Grant Humphries (9 results)

Hardcover/gebunden. Condition: gut. First Printing. Schwarzer Pappeinband mit (laminiertem) Rücken- und Deckeltitel, schwarzen Vorsätzen und illustriertem glanzfolienkaschiertem Schutzumschlag mit geprägtem Deckeltitel. Der Umschlag und die Einbandecken dezent berieben, ansonsten guter bis sehr guter Erhaltungszustand. "Seven nightmarish versions of Batman from seven dying alternate realities have been recruited by the dark god Barbatos to terrorize the World's Greatest Heroes in our universe. They threaten life across the Multiverse, and the Justice League may be powerless to stop them! We introduce you to: The Batman Who Laughs: a lunatic driven mad by his world's Joker. The Red Death: a thief who stole his reality's Speed Force power. The Drowned: a female, amphibious Batman. The Dawnbreaker: a twisted Green Lantern. The Murder Machine: a deranged, deadly cyborg. The Merciless: a warrior who wears the helmet of Ares. The Devastator: a part-human, part-Doomsday monster. Featuring stories from Scott Snyder, James Tynion IV, Peter J. Tomasi, Grant Morrison, Joshua Williamson, Ethan Van Sciver, Philip Tan, Tyler Kirkham, Francis Manapul, Riley Rossmo, Tony S. Daniel, Howard Porter, Doug Mahnke and many more! Collects the seven Dark Nights: Batman tie-in one-shots and Dark Knights Rising: The Wild Hunt #1." (Verlagstext) In englischer Sprache. Ohne Seitenzählung [216] pages. 4° (175 x 265mm). Manapul, Francis; Van Sciver, Ethan (illustrator).…

Hardcover. Condition: gut. 2018. Dark Nights Metal: Dark Knights Rising. Colorists: Ivan Plascencia, Rain Beredo, Jason Wright u.a. Letterers: Tom Napolitano and Clayton Cowles. Collection Cover Artists: Jason Fabok & Brad Anderson. In englischer Sprache. pages. Manapul, Francis; Van Sciver, Ethan (illustrator).…

Condition: New. In English.

Hardcover. Condition: Brand New. 441 pages. 9.50x6.50x1.00 inches. In Stock.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often 'messy' and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems.Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.…

Condition: new. Questo è un articolo print on demand.

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Shows ecologists cutting-edge methods that can help in understanding complex systems with multiple interacting variablesto and to&nbspform predictive hypotheses from large datasets&nbspProvides practical examples of the applicatio.…

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often 'messy' and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems.Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field. 468 pp. Englisch. …

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often 'messy' and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 468 pp. Englisch.…