├── .devcontainer └── devcontainer.json ├── .gitignore ├── Assignments ├── assignment01-python-numpy-programming.ipynb └── assignment03.ipynb ├── Data ├── Assignment03-classification-model │ ├── CC359-data-split │ │ └── 5-fold-CV-stratified │ │ │ ├── test_1.txt │ │ │ ├── test_2.txt │ │ │ ├── test_3.txt │ │ │ ├── test_4.txt │ │ │ ├── test_5.txt │ │ │ ├── train_1.txt │ │ │ ├── train_2.txt │ │ │ ├── train_3.txt │ │ │ ├── train_4.txt │ │ │ ├── train_5.txt │ │ │ ├── val_1.txt │ │ │ ├── val_2.txt │ │ │ ├── val_3.txt │ │ │ ├── val_4.txt │ │ │ └── val_5.txt │ └── volumetry_cc359.xlsx ├── Traditional-ML │ ├── 5-fold-CV-stratified │ │ ├── test_1.txt │ │ ├── test_2.txt │ │ ├── test_3.txt │ │ ├── test_4.txt │ │ └── test_5.txt │ ├── Puzzle description - 2018.pdf │ ├── puzzle_test_dataset.csv │ ├── puzzle_train_dataset.csv │ └── volumetry_cc359.xlsx └── Tutorial-RNN │ ├── Google_Stock_Price_Test.csv │ └── Google_Stock_Price_Train.csv ├── Dockerfile ├── Figures ├── bbox.png ├── bin_image.png ├── gnn_batching.png ├── lbp.png ├── lbp2.png ├── lena.png ├── rgb.png ├── train_val_test.png └── transductive_inductive.png ├── GPT ├── input.txt ├── run.py └── scale.py ├── JNotebooks ├── DANN.ipynb ├── Scratch │ └── garbage_classification.ipynb ├── W&B_tutorial.ipynb ├── alzheimer_classificiation.ipynb ├── denoising-1d-pt-updated.ipynb ├── denoising-1d-pt.ipynb ├── denoising-1d.ipynb ├── domain-adversarial-network-mnist-mnistm.ipynb ├── fully_connected_NN_mnist_pytorch.ipynb ├── garbage_classification_text.ipynb ├── garbage_classifier_images_w2023.ipynb ├── gender_classification.ipynb ├── gnn_tutorial.ipynb ├── heat_eqn_tutorial.ipynb ├── next_frame_video_prediction_with_convLSTMs.ipynb ├── traditional_ml_balanced_rf.ipynb ├── transfer_learning_BERT.ipynb ├── transfer_learning_imagenet.ipynb ├── tutorial01-python.ipynb ├── tutorial02-numpy.ipynb ├── tutorial02_1_python_sumpy_programming_style.ipynb ├── tutorial03-overfitting_regularization.ipynb ├── tutorial04_fully_connected_neural_network_2D_synthetic_example.ipynb ├── tutorial04_fully_connected_neural_network_2D_synthetic_example_colab.ipynb ├── tutorial05_different_approaches_to_define_neural_networks_keras.ipynb ├── tutorial06_tensorboard.ipynb ├── tutorial07_softmax_one_hot_encoding_loss_functions.ipynb ├── tutorial08_step_by_step_MNIST_digits_classification.ipynb ├── tutorial09_fully_connected_neural_networks_revisited.ipynb ├── tutorial10_step_by_step_MNIST_digits_classification_cnn.ipynb ├── tutorial11_transfer_learning_imagenet.ipynb ├── tutorial12_auto_encoder_mnist.ipynb ├── tutorial13_unet_jpeg_restoration.ipynb ├── tutorial14_recurrent_neural_networks.ipynb ├── tutorial15_generative_adversarial_networks.ipynb ├── tutorial16_auto_ML.ipynb ├── tutorial17_self_supervised_learning_CIFAR10_cnn.ipynb ├── tutorial_bias_mitigation_fairness.ipynb ├── tutorial_rf_sex_classification.ipynb └── tutorial_traditional_ML.ipynb ├── LICENSE ├── Modules ├── dataset_utils.py └── models_imagenet.py ├── OUTLINE.txt ├── PDFs ├── ENEL645 │ ├── ENEL645-ResponsibleAI.pdf │ ├── ENEL645-XAI.pdf │ ├── ENEL645_lecture01_course_overview.pdf │ ├── ENEL645_lecture02_fundamentals_ml.pdf │ ├── ENEL645_lecture03_overfitting_regularization.pdf │ ├── GANs_DA.pdf │ ├── SSL.pptx │ ├── Transformers.pdf │ ├── Transformers.pptx │ ├── lecture04_deep_learning_intuition.pdf │ ├── lecture05_fully_connected_neural_networks.pdf │ ├── lecture06_data_normalization.pdf │ ├── lecture07_convolutional_neural_networks.pdf │ ├── lecture08_transfer_learning.pdf │ ├── lecture09_unets.pdf │ ├── lecture16_phsyics_informed_NNs.pdf │ └── lecture17_graph_NNs.pdf └── ENSF619 │ ├── ENSF619_lecture01_course_overview.pdf │ ├── ENSF619_lecture02_fundamentals_ml.pdf │ ├── ENSF619_lecture03_overfitting_regularization.pdf │ ├── lecture04_deep_learning_intuition.pdf │ ├── lecture05_fully_connected_neural_networks.pdf │ ├── lecture06_data_normalization.pdf │ ├── lecture07_convolutional_neural_networks.pdf │ ├── lecture08_parameters_and_memory.pdf │ ├── lecture08_transfer_learning.pdf │ └── lecture09_unets.pdf ├── README.md ├── Rubric ├── ENEL645 │ ├── Final-project-description-rubric-ENEL645.pdf │ ├── assignment01-Garbage-classification-assignment.pdf │ └── assignment02-classification-programming.pdf ├── ENSF619 │ ├── ENSF-619-02-paper-reading-assignment.pdf │ ├── Final-project-description-rubric-ENSF.pdf │ └── Tutorial-creation-assignment.pdf └── Team-work-requirement.pdf ├── SLURM ├── Garbage-classifier-w2023 │ ├── garbage_main.py │ ├── garbage_utils.py │ └── run_garbage_classifier.slurm ├── TL-garbage-classification │ ├── transfer-learning-imagenet.slurm │ └── transfer_learning_imagenet.py └── unet-segmentation │ ├── Data │ ├── test-set.txt │ ├── train-set.txt │ └── val-set.txt │ ├── data_generator.py │ ├── main.py │ ├── models.py │ ├── unet_test.ipynb │ └── utils.py ├── Submissions └── lbp_compiled.ipynb ├── final-project-template.docx └── requirements.txt 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