Carolus Jung

Multi tool use
Multi tool use





Schlaegel und eisen yellow.svg -3 (maxdubium) Latinitas huius rei maxime dubia est. Corrige si potes. Vide {{latinitas}}.



Wikidata-logo.svg Carolus Jung

Res apud Vicidata repertae:

Carolus Jung: imago

Carolus Jung: subscriptio




Nativitas: 26 Iulii 1875; Kesswil
Obitus: 6 Iunii 1961; Cussenachus
Patria: Helvetia
Nomen nativum: Carl Gustav Jung

Officium



Munus: psychotherapist, psychologist, Psychiater, philosophus, Autobiographus, essayist, non-fiction writer, mythographus
Patronus: Universitas Basiliensis, Universitas Turicensis



Familia



Coniunx: Emma Jung




Memoria



Sepultura: Cussenachus







Carolus Jung anno 1912


Carolus Gustavus Jung (Theodisce Carl Gustav Jung ; natus die 26 Iulii 1875; mortuus die 6 Iunii 1961) fuit psychiater Helveticus et conditor psychologiae analyticae. Jung saepe habetur primo ponenda psychologus hodiernus cum psyche ingenio religiosi et explorare in profundum. Natus est in Kesswil pagi Helvetici Thurgoviae quartus filius superstes, filius Paulus Achilles Jung et Aemilia Preiswerk. Pater eius fuit pastor Ecclesiae Reformatae Helveticae.




Index






  • 1 Opera selecta


  • 2 Bibliographia


  • 3 Nexus interni


  • 4 Nexus externi





Opera selecta |



  • Traumanalyse. Nach Aufzeichnungen der Seminare 1928–1930

  • Kinderträume. Zur Methodik der Trauminterpretation. Psychologische Interpretation von Kinderträumen

  • Der Mensch und seine Symbole

  • Das Rote Buch


  • Responsum in Iob (Antwort auf Hiob, 1952)



Bibliographia |



  • Kerr, John. 1993. A Most Dangerous Method: the story of Jung, Freud, and Sabina Spielrein. Novi Eboraci: Knopf. (Paginae selectae apud Google Books)

  • McLynn, Frank. 1996. Carl Gustav Jung: a biography. Novi Eboraci: Bantam. ISBN 0-593-03391-4.


Nexus interni



  • Sigismundus Freud

  • Martinus Buber

  • Syzygia (gnosis)



Nexus externi |


  • Associatio Psychoanalytica Internationalis

.mw-parser-output .stipula{padding:3px;background:#F7F8FF;border:1px solid grey;margin:auto}.mw-parser-output .stipula td.cell1{background:transparent;color:white}




David's face
Haec stipula ad biographiam spectat. Amplifica, si potes!








tp,1SJEwDeDOi 9Sb7VU,a,4tPIvrcgUHMrA3wWbr UPS
-
ai 8Zgv57Es,cKSjCsM4Zd G8TdJPvlhvyfQ6K fGm2lIh4ykmnINE,vOe

Popular posts from this blog

Tabula Rosettana

Image
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...
Read more

How to label and detect the document text images

Image
1 $begingroup$ 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...
Read more

LSTM sequence prediction: 3d input to 2d output

Image
1 $begingroup$ 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...
Read more