Nativitas: 12 Martii 1685; Canicopolis Obitus: 14 Ianuarii 1753; Oxonia Patria: Kingdom of Ireland
Officium
Officium: Bishop of Cloyne, Dean of Derry, Dean of Dromore Munus: philosophus, Anglican priest, Scriptor, epistemologist, philosopher of science, metaphysician
Consociatio
Religio: Anglicanismus
Familia
Coniunx: Anne Forster Proles: Lucia Berkeley, Henry Berkeley, George Berkeley
Memoria
Sepultura: Christ Church Cathedral
Episcopus Berkeleius.
Georgius Berkeley sive Georgius Berkeleius[1] sive Georgius Berkleius[2] fuit Socius Collegii Trinitatis Dublinensis, Deriensis Decanus postea episcopus Cluanensis Ecclesiae Hiberniae (Anglicanae) et philosophus Hibernicus qui Empirismum Ioannis Lockii dicit. Principium philosophiae idealistae suae est "Esse est percipi." Et theoriam Issaci Newtoni cum libro De Motu et spatio absoluto.
Sepultus est Oxoniae in Aedibus Christi.
Urbi in California vulgo Berkeley nomen ad honorem Georgii Berkeleii impositum est.
Index
1Opera
2Nexus interni
3Nota
4Bibliographia
5Nexus externi
Opera |
Berkeleius scripsit:
Commonplace book, 1702-1710
Arithmetica absque algebra aut Euclide demonstrata (1704)
Theoria visionis, 1709
De Motu (1721)
Siris (1744)
Nexus interni
Abstractionismus
Innotabile
Nota |
↑liber apud books.google.de Compendium philosophiæ ad usum seminariorum, auctore M***. Sti Sulpitii. . . .
↑Cataloguus Librorum in Bibliotheca Collegii Yalensis Universitatis Yalensis.
Bibliographia |
Arend Kulenkampff: George Berkeley. Beck, Monaci anno 1987, ISBN 3-406-32280-8
Rudolf Metz: George Berkeley : Leben und Lehre. Frommann, Stuttgarti anno 1968
Bruno Marciano, George Berkeley. Estetica e idealismo, Nova Scripta, Genova 2010
Nexus externi |
De Georgio Berkeley pagina a Lisa Downing scripta
De Georgio Berkeley pagina a Daniel E. Flage
De Georgio Berkeley .mw-parser-output .existinglinksgray a,.mw-parser-output .existinglinksgray a:visited{color:gray}.mw-parser-output .existinglinksgray a.new{color:#ba0000}.mw-parser-output .existinglinksgray a.new:visited{color:#a55858} (Theodisce)
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
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This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
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I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...
Georgius Berkeley
