custom-model-development

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Ankit-Kumar-Saini / Coursera_TensorFlow_Advanced_Techniques_Specialization

Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.

object-detection image-segmentation generative-adversarial-networks auto-encoders saliency-map mask-rcnn variational-autoencoders coursera-specialization retina-net model-interpretability tensorflow2 functional-api custom-model-development custom-loss-functions gradcam-visualization custom-layers gradient-tape-optimization custom-training-loops

Updated Jun 9, 2021
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powerworld / pwUserDefinedModels

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Solutions for creating a DLL that can be recognized by PowerWorld Transient Stability

dynamic-models power-system-transient-stability custom-model-development user-defined-models powerworld-simulator

Updated Dec 13, 2019
Pascal

kapilbhise / ml-lab

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Machine learning algorithms

text-to-speech neural-network linear-regression machine-learning-algorithms audiobook logistic-regression gradient-descent decision-tree decision-tree-algorithm multivariate-linear-regression candidate-elimination finds decison-trees custom-model-development using-custom-models usa-election-result text-to-speech-gui

Updated Feb 13, 2022
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akashprap / Object-Detector-YOLOv4

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This repository hosts an Object Detector implemented using YOLOv4. Trained to recognize five classes—Person, Bird, Animal, Building, and Tree—it enables accurate and swift object detection and tracking.

python object-detection object-tracking open-images-dataset custom-model-development yolov4 yolov4-darknet