ktrain: A Low-Code Library for Augmented Machine Learning
원문 초록
We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply. As a wrapper to TensorFlow and many other libraries (e.g., transformers, scikit-learn, stellargraph), it is designed to make sophisticated, state-of-the-art machine learning models simple to build, train, inspect, and apply by both beginners and experienced practitioners. Featuring modules that support text data (e.g., text classification, sequence tagging, open-domain question-answering), vision data (e.g., image classification), graph data (e.g., node classification, link prediction), and tabular data, ktrain presents a simple unified interface enabling one to quickly solve a wide range of tasks in as little as three or four "commands" or lines of code.
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Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Image and Text fusion for UPMC Food-101 \\using BERT and CNNs
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion
Performing Image Classification for 10 Different Monkey Species using CNN
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