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add eager API
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‎README.md‎

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@@ -4,7 +4,7 @@ This tutorial was designed for easily diving into TensorFlow, through examples.
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It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...).
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**Update (12/12/2017):** TensorFlow v1.4 is recommended. Added many new examples (kmeans, random forest, multi-gpu training, layers api, estimator api, dataset api ...).
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**Update (03/18/2018):** TensorFlow's Eager API examples available! (TF v1.5+ recommended).
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*If you are using older TensorFlow version (0.11 and under), please have a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).*
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#### 1 - Introduction
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- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow.
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- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations.
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- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API.
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#### 2 - Basic Models
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- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow.
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- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API.
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- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow.
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- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API.
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- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow.
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- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow.
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- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow.
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- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation.
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- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset.
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- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset.
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- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation.
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- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset.
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- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset.

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