Multi-class metrics for Tensorflow
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Updated
Sep 20, 2022 - Python
Multi-class metrics for Tensorflow
Distributed Deep Learning Framework on Ray, including tensorflow/pytorch/mxnet
Gradient accumulation on tf.estimator
Weakly supervised 3D classification of multi-disease chest CT scans using multi-resolution deep segmentation features via dual-stage CNN architecture (DenseVNet, 3D Residual U-Net).
Fully supervised, healthy/malignant prostate detection in multi-parametric MRI (T2W, DWI, ADC), using a modified 2D RetinaNet model for medical object detection, built upon a shallow SEResNet backbone.
Fully supervised, multi-class 3D brain segmentation in T1 MRI using an ensemble of diverse CNN architectures (3D FCN, 3D U-Net) with multi-scale input.
ResNet for CIFAR with Estimator API and tf.keras.Model class
Scripts to practice the basics of TF and Keras while building networks for image classification (CIFAR, MNIST).
Train, predict, export and reload a tf.estimator for inference
Tensorflow estimator implementation of the C3D network
OpenAI Glow implementation for TPU/GPU
Try to use tf.estimator and tf.data together to train a cnn model.
TensorFlow practice using the higher-level APIs
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