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leaf-audio

licsbas

LiCSBAS: InSAR time series analysis package using LiCSAR products

life-expectancy-regression-analysis-and-classification

I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.

lung_nodule_detection_and_3d_reconstruction

Lung nodule detection using UNET (from CT scan images) & 3D Reconstruction

machine-vision-based-defect-detection-in-welding-process

Implementation of automatic computer-aided identification system to recognize different types of welding defects in radiographic images which includes defect detection and classification using Deep Neural Network

machine_part_defect-detection

Classification of automotive parts as defective and non-defective with transfer learning.

mask-rcnn-training-with-docker-containers-on-sagemaker

Mask R-CNN for metal casting defects detection and instance segmentation using Keras and TensorFlow.

mask_rcnn_concrete

Apply mask rcnn to concrete CT image -- Revealing the Secrets of Ancient Roman Concrete with Image Segmentation

matplotlib-label-lines

Label line using matplotlib.

matseed

The universal deep learning model for transfer leaning to predict physical properties of inorganic materials

metal_defects_detection

Defects dection in SEM images by using advanced computer vision, Mask-RCNN

mirror-glass-detection

🍸 Mirror & Glass Detection in Real-world Scenes

ml_in_ls_studies

:sparkles::bar_chart: :chart_with_upwards_trend::sparkles: Landslide Susceptibility Mapping in Mila Basin, Algeria

ml_pothole

contains dataset and other tools for developing tf model

ml_projrct_concrete_strength_prediction

Perform These algorithms: - Linear Regression - Lasso Regression - Ridge Regression - Decision Tree Regressor - Random Forest Regressor - KNN Regressor - SVM Regressor AND Pick each of the algorithm and perform These steps: o Split your data between train and test steps. Build your model List down the evaluation metrics you would use to evaluate the performance of the model? Evaluate the model on training data o Predict the response variables for the test data How are the two scores? Are they significantly different? Are they the same? Is the test score better than training score?

mmuu-net

MMUU-Net：A Robust and Effective Network for Farmland Segmentation of Satellite Imagery

modeling-of-strength-of-high-performance-concrete-using-machine-learning

monk_object_detection

A one-stop repository for low-code easily-installable object detection pipelines.

monk_object_detection-1

Monk_Object_Detection

mosaic-data-augmentation

Mosaic Data Augmentation in YOLOv4

msc-thesis

Jupyter Notebooks and Python scripts to create data-driven models for landslide susceptibility assesment.

mumbai-slum-segmentation

Mumbai slum segmentation and change detection on statellite images.

mxnet-the-straight-dope

An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at https://d2l.ai/.

natural_disaster_pred

Using CNNs and Sentinel-2 satellite data to predict landslides

nested-transformer

Aggregating Nested Transformer https://arxiv.org/pdf/2105.12723.pdf

neural-structural-optimization

Neural reparameterization improves structural optimization

neural-structural-optimization-study

nevergrad

A Python toolbox for performing gradient-free optimization

np-hysteresis

A python based script to obtain the hysteresis curves of magnetic nanoparticles.

odml-pathways