222_working_with_large_data_that_does_not_fit_memory_semantic_segm
228_semantic_segmentation_of_aerial_imagery_using_unet
229_smooth_predictions_by_blending_patches
230_landcover_dataset_segmentation
231_234_BraTa2020_Unet_segmentation
235-236_pre-training_unet_using_autoencoders
237_tflite_using_malaria_binary_classification
238_face_eye_detection_using_opencv
239_train_emotion_detection
240_train_age_gender_detection
241_live_age_gender_emotion_detection
242 - Real time detection of facial emotion, age, and gender using TensorFlow Lite
243 - Real time detection of facial emotion, age, and gender using TensorFlow Lite on RaspberryPi
244-what_are_embedding_layers
245 - Advantages of keras functional API in defining complex models
245_adv_of_keras_functional_API_in_defining_complex_models.py
246 - Training a keras model by enumerating epochs and batches
248_keras_implementation_of_GAN
249_keras_implementation-of_conditional_GAN
251_satellite_image_to_maps_translation
252_generating_realistic_scientific_images_using_pix2pix
253_254_cycleGAN_monet2photo
257 - Exploring GAN latent space to generate images with desired features
260_image_anomaly_detection_using_autoencoders
261_global_average_pooling
262_localizing_anomalies_in_images
263_Object localization in images_using_GAP_layer
264 - Image outlier detection using alibi-detect
265_feature_engineering_or_deep_learning
266_openslide_for_whole_slide_images
267_processing_whole_slide_images
268-How to deploy your trained machine learning model into a local web application
269_How to deploy your trained machine learning model as an app on Heroku
270-How to deploy your trained machine learning model as an app on Heroku-HAM10000-no docker
271-How to deploy your trained machine learning model as an app on Heroku-HAM10000-with docker
272-Instance segmentation via semantic segmentation by using border class
274-object_segmentation_using_voronoi_otsu
275-object_segmentation_and_analysis_using_voronoi_otsu_labeling
276 - Grain segmentation using less than 10 lines of code in python
277-3D_segmentation_using_voronoi-otsu
278 - IHC color separation followed by nuclei segmentation using python
285-Object detection using Mask R CNN with XML annotated data
286-Object detection using mask RCNN - end to end
290-Deep Learning based edge detection using HED
291-Object segmentation using Deep Learning based edge detection (HED)
299-Evaluating sklearn model using KFold cross validation in python
300-Picking the best model and corresponding hyperparameters using Gridsearch
301-Evaluating keras model using KFold cross validation
302-Tuning deep learning hyperparameters
303 - Reinhard color transformation
304 - Augmentation of histology images
307 - Segment your images in python without training
310-Understanding sub-word tokenization used for NLP
323-Train a chatbot on your own documents
324-Chat-based data analysis-pandasAI
332 - All about image annotations
335 - Converting COCO JSON annotations to labeled mask images
336-Nuclei-Instance-Detectron2.0_YOLOv8_code
337 - Whole Slide Image segmentation for nuclei using Detectron2 and YOLOv8
Prompt Engineering for Marketers - Tips Tricks 55
Tips_Tricks_45_white-balance_using_python
tips_tricks_36_pyscript_python_in_the_browser
tips_tricks_46-Feature engineering vs feature learning
tips_tricks_52_Hidden Gems - defaultdict
00_A review of COVID19 situation in India using Python.py
017-Reading_Images_in_Python.py
018-image_processing_in_pillow.py
019-image_processing_in_scipy.py
020-image_processing_in_scikit-image.py
021-scratch_assay_using_scikit_image.py
