every concurrent application must follow See how to parallelize a sequential algorithm to obtain better performance without data inconsistencies and deadlocks Get the most from the Java Concurrency API components Separate the thread management from the rest of the application with the Executor component Execute phased-based tasks in an efficient way with the Phaser components Solve problems using a parallelized version of the divide and conquer paradigm with the Fork / Join framework Find out how to use parallel Streams and Reactive Streams Implement the ?…”

“…Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features Explore the ins and outs of genetic algorithms with this fast-paced guide Implement tasks such as feature selection, search optimization, and cluster analysis using Python Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications Book Description Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. …”

“…Leverage the power of Python and statistical modeling techniques for building accurate predictive models Key Features Get started with Python's rich suite of libraries for statistical modeling Implement regression and clustering, and train neural networks from scratch Discover real-world examples on training end-to-end machine learning systems in Python Book Description Python's ease-of-use and multi-purpose nature has made it one of the most popular tools for data scientists and machine learning developers. …”

Tabla de Contenidos: “…Neural networks and gradient-based optimization -- Applying machine learning to structured data -- Utilizing computer vision -- Understanding time series -- Parsing textual data with natural language processing -- Using generative models -- Reinforcement learning for financial markets -- Privacy, debugging, and launching your products -- Fighting bias -- Bayesian inference and probabilistic programming…”

“…From there, the middleware systems provided the exploitation and advanced functions to mature the technology. The component in the middle of all this great technology can easily be taken for granted. …”

“…Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. …”

“…Write elegant, reusable, and efficient code in any situation Understand when to use the functional or the object oriented programming approach Create bulletproof, reliable software by writing tests to support your code Explore examples of GUIs, scripting, data science and web applications Learn to be independent, capable of fetching any resource you need, as well as dig deeper In Detail Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. …”

“…You will be using real-world examples of complex algorithms related to machine learning, data mining, natural language processing, image processing in client / server en..…”