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Copy file name to clipboardExpand all lines: sagemaker-python-sdk/README.md
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These examples focus on the Amazon SageMaker Python SDK which allows you to write idiomatic TensorFlow or MXNet and then train or host in pre-built containers.
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-[cifar 10 with MXNet Gluon](mxnet_gluon_cifar10)
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-[MNIST with MXNet Gluon](mxnet_gluon_mnist)
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-[MNIST with MXNet](mxnet_mnist)
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-[TensorFlow Neural Networks with Layers](tensorflow_abalone_age_predictor_using_layers)
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-[TensorFlow Networks with Keras](tensorflow_abalone_age_predictor_keras)
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-[Introduction to Estimators in TensorFlow](tensorflow_iris_dnn_classifier_using_estimators)
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-[TensorFlow and TensorBoard](tensorflow_resnet_cifar10_with_tensorboard)
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-[cifar 10 with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_cifar10)
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-[MNIST with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_mnist)
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-[MNIST with MXNet](sagemaker-python-sdk/mxnet_mnist)
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-[Sentiment Analysis with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_sentiment)
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-[TensorFlow Neural Networks with Layers](sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_layers)
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-[TensorFlow Networks with Keras](sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras)
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-[Introduction to Estimators in TensorFlow](sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators)
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-[TensorFlow and TensorBoard](sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard)
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