023-histogram_segmentation_using_scikit_image.py
024-random_walker_segmentation_scikit-image.py
025-image_processing_in_openCV_intro1-preprocessing.py
026-image_processing_in_openCV_intro1-preprocessing.py
027-image_processing_in_openCV_intro2-Thresholding.py
028-image_processing_in_openCV_intro2-Thresholding.py
029-keypoint detectors and descriptors in opencv.py
02_Tips_Tricks_python_tips_and_tricks_2_google_image_download.py
030-keypoints_homography_for_registration in opencv.py
031-reading_and_writing_csv.py
032-grain_analysis_saving_to_csv.py
033-grain_size_analysis_using_wateshed_segmentation.py
034a-grain_size_analysis_using_wateshed_segmentation_multiple_files.py
034b-grain_size_analysis_using_wateshed_segmentation_multiple_files_functions.py
035-Cell Nuclei analysis using watershed.py
036-data_analysis_using_Pandas_Intro_data_loading.py
037-data_analysis_using_Pandas_data_handling.py
038-data_sorting_using_Pandas.py
039-data_grouping_using_Pandas.py
040-dealing with null data_using_Pandas.py
041_data_analysis_using_Pandas_Plotting.py
042_data_analysis_using_Seaborn_Plotting.py
045-linear_regression_cells.py
046-linear_regression_cells_train_test.py
047-multi_linear_regression.py
049-Logistic_regression.py
051-Kmeans_using_opencv.py
052-GMM_image_segmentation.py
053-How to pick optimal number of parameters.py
055-ML_06_01_how to open proprietary images.py
057-ML_06_02_what are features.py
058-ML_06_03_what is gabor filter.py
061-Gabor_Filter_Banks.py
062-066-ML_06_04_TRAIN_ML_segmentation_All_filters_RForest.py
067-ML_06_05_PREDICT_ML_segmentation_All_filters_RForest.py
068b-ML_06_04_TRAIN_ML_segmentation_All_filters_RForest_SVM.py
069b-Validate_BOVW_V1.0.py
071-Malaria_cell_CNN_V5.0_for video.py
074-Defining U-net in Python using Keras.py
076-077-078-Unet_nuclei_tutorial.py
085-auto_encode_single_image_V3.0.py
086--auto_denoise_mnist.py
087-auto_denoise_custom_file_V3.0.py
088-autoencoder_anomaly_V0.1.py
089a-auto_encode_single_image_to_different_image_V1.0.py
089b-auto_encode_single_image_to_different_image_multi_file_V1.0.py
090a-autoencoder_colorize_V0.2.py
090b-autoencoder_colorize_V0.1_predict.py
091_intro_to_transfer_learning_VGG16.py
092-autoencoder_colorize_transfer_learning_VGG16_V0.1.py
093_no_need_for_deep_learning.py
095_what_is_convolution.py
096_What is Gaussian denoising.py
097_What is Median denoising.py
098_What is Bilateral denoising.py
100_What is Total Variation denoising.py
102-What is Unsharp Mask.py
105_what_is_fourier_transform.py
106_image_filters_using_fourier_transform_DFT.py
107_analysis_of_covid19_data_using_python_part1.py
108_analysis_of_covid19_data_using_python_part2.py
109_.predicting_covid19_cases_using_pythonpy.py
110_covid19_visualization_using_plotly.py
111_top_10_countries_with_highest_cases_deaths.py
112_denoising_images_by_dct_averaging.py
113-what_is_histogram_equalization.py
114_auto_image_quality_assessment_BRISQUE.py
115_auto_segmentation_using_multiotsu.py
116_.measuring_properties_of_labeled_objects_in_imagespy.py
117_shading_correction_using_rolling_ball.py
118_object_detection_by_template_matching.py
119_sub_pixel_image_registration.py
120_img_registration_methods_in_python.py
121_image_registration_using_pystackreg.py
122_normalizing_HnE_images.py
123-reference_based_image_quality.py
124-evaluate_sharpness_of_image.py
125_126_GAN_predict_mnist.py
125_126_GAN_training_mnist.py
127_data_augmentation_using_keras.py
128_Malaria_cell_classification_CNN_with_data_aug.py
129_130_131-tips_tricks_callbacks_continuing_training.py
135_model_compile_metrics.py
136_understanding_batch_size.py
