Tuner Implementation of Parallel Architecture and Hyperparameter Search via Successive Halving and Classification (SHAC)
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Updated
Jun 27, 2018
Tuner Implementation of Parallel Architecture and Hyperparameter Search via Successive Halving and Classification (SHAC)
Implementation of early stopping in tensorflow based on any chosen metric
some scripts using deepchem
Fashion Mnist image classification using cross entropy and Triplet loss
A classification model to detect breast cancer
Deep neural networks to predict Pneumonia using chest xray
ARTIFICIAL NEURAL NETWORKS AND DEEP LEARNING
PyTorch and TensorFlow-Keras Training - 🧠🛠️ Utilizes CIFAR-10 dataset for PyTorch and MNIST for TensorFlow-Keras. Implements early-stopping to prevent overfitting during training. Provides code snippets for early-stopping implementation in both PyTorch and TensorFlow-Keras.
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
Classify an activity by sensor data from gyroscope and accelerometer.
A repository to show how Early Stopping in Keras can Prevent Overfitting
AAAI 2021: Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
Deep Learning Implementations on Acoustic Wave Processing with LSTM Architecture
Features injected recurrent neural networks for short-term traffic speed prediction
Single Layer Perceptrons (SLPs) and Multi-Layer Perceptrons (MLPs) from scratch, only with numpy, for classification and regression. MLPs with Keras for time-series prediction.
A basic introduction to learning CNN through applications of VGG models.
Instructive game aimed to illustrate the concept of Optimal Stopping in Reinforcement Learning.
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