Hybrid Quantum-Classical Convolutional Neural Network to classify esophagus cancer images
Data Challenge: Data Challenge by Mauna Kea (Challenge Link: https://challengedata.ens.fr/participants/challenges/11/)
1.Hybrid quantum-classical Neural Networks with PyTorch and Qiskit: https://qiskit.org/textbook/ch-machine-learning/machine-learning-qiskit-pytorch.html
2.Torch Connector and Hybrid QNNs: https://qiskit.org/documentation/machine-learning/tutorials/05_torch_connector.html
3.The Quantum Convolution Neural Network: https://qiskit.org/documentation/machine-learning/tutorials/11_quantum_convolutional_neural_networks.html
4.Esophagus Cancer Classifier : https://github.com/AnIsAsPe/ClassificadorCancerEsofago
5.Machine Learning & IA for the Working Analyst - Colegio de Matemáticas Bourbaki - Mexico
- https://www.colegio-bourbaki.com/
- LinkedIn: Colegio Bourbaki
6.Qiskit Slack Channel (qiskit.slack.com) - Special Thanks to Owen Lockwood & Anton Dekusar
7.PyTorch Performance Tuning Guide - Szymon Migacz, NVIDIA - https://www.youtube.com/watch?v=9mS1fIYj1So
8.PyTorch 101, Part 4: Memory Management and Using Multiple GPUs - Ayoosh Kathuria - https://blog.paperspace.com/pytorch-memory-multi-gpu-debugging/amp/
9.AdamW and Super-convergence is now the fastest way to train neural nets - Sylvain Gugger and Jeremy Howard - https://www.fast.ai/posts/2018-07-02-adam-weight-decay.html#appendix-full-results
10.Decoupled Weight Decay Regularization - Ilya Loshchilov & Frank Hutter - arXiv:1711.05101v3
11.Improved Regularization of Convolutional Neural Networks with Cutout - Terrance DeVries and Graham W. Taylor - arXiv:1708.04552