138_scaling_and_normalization_cifar_working.py
139-topology_of_neural_networks.py
141-regression_housing_example.py
142-multi_label_classification.py
144_145_binary_classification_ROC_AUC.py
148_imbalanced_data_DeepLearning.py
148_imbalanced_data_RandomForest.py
149-imbalanced_data_liver.py
150_151_custom_data_augmentation.py
150_151_data_augmentation_using_keras_images_and_masks_tutorial.py
152-visualizing_conv_layer_outputs.py
153-multi_linear_regression.py
154_understanding_train_validation_loss_curves.py
156_defining_GPU_memory_usage_for_deep_learning.py
157_understanding_tensorboard.py
158_classification_CNN_RF.py
158b_transfer_learning_using_CNN_weights_VGG16_RF.py
159_CNN_features_RF_sandstone.py
159b_VGG16_imagenet_weights_RF_for_semantic.py
160_when_to_retrain_your_ML_model.py
162-Intro_to_time_series_exploring_dataset_using_python.py
163-Intro_to_time_series_Forecasting_using_ARIMA.py
164a-Intro_to_time_series_Forecasting_using_feed_forward_NN.py
164b-Intro_to_time_series_Forecasting_using_feed_forward_NN_and_TimeseriesGenerator.py
166a-Intro_to_time_series_Forecasting_using_LSTM.py
166b-Intro_to_time_series_Forecasting_using_LSTM_and_TimeseriesGenerator.py
166b_COVID_forecasting_using_LSTM.py
167-LSTM_text_generation_ENGLISH.py
168-LSTM_text_generation_TELUGU_V2.0.py
169_installing_autokeras_and_testing_mnist.py
170-breast_cancer_classification_with_AutoKeras.py
171-multiclass_cifar_with_autokeras.py
173_IOU_VGG16_imagenet_weights_RF_for_semantic.py
175-breast_cancer_without_PCA.py
176-multiclass_using_VGG_weights_PCA_NN_RF.py
177_COLAB_semantic_segmentation_made_easy_using_segm_models.ipynb
177_albumentations_aug.py
177_semantic_segmentation_made_easy_using_segm_models.py
178_179_variational_autoencoders_mnist.py
180_LSTM_encoder_decoder_anomaly_GE.py
181_multivariate_timeseries_LSTM_GE.py
182_batch_processing_multiple_images_in_python.py
183_OCR_in_python_using_keras-ocr.py
184-scheduling_learning_rate_in_keras.py
185-187-gridsearch_hyperparam_tuning_lr_momentum.py
188-gridsearch_hyperparam_tuning_activation_opt_weights.py
189-gridsearch_hyperparam_tuning_dropout_wt_constr_hidden_layer_neurons.py
190-gridsearch_hyperparam_tuning_RF_SVM_MNIST.py
191_measure_img_similarity.py
192_working_with_3d_images.py
193_xgboost_intro_using_wisconsin_dataset.py
194_xgboost_for_semantic_using_VGG_features.py
195_xgboost_for_image_classification_using_VGG16.py
196_lightGBM_feature_selection_breast_cancer.py
197_lgbm_vs_xgboost_for_semantic_using_VGG_features.py
198_Boruta_feature_selection_breast_cancer.py
200_image_classification_using_GLCM.py
201_geotiff_using_rasterio.py
202_2_ways_to_load_HAM10000_data.py
203a_skin_cancer_lesion_classification_V4.0_autokeras.py
203b_skin_cancer_lesion_classification_V4.0.py
204-207simple_unet_model.py
204_train_simple_unet_for_mitochondria.py
205_predict_unet_with_watershed_single_image.py
206_sem_segm_large_images_using_unet_not_recommended.py
206_sem_segm_large_images_using_unet_with_custom_patch_inference.py
206_sem_segm_large_images_using_unet_with_patchify.py
207-simple_unet_model_with_jacard.py
207_train_simple_unet_for_mitochondria_using_Jacard.py
208-simple_multi_unet_model.py
208_multiclass_Unet_sandstone.py
209_predict_full_volume_sandstone.py
210_multiclass_Unet_using_VGG_resnet_inception.py
211_multiclass_Unet_vs_linknet.py
213-ensemble_sign_language.py
214_multiclass_Unet_sandstone_segm_models_ensemble.py
216_mito_unet_12_training_images_V1.0.py
216_mito_unet__xferlearn_12_training_images.py
218_difference_between_Upsampling_and_Conv2DTranspose.py
219-unet_model_with_functions_of_blocks.py
