├── 365_Correlation_Analysis_Part_1.ipynb ├── README.md ├── YR1_hough_transform.py ├── YR2_batch_processing_multiple_images_in_python.py ├── generating_labeled_data_for_deep_learning ├── data_augmentation_custom_code.py ├── divide_images_to_patches.py ├── prediction_using_feature_extraction_and_random_forest.py └── training_using_feature_extraction_and_random_forest.py ├── images ├── Alloy_gradient.jpg ├── BSE.tif ├── Osteosarcoma_01.tif ├── Osteosarcoma_01_1sigma_blur.tif ├── Osteosarcoma_01_25Sigma_noise.tif ├── Osteosarcoma_01_2sigma_blur.tif ├── Osteosarcoma_01_8bit.ome.tiff ├── Osteosarcoma_01_8bit_salt_pepper.tif ├── Osteosarcoma_01_8bit_salt_pepper_cropped.tif ├── Osteosarcoma_01_small.tif ├── Osteosarcoma_01_transl.tif ├── Osteosarcoma_01_transl_rot.tif ├── Ti_powder.tif ├── Ti_powder_single.tif ├── bubbles.tif ├── cast_iron1.tif ├── cast_iron2.jpg ├── monalisa.jpg ├── sandstone.tif ├── sandstone_blur_2sigma.tif ├── sandstone_low_contrast.tif ├── scratch_time_series.tif └── synthetic.jpg ├── smooth_tiled_predictions.py ├── tutorial04_appreciating the simplicity of python.py ├── tutorial09_top5_libraries.py ├── tutorial101_DL_terminology_training_and_valid_curves.ipynb ├── tutorial102_DL_terminology_training_testing_validation_data.ipynb ├── tutorial103_DL_terminology_data_augmentation1.ipynb ├── tutorial103_DL_terminology_data_augmentation2.ipynb ├── tutorial104_DL_terminology_callbacks_checkpoints_earlystopping.ipynb ├── tutorial105_DL_terminology_Learning_rate_scheduling.ipynb ├── tutorial107_7_steps_to_DL.ipynb ├── tutorial109_regression_housing.ipynb ├── tutorial10_first_lines_of_code.py ├── tutorial110_binary_classification_malaria.ipynb ├── tutorial111_multiclass_classification_cifar.ipynb ├── tutorial112_top_k_accuracy_multiclass_classif.ipynb ├── tutorial113_what_are_autoencoders.ipynb ├── tutorial114_can_autoencoders_be_used_for_semantic_segm.ipynb ├── tutorial116_upsampling_vs_conv2D_transpose.ipynb ├── tutorial117_building_unet_using_encoder_decoder_blocks.ipynb ├── tutorial118_binary_semantic_segmentation_using_unet.ipynb ├── tutorial119_multiclass_semantic_segmentation.ipynb ├── tutorial11_operators_basic_math.py ├── tutorial120_applying_trained_unet_model_to_large_images.ipynb ├── tutorial121a_split_folder_into_train_test_val.ipynb ├── tutorial121b_loading_data_from_drive_in_batches_for_unet_training.ipynb ├── tutorial122_3D_Unet.ipynb ├── tutorial123_various_pretrained_DL_models.ipynb ├── tutorial124_multiclass_semantic_segmentation_using_pre_trained_networks.ipynb ├── tutorial125_semantic_segmentation_using_Xfer_learning_VGG_XGBoost.ipynb ├── tutorial126_classification_using_Xfer_learning_VGG_XGBoost.ipynb ├── tutorial12_lists.py ├── tutorial13_tuples.py ├── tutorial14_dictionaries.py ├── tutorial15_numpy_arrays.py ├── tutorial16_data_types_in_python.py ├── tutorial17_if_else_statements.py ├── tutorial18_automating_using_while_loops.py ├── tutorial19_automating_using_for_loops.py ├── tutorial20_functions.py ├── tutorial21_lambda_functions.py ├── tutorial22_reading_images_into_python.py ├── tutorial23_reading_proprietary_images_into_python.py ├── tutorial24_saving_images_in_python.py ├── tutorial25_viewing_images_in_python.py ├── tutorial26_plotting_using_pyplots_python.py ├── tutorial27_using_glob_to_read_multiple_images.py ├── tutorial28_using_os.listdir_to_read_multiple_images.py ├── tutorial29_image_processing_using_scikit-image.py ├── tutorial30_image_processing_using_opencv.py ├── tutorial31_image_sharpening_using_unsharp_mask.py ├── tutorial32_denoising_using_gaussian.py ├── tutorial33_denoising_using_median.py ├── tutorial34_denoising_using_bilateral.py ├── tutorial35_denoising_using_NLM.py ├── tutorial36_denoising_using_total_variance.py ├── tutorial37_denoising_using_BM3D.py ├── tutorial38_edge_detection_filters.py ├── tutorial39_edge_detection_using_canny.py ├── tutorial40_what_is_fourier_transform.py ├── tutorial41_image_filters_using_fourier_transform_DFT.py ├── tutorial42_what_is_histogram_equalization_CLAHE.py ├── tutorial43_shading_correction_using_rolling_ball.py ├── tutorial44_a_note_about_color_spaces.py ├── tutorial45_applying_grey_scale_filters_to_color_images.py ├── tutorial46_img_registration_libraries_in_python.py ├── tutorial47_image_registration_using_pystackreg.py ├── tutorial48_reference_based_image_quality.py ├── tutorial49_no_reference_image_quality_sharpness.py ├── tutorial50_no_reference_image_quality_assessment_BRISQUE.py ├── tutorial51_what_is_image_segmentation_and_thresholding.py ├── tutorial52_multi_thresholding_and_morph_operators.py ├── tutorial53-using_texture_to_segment_images.py ├── tutorial54-scratch_assay_in_python.py ├── tutorial55_image_segmentation_followed_by_measurements.py ├── tutorial56_simple_blob_detector.py ├── tutorial56b_hough_circles.py ├── tutorial57_Cell Nuclei analysis using watershed.py ├── tutorial58_object_detection_by_template_matching.py ├── tutorial59_data_analysis_using_Pandas_Intro_data_loading.py ├── tutorial60_data_analysis_using_Pandas_data_handling.py ├── tutorial61_data_analysis_using_Pandas_data_sorting.py ├── tutorial62_data_analysis_using_Pandas_data_grouping.py ├── tutorial63_dealing with null data_in_Pandas.py ├── tutorial64_basic_plotting_of_data_from_pandas_dataframe.py ├── tutorial65_pandas_data_plotting_using_Seaborn.py ├── tutorial66a_need_for_data_normalization.py ├── tutorial66b_various_data_normalization_techniques.py ├── tutorial68-linear_regression.py ├── tutorial69-multi_linear_regression.py ├── tutorial70-Logistic_regression_breast_cancer.py ├── tutorial71-K_means.py ├── tutorial71a-K_means_demo.py ├── tutorial72-GMM_image_segmentation.py ├── tutorial74_what is gabor filter.py ├── tutorial75-Extracting_features_using_Gabor_Filter.py ├── tutorial76-Random Forest_breast_cancer.py ├── tutorial77-SVM_breast_cancer.py ├── tutorial79_part1_traditional_machine_learning_based_img_segmentation.py ├── tutorial80_part2_predict_ML_segmentation_All_filters_RForest.py ├── tutorial81_part3_Feature_based_segm_RF_multi_image_TRAIN.py ├── tutorial83_feature_extraction_RF_classification_V2.0.py ├── tutorial84_IoU.py ├── tutorial85_working_with_imbalanced_data.py ├── tutorial86_87_PREDICT_img_segm_using_RF_XGB_LGBM.py ├── tutorial86_87_TRAIN_img_segm_using_RF_XGB_LGBM.py ├── tutorial88_BORUTA_for_feature_selection.py ├── tutorial91_binary_classification_breast_cancer.ipynb ├── tutorial93_DL_terminology_scaling.ipynb ├── tutorial94_DL_terminology_Activation.ipynb ├── tutorial95_DL_terminology_loss_functions.ipynb ├── tutorial96_DL_terminology_backpropagation_optimizers.ipynb ├── tutorial97_DL_terminology_batch_size_iterations_epochs.ipynb └── tutorial98_99_100_DL_terminology_dropout_batch_norm.ipynb /365_Correlation_Analysis_Part_1.ipynb: -------------------------------------------------------------------------------- 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