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The NGC catalog offers step-by-step instructions and scripts for creating deep learning models, with sample performance and accuracy metrics to compare your results. These scripts provide expert guidance on building DL models for image classification, language translation, text-to-speech and more. Data scientists can quickly build performance-optimized models by easily adjusting the hyperparameters.
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Text to Speech Notebook
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End to End workflow for text to speech training with TAO Toolkit and deployment using Riva.
Nvidia Clara Parabricks Bare Metal Debian Package
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Nvidia Clara Parabricks Bare Metal Debian Package for Ubuntu 18.04
AzureML Quick Launch: Clara COVID-19 CT Scan Classifier
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Download pre-configured setup files to launch and fine-tune the Clara Train COVID-19 CT Scan Classification model on AzureML using the NGC-AzureML Quick Launch Toolkit
DOCA Container Resources
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Set of container configuration files for the various DOCA containers.
TensorFlow Image Classification Example
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Example Jupyter Notebook using the NVIDIA TensorFlow Container and the Fashion-MNIST data set
Riva Skills Quick Start
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Scripts and utilities for getting started with Riva Speech Skills
TAO Toolkit Computer Vision Sample Workflows
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Sample notebooks for TAO Toolkit Computer Vision workflows.
Speech to Text Citrinet Notebook
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End to End workflow for speech to text citrinet training with TAO Toolkit and deployment using Riva.
Question Answering Notebook
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End to End workflow for question answering starting with training in TAO Toolkit and deployment using Riva.
Punctuation Capitalization Notebook
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End to End workflow for punctuation and capitalization starting with training in TAO Toolkit and deployment using Riva.
Text Classification Notebook
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End to End sample workflow for Text Classification starting with training in TAO Toolkit and deployment using Riva.
Token Classification Notebook
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End to End sample workflow for Token Classification starting with training in TAO Toolkit and deployment using Riva.
Speech to Text Notebook
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End to End workflow for speech to text training with TAO Toolkit and deployment using Riva.
Intent Slot Classification Notebook
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End to End sample workflow for Intent Slot Classification starting with training in TAO Toolkit and deployment using Riva.
EfficientNet For Tensorflow2
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EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, being an order-of-magnitude smaller and faster.
nnU-Net for PyTorch
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An optimized, robust and self-adapting framework for U-Net based medical image segmentation
V-Net Medical for TensorFlow
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V-Net is a convolutional neural network for 3D image segmentation.
UNet Medical for TensorFlow2
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U-Net allows for seamless segmentation of 2D images, with high accuracy and performance.
UNet Medical for TensorFlow
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U-Net allows for seamless segmentation of 2D images, with high accuracy and performance.
UNet Industrial for TensorFlow
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This model is a convolutional neural network for 2D image segmentation tuned to avoid overfitting.
Transformer for PyTorch
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This implementation of Transformer model architecture is based on the optimized implementation in Fairseq NLP toolkit.
Mask R-CNN for PyTorch
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Mask R-CNN is a convolution based network for object instance segmentation. This implementation provides 1.3x faster training while maintaining target accuracy.
NCF for TensorFlow
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The NCF model focuses on providing recommendations. This is a modified implementation with improved overfitting and better accuracy.
Jasper for PyTorch
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The Jasper model is an end-to-end neural acoustic model for automatic speech recognition (ASR).
GNMT v2 for PyTorch
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The GNMT v2 model is an improved version of the first Google's Neural Machine Translation System with a modified attention mechanism.