├── Bild5_18.png ├── Bild5_2.png ├── Daten ├── anlernprozess_steuern.csv ├── breast_cancer_wisconsin.csv ├── householde_power_hourly.csv ├── insurance.csv ├── iris_data.csv ├── jena_climate_complete_hourly.csv ├── jena_climate_temp_monthly.csv ├── music_movies_interests_pref.csv ├── oracle_monthly.csv ├── rnn_sinus.csv ├── sales_dat_prep.csv ├── trip_weather_hourly.csv └── verlaufsdaten.csv ├── Interaktionen.png ├── Kapitel_3_MachineLearning_Standardverfahren ├── .ipynb_checkpoints │ └── a_lineare_regression_health_care-checkpoint.ipynb ├── 3_1_Lineare_Regression │ ├── .ipynb_checkpoints │ │ └── a_lineare_regression_insurance-checkpoint.ipynb │ └── a_lineare_regression_insurance.ipynb ├── 3_2_Logistische_Regression │ ├── .ipynb_checkpoints │ │ └── a_logistische_regression_breast_cancer_daten-checkpoint.ipynb │ └── a_logistische_regression_breast_cancer_daten.ipynb ├── 3_3_Softmax_Regression │ ├── .ipynb_checkpoints │ │ └── a_softmax_regression_iris_data-checkpoint.ipynb │ └── a_softmax_regression_iris_data.ipynb ├── 3_4_Feature_Vorverarbeitung │ ├── .ipynb_checkpoints │ │ ├── a_one_hot_encoding-checkpoint.ipynb │ │ ├── b_pca_breast_cancer_data-checkpoint.ipynb │ │ └── c_vorverarbeitung_in_der_praxis-checkpoint.ipynb │ ├── a_one_hot_encoding.ipynb │ ├── b_pca_breast_cancer_data.ipynb │ ├── c_vorverarbeitung_in_der_praxis.ipynb │ ├── model.pkl │ ├── ohe.pkl │ └── scaler.pkl └── 3_5_Zeitreihenanalysen_mit_Standardverfahren │ ├── .ipynb_checkpoints │ ├── a_zeitreihen_mit_standardverfahren_verarbeiten-checkpoint.ipynb │ ├── b_zeitreihen_mit_interaktionsvariablen_arbeiten-checkpoint.ipynb │ └── c_zeitreihen_polynomial_features-checkpoint.ipynb │ ├── a_zeitreihen_mit_standardverfahren_verarbeiten.ipynb │ ├── b_zeitreihen_mit_interaktionsvariablen_arbeiten.ipynb │ └── c_zeitreihen_polynomial_features.ipynb ├── Kapitel_4_ARIMA ├── 4_5_ARIMA │ ├── .ipynb_checkpoints │ │ └── a_ARIMA_Modell_Oracle-checkpoint.ipynb │ └── a_ARIMA_Modell_Oracle.ipynb ├── 4_6_Seasonal_ARIMA │ ├── .ipynb_checkpoints │ │ └── a_Seasonal_arima_climate-checkpoint.ipynb │ └── a_Seasonal_arima_climate.ipynb └── 4_8_ARIMA_mit_exogener_Variable │ ├── .ipynb_checkpoints │ └── a_ARIMA_mit_exogener_Variable_Sales_data-checkpoint.ipynb │ └── a_ARIMA_mit_exogener_Variable_Sales_data.ipynb ├── Kapitel_5_DeepLearning ├── 5_2_Einfache_Modelle_mit_Keras_aufbauen │ ├── .ipynb_checkpoints │ │ ├── a_Lineare_Regression_Simulation_Keras-checkpoint.ipynb │ │ ├── b_Binäre_Klassifikation_Keras_breast_cancer_data-checkpoint.ipynb │ │ └── c_Anlernprozess_steuern-checkpoint.ipynb │ ├── a_Lineare_Regression_Simulation_Keras.ipynb │ ├── b_Binäre_Klassifikation_Keras_breast_cancer_data.ipynb │ ├── c_Anlernprozess_steuern.ipynb │ └── keras_test_model.h5 ├── 5_4_Rekurrente_Netze │ ├── .ipynb_checkpoints │ │ ├── a_Training_rekurrentes_Layer_Sinus_Bsp-checkpoint.ipynb │ │ └── b_Stromverbrauch_Kapitel_544-checkpoint.ipynb │ ├── a_Training_rekurrentes_Layer_Sinus_Bsp.ipynb │ ├── b_Stromverbrauch_Kapitel_544.ipynb │ ├── consumption_power_model.h5 │ └── sin_model_saved.h5 ├── 5_5_Konvolutionale_Netze │ ├── .ipynb_checkpoints │ │ └── a_Verlaufsdaten_Produktion-checkpoint.ipynb │ ├── a_Verlaufsdaten_Produktion.ipynb │ └── cnn_produktion_simple.h5 ├── 5_6_Zeitfenster_in_der_Praxis │ ├── .ipynb_checkpoints │ │ └── a_Temperaturdaten_mit_Generator_anlernen-checkpoint.ipynb │ ├── a_Temperaturdaten_mit_Generator_anlernen.ipynb │ ├── climate_model.h5 │ ├── scaler_x.pkl │ └── scaler_y.pkl ├── 5_7_Domänenspezifische_Lernarchitekturen │ ├── .ipynb_checkpoints │ │ └── d_Saisonales_Modell_mit_woechentlichen_Mittelwerten-checkpoint.ipynb │ ├── climate_model_seasonal_means.h5 │ └── d_Saisonales_Modell_mit_woechentlichen_Mittelwerten.ipynb └── 5_8_Autoencoder │ ├── .ipynb_checkpoints │ └── b_Autoencoder_kategoriale_Daten-checkpoint.ipynb │ ├── autoencoder_ohe.h5 │ └── b_Autoencoder_kategoriale_Daten.ipynb ├── readme.md └── requirements.txt /Bild5_18.png: 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