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deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deepmedic

Efficient Multi-Scale 3D Convolutional Neural Network for Segmentation of 3D Medical Scans

distil-bilstm

Scripts to train a bidirectional LSTM with knowledge distillation from BERT

dltk

Deep Learning Toolkit for Medical Image Analysis

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

ideal_profiles

New repo for the Ideal Data Science Profiles Project

inrad

islr-python

knowledge-distillation

knowledge-distillation-pytorch

A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

knowledge-distillation-zoo

Pytorch implementation of various Knowledge Distillation (KD) methods.

knowledge-distilling-pytorch

Implementation of Distilling the Knowledge in a Neural Network https://arxiv.org/pdf/1503.02531.pdf

knowledge_distillation

PyTorch implementation of "Distilling the Knowledge in a Neural Network"

lookup

Code for assignments of the graduate course CS 7643: Deep Learning offered at Georgia Tech in Fall 2018.

lstm-autoencoder-time-series

Time Series embedding using LSTM Autoencoders with PyTorch in Python

lstm_encoder_decoder

Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data

minsizekmeans

A python implementation of KMeans clustering with minimum cluster size constraint (Bradley et al., 2000)

notes

opencl-book-samples

Automatically exported from code.google.com/p/opencl-book-samples

overhaul-distillation

Official PyTorch implementation of "A Comprehensive Overhaul of Feature Distillation" (ICCV 2019)

simpleitk-notebooks

Jupyter notebooks for learning how to use SimpleITK

tool-swat-preprocess

A tool for data preprocess on iTrust SWaT dataset.

torchdistill

A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆20 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

tranad

[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.