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3dgnn

3dunetcnn

Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

a_guide_to_running_tensorflow_models_on_android

This is the code for"A Guide to Running Tensorflow Models on Android" By SIraj Raval on Youtube

advanced-deep-trading

Mostly experiments based on "Advances in financial machine learning" book

advanced_keras_tutorial

adversarial-deep-structural-networks

ISBI2018: Adversarial Deep Structural Networks for Mammographic Mass Segmentation https://arxiv.org/abs/1612.05970

ai_in_medicine_clinical_imaging_classification

This is the code for "AI in Medicine " By Siraj Raval on Youtube

androidflask

Image Upload from Android to Python-Based Flask Server

androidtensorflowmachinelearningexample

Android TensorFlow MachineLearning Example (Building TensorFlow for Android)

augmentor

Image augmentation library in Python for machine learning.

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

bag_of_tricks_for_image_classification_with_convolutional_neural_networks

experiments on Paper <Bag of Tricks for Image Classification with Convolutional Neural Networks> and other useful tricks to improve CNN acc

banknoteclassifier

Banknote classifier iOS app created with Resnet and CoreML

batchgenerators

A framework for data augmentation for 2D and 3D image classification and segmentation

bicyclegan-tensorflow

A Tensorflow implementation of BicycleGAN.

blog-resources

This repo will contain the resources available in my blog for learning

brats17

Patch-based 3D U-Net for brain tumor segmentation

bulbea

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

cancer-detection-and-segmentation

cancer_nn

ISIC 2017: Skin Lesion Analysis Towards Melanoma Detection (Lesion Classification)

cats-vs-dogs-cnn-using-keras-

The training set consisted of 25,000 images out of which 5,000 images were taken out as validation data. Separate test data folder consisted of 12,500 images for which the labels were predicted using trained model. My work includes preprocessing for model, Data augmentation to prevent overfitting, callbacks in keras to reduce learning rate timely, various CNN architecture trials with different layers and hyperparameters for best fit and learning curve wrt epochs. I gained a validation accuracy of 87.15 % without using any pretrained imagenet models . VGG-16 gave around 89 % as validation accuracy.

cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers

cifar10_tutorial

Copy of tensorflow:models/tutorials/images/cifar10

cnn_finetune

Fine-tune CNN in Keras

convenience_py

A collection of functions I have written for common tasks I perform in Python. Put together into one repository purely for convenience.

convnet-study

convolution-layer-visualization-vgg-16

A GUI Tool for visualizing the intermediate layer of VGG-16 CNN trained on Imagenet

coreml-custom-layers

Source code for the blog post "Custom Layers in Core ML"

coremltools

Converter tools for Core ML.

covid-19-screening-using-ran-on-x-ray-images

covid-19 screening using Residual Attention Network on X-ray images