219_unet_small_dataset_using_functional_blocks.py
221_split_folder_into_train_test_val.py
223_test_time_augmentation_for_semantic_segmentation.py
224_225_226_mito_segm_using_various_unet_models.py
227_mito_segm_using_models_from_Keras_Unet_collection.py
279_An_introduction_to_object_segmentation_using_StarDist.ipynb
280a_custom_object_segmentation_using_stardist_TRAIN.ipynb
280b_custom_object_segmentation_using_stardist_PREDICT.ipynb
281_Segmenting_WSI_using_StarDist.ipynb
282_IHC_separation_followed_by_StarDist_segmentation.ipynb
287_tracking_particles_using_trackpy.ipynb
288_nuclei_tracking_trackpy_stardist.ipynb
289_tracking_particles_in_3D.ipynb
293_denoising_RGB_images_using_deep learning.ipynb
294_n2v_3D_multi_ch_czi.ipynb
296-Converting keras-trained model to ONNX format-Img Classification.py
297-Converting keras-trained model to ONNX-Sem Segm.py
305_What_is_Cellpose_algorithm_for_segmentation.ipynb
309_Training_your_own_Chatbot_using_GPT.ipynb
311_fine_tuning_GPT2.ipynb
313_GeneticAlgorithm_Camouflage.ipynb
314_How_to_code_the_genetic_algorithm_in_python.ipynb
315_Optimization_using_Genetic_Algorithm_Heart_disease.ipynb
316_Optimizing_Steel_Strength_using_Metaheuristic_algo.ipynb
317_HyperParameter_Optimization_using_Genetic_algo.ipynb
319_what_is_simulated_annealing.ipynb
320_Optimizing_Steel_Strength_using_simulated_annealing.ipynb
321_what_is_particle_swarm_optimization.ipynb
322_Optimizing_Steel_Strength_using_PSO.ipynb
325_Transcriptomics_Unveiled.ipynb
326_Cell_type_annotation_for_single_cell_RNA_seq_data.ipynb
328a_smFISH_analysis_using_Big_FISH_singleplex.ipynb
328b_smFISH_analysis_using_Big_FISH_multiplex.ipynb
329_Detectron2_intro.ipynb
330_Detectron2_Instance_3D_EM_Platelet.ipynb
331_fine_tune_SAM_mito.ipynb
333_Intro_to_YOLO_V8.ipynb
334_training_YOLO_V8_EM_platelets_converted_labels.ipynb
339_surrogate_optimization.ipynb
67b_Feature_based_segm_RF_multi_image_TRAIN.py
Book_review_fastai_cookbook.ipynb
Tips_Tricks_10_loading_images_and_masks_in_order_for_sem_segm.ipynb
Tips_Tricks_13_how_to_visualize_keras_models_on_windows10.py
Tips_Tricks_14_easyocr_to_detect_text_in_images.py
Tips_Tricks_23_COVID_vaccine_analysis.ipynb
Tips_Tricks_24_quick_intro_to_pyviz.ipynb
Tips_Tricks_25_locating_objects_in_large_images_via_template_matching.py
Tips_Tricks_26_proper-way_to_convert_16bit_to_8bit_image.py
Tips_Tricks_42_How to remove text from images.py
Tips_Tricks_54_Exploring_metadata_in_scientific_images.py
Tips_Tricks_56_CPU_vs_GPU_performance_test.ipynb
Tips_Tricks_5_extracting_patches_from_large_images_and_masks_for_semantic_segm.py
Tips_Tricks_6_fixing_generic_utils_bug_in_segm_models_library.py
Tips_and_Tricks_50_interpolate_images_in_a_stack.ipynb
Tips_tricks_15_understanding_binary_crossentropy.py
Tips_tricks_16_How much memory is required_to_train_a_DL_model.py
Tips_tricks_17_all_you_need_to_know_about_decorators.py
Tips_tricks_20_Understanding transfer learning for different size and channel inputs.py
Tips_tricks_22_fastai_lung_cancer_classification.ipynb
Tips_tricks_27_labeling_images_for_sem_segm_using_label_studio.py
Tips_tricks_35_loading_kaggle_data_to_colab.ipynb
generate_digital_holiday_card.py
tips_tricks_30_random_is_not_random.py
tips_tricks_31_generating_borders_around_objects.py
tips_tricks_32_automate_periodic_mouse_movements.py
tips_tricks_37_Understanding MAE and MSE.py
tips_tricks_38_Installing_conda_in_Google_Colab.ipynb
tips_tricks_3_data_augmentation.ipynb
tips_tricks_44_underscores_in_python.ipynb
tips_tricks_48_overlay_image_comparison.ipynb
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