“…Ces scansions correspondent au surréalisme, au Nouveau Roman, à Tel Quel et, dans la dernière période, à l’essor de la littérature à caractère autobiographique au sein de laquelle émerge en particulier une « hétérographie » ou un « roman du Je », selon la terminologie suggérée par Philippe Forest. Récuser le réalisme du xixe siècle n’a pas en effet pour corollaire de se détourner du réel et de la réalité dont on peut essayer de s’approcher en mariant l’appris avec le vécu, les potentialités de notre appareil psychique avec les ressources de la virtualité. …”

“…Highly energetic (110 to 120 cal/100 g), rich in minerals and vitamins, these foods meet the challenges of food security and nutrition. The forest regions of Africa, Latin America and Asia produce an average of 30 million tons per year (nearly 70% in Africa) and the average consumption is 50 to 200 kg/inhab/year. …”

“…The analysis is complemented with an overview of empirical studies on environmental effects, including evidence that label competition has led to market-driven convergence of standards in some sectors, such as forest certification. However, it is important that convergence leads to more holistic and streamlined ELIS rather than acting as a weakening influence on the stringency and quality of standards or how schemes are implemented, to maximise environmental effectiveness. …”

“…Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering and ensemble diversity Interpretability and explainability for ensemble methods Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. …”

“…Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering and ensemble diversity Interpretability and explainability for ensemble methods Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. …”

“…It is organized into 26 excellent chapters which include optical and infrared modeling, microwave scattering propagation, forests and vegetation, soils, ocean temperature, geographic information , object classification, data mining, image processing, passive optical sensor, multispectral and hyperspectral sensing, lidar, radiometer instruments, calibration, active microwave and SAR processing. …”

“…Many of the projects and sites investigated in this book are recent “firsts” in Laos: Laos’s first wildlife sanctuary for trafficked endangered species, its first botanical garden and its first planting plan for a community forest. Most often the agents and accomplices of neoliberal development, the planning and design professions, including landscape architecture, have little dialogue with either the mainstream natural sciences or critical social sciences that form the discourse of projects in Laos and comparable contexts. …”

“…The pressure on the environment that natural resource exploitation is creating should be addressed by increasing the share of gas and renewables in the energy mix, properly defining property rights and regulations regarding forest land, and implementing a positive implicit carbon price. …”

“…While the measurement of natural capital remains very incomplete, leaving out natural forests, water and soil, the measurement framework can readily be applied to more encompassing data on the natural capital stock, once it becomes available…”

“…In just 24 lessons of one hour or less, Sams Teach Yourself Unity Game Development in 24 Hours will help you master the Unity 2018 game engine at the heart of Ori and the Blind Forest , Firewatch , Monument Valley , and many other sizzling-hot games! …”

“…Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field…”

“…Autour d’Hugo Pratt, Jacques Tardi, Jean-Claude Forest, Didier Comès, Benoît Sokal, Benoît Peeters et François Schuiten ou François Boucq, le rédacteur en chef Jean-Paul Mougin dirige un groupe d’auteurs de talents qui défrichent de nouvelles voies narratives. …”