Tabla de Contenidos: “…Lăzurică on Popp Șerboianu's Book -- Comments -- 6.3.2 An Appeal to All Gypsies in Romania -- 6.3.3 A Call for a Meeting by the General Association of Gypsies in Romania -- 6.3.4 A Call for Participation at a Roma Congress in Bucharest, October 8, 1933 -- 6.3.5 The Congress of the Gypsies -- Comments -- 6.3.6 After the Congress of Roma -- Comments -- 6.3.7 The Statute and Regulations of the General Union of the Roma in Romania -- 6.3.8 The Artistic and Cultural Festival of the Roma -- 6.3.9 The Roma from Romania Have Met -- Comments -- 6.3.10 Roma Brothers! -- Comments -- 6.3.11 Our Programme -- Comments -- 6.3.12 An Appeal to the Roma from 1936.…”

Tabla de Contenidos: “…13.1.3 Residuals and normalization -- 13.2 Using pretrained BERT for spam classification -- 13.2.1 Understanding BERT -- 13.2.2 Classifying spam with BERT in TensorFlow -- 13.3 Question answering with Hugging Face's Transformers -- 13.3.1 Understanding the data -- 13.3.2 Processing data -- 13.3.3 Defining the DistilBERT model -- 13.3.4 Training the model -- 13.3.5 Ask BERT a question -- Summary -- Answers to exercises -- 14 TensorBoard: Big brother of TensorFlow -- 14.1 Visualize data with TensorBoard -- 14.2 Tracking and monitoring models with TensorBoard -- 14.3 Using tf.summary to write custom metrics during model training -- 14.4 Profiling models to detect performance bottlenecks -- 14.4.1 Optimizing the input pipeline -- 14.4.2 Mixed precision training -- 14.5 Visualizing word vectors with the TensorBoard -- Summary -- Answers to exercises -- 15 TFX: MLOps and deploying models with TensorFlow -- 15.1 Writing a data pipeline with TFX -- 15.1.1 Loading data from CSV files -- 15.1.2 Generating basic statistics from the data -- 15.1.3 Inferring the schema from data -- 15.1.4 Converting data to features -- 15.2 Training a simple regression neural network: TFX Trainer API -- 15.2.1 Defining a Keras model -- 15.2.2 Defining the model training -- 15.2.3 SignatureDefs: Defining how models are used outside TensorFlow -- 15.2.4 Training the Keras model with TFX Trainer -- 15.3 Setting up Docker to serve a trained model -- 15.4 Deploying the model and serving it through an API -- 15.4.1 Validating the infrastructure -- 15.4.2 Resolving the correct model -- 15.4.3 Evaluating the model -- 15.4.4 Pushing the final model -- 15.4.5 Predicting with the TensorFlow serving API -- Summary -- Answers to exercises -- appendix A Setting up the environment -- A.1 In a Unix-based environment…”

“…One night, after a few drinks at an office party, Quirke shuffles down into the morgue where he works and finds his brother-in-law, Malachy, altering a file he has no business even reading. …”
Acceso a las primeras páginas

“…Consequently, it deals with different themes: initiatory metamorphosis, adoption rites, incest and Oedipal motives, lovers as brother and sister, myth of the red cow, compulsory silence of young wives, etc…”

“…As with His Brother's Blood, Rowland rounds off The Graven Image with speculation on how, at one level, it might have reached a resolution. …”

Tabla de Contenidos: “…-- 6.5.3 Anonymous FTP -- 6.6 TFTP -- 6.7 SNMP - Big Brother is Watching You! -- 6.7.1 Arbeitsweise von SNMP -- 6.7.2 SNMP-Sicherheit -- 6.8 SMTP - Die Post ist da! …”

“…He shows how the "stern" Roman father and "indulgent" Roman mother had their exact counterparts elsewhere in the family: the harsh "father's brother" (patruus) and the tolerant "mother's brother" (avunculus). …”

“…Together with his younger brother Franz Ferdinand he headed the Mutzenbecher Group between 1901 and 1931, a group of companies which included Albingia Versicherungsgesellschaft. …”