Search Results - (architect OR (architects OR architectures))-designed (houses OR house)

Prescriptions for a healthy house : a practical guide for architects, builders, and homeowners / by Baker, Paula

Turkish house : in search of spatial identity = Türk evi : kendi mekanının arayışı içinde / by Küçükerman, Önder

Breuer houses / by Driller, Joachim

Housing and Housing Politics in European Metropolises Jahrbuch StadtRegion 2017/2018 /

Full-text access

The Care and conservation of Georgian houses /

Anadolu kerpiç mimarlığı / by Çelebi, M. Rıfat

Subjects: “…Adobe houses Turkey. 68561…”

İç mimarlığın temelleri / by Coles, John

Full-text access

New housing competition for the multi-function administrative city /

Haneden ev haline : "Türk Evi"nde mimari, düzenleme, pratik by Kurt, Seyhan

Subjects:

Türk evi / by Bektaş, Cengiz

Subjects:

Geleneksel divriği evlerinde ahşap süslemeli tavanlar / by Ünlüdil, Selma

Subjects:

Jean Prouvé : la Maison tropicale = [Jean Prouvé]. the tropical house / by Prouvé, Jean

Das alttürkische Wohnhaus : auf der suche nach der raumlichen identiat / by Küçükerman, Önder, 1939-

Subjects: “…Housing Turkey. 47746…”

Landscape Ecology Principles in Landscape Architecture and Land-Use Planning. by Dramstad, Wenche

Full-text access

Historical Earthquake-Resistant Timber Framing in the Mediterranean Area HEaRT 2015 /

Table of Contents: “…Cooperating Timber and Stone Antiseismic frames in Historic Structures of Greece -- The Role of a Post-Byzantine Timber Roof Structure in the Seismic Behavior of a Masonry Building - The Case of a Unique Type of Timber-Roofed Basilicas in Cyprus (15th-19th century) -- Historic Earthquake-Resistant Constructive Techniques Reinforced by Wooden Logs in Algeria -- A Comparative Evaluation of the Results of Two Earthquakes: Istanbul and Lisbon Earthquake in 18th Century -- Some Examples of Turkısh Houses Wıth Wooden Frame in the Seısmıc Zone Anatolıa -- Traditional Timber Housing Structure in Zeyrek -- Possible Precursors of Pombalino cage -- Local Seismic Cultures: The use of timber frame structures in the South of Portugal -- The use Of Wood With an Anti-Seismic Function in the Architecture of Palermo During the 18th Century.…”
Full-text access

The Advances in Civil Engineering Materials Selected Papers of the ICACE 2018 held in Batu Ferringhi, Penang Malaysia on 9th -10th May 2018 /

Full-text access

An Ethnography of the Goodman Building The Longest Rent Strike / by Caldararo, Niccolo

Table of Contents: “…Part I: The National Context for the Goodman Buildilng -- Chapter 1: The Housing Crisis in America and the Policies That Created and Promote It -- Part II: Setting the Scene of the Goodman Building -- Chapter 2: Preface to the Goodman Building Ethnography -- Chapter 3: The Background and Setting -- Chapter 4. …”
Full-text access

Cloud Computing Concepts and Practices / by Sehgal, Naresh Kumar, Bhatt, Pramod Chandra P.

Full-text access

Deep Learning with Python, Second Edition. by Chollet, Francois

Table of Contents: “…4.1.5 Using a trained model to generate predictions on new data -- 4.1.6 Further experiments -- 4.1.7 Wrapping up -- 4.2 Classifying newswires: A multiclass classification example -- 4.2.1 The Reuters dataset -- 4.2.2 Preparing the data -- 4.2.3 Building your model -- 4.2.4 Validating your approach -- 4.2.5 Generating predictions on new data -- 4.2.6 A different way to handle the labels and the loss -- 4.2.7 The importance of having sufficiently large intermediate layers -- 4.2.8 Further experiments -- 4.2.9 Wrapping up -- 4.3 Predicting house prices: A regression example -- 4.3.1 The Boston housing price dataset -- 4.3.2 Preparing the data -- 4.3.3 Building your model -- 4.3.4 Validating your approach using K-fold validation -- 4.3.5 Generating predictions on new data -- 4.3.6 Wrapping up -- Summary -- 5 Fundamentals of machine learning -- 5.1 Generalization: The goal of machine learning -- 5.1.1 Underfitting and overfitting -- 5.1.2 The nature of generalization in deep learning -- 5.2 Evaluating machine learning models -- 5.2.1 Training, validation, and test sets -- 5.2.2 Beating a common-sense baseline -- 5.2.3 Things to keep in mind about model evaluation -- 5.3 Improving model fit -- 5.3.1 Tuning key gradient descent parameters -- 5.3.2 Leveraging better architecture priors -- 5.3.3 Increasing model capacity -- 5.4 Improving generalization -- 5.4.1 Dataset curation -- 5.4.2 Feature engineering -- 5.4.3 Using early stopping -- 5.4.4 Regularizing your model -- Summary -- 6 The universal workflow of machine learning -- 6.1 Define the task -- 6.1.1 Frame the problem -- 6.1.2 Collect a dataset -- 6.1.3 Understand your data -- 6.1.4 Choose a measure of success -- 6.2 Develop a model -- 6.2.1 Prepare the data -- 6.2.2 Choose an evaluation protocol -- 6.2.3 Beat a baseline -- 6.2.4 Scale up: Develop a model that overfits.…”
Full-text access

First RILEM International Conference on Concrete and Digital Fabrication - Digital Concrete 2018

Full-text access