| @@ -128,18 +128,18 @@ Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflow | |||||
| * [Hello World](test/TensorFlowNET.Examples/HelloWorld.cs) | * [Hello World](test/TensorFlowNET.Examples/HelloWorld.cs) | ||||
| * [Basic Operations](test/TensorFlowNET.Examples/BasicOperations.cs) | * [Basic Operations](test/TensorFlowNET.Examples/BasicOperations.cs) | ||||
| * [Linear Regression](test/TensorFlowNET.Examples/LinearRegression.cs) | |||||
| * [Logistic Regression](test/TensorFlowNET.Examples/LogisticRegression.cs) | |||||
| * [Nearest Neighbor](test/TensorFlowNET.Examples/NearestNeighbor.cs) | |||||
| * [Naive Bayes Classification](test/TensorFlowNET.Examples/NaiveBayesClassifier.cs) | |||||
| * [Image Recognition](test/TensorFlowNET.Examples/ImageRecognition.cs) | |||||
| * [K-means Clustering](test/TensorFlowNET.Examples/KMeansClustering.cs) | |||||
| * [NN XOR](test/TensorFlowNET.Examples/NeuralNetXor.cs) | |||||
| * [Object Detection](test/TensorFlowNET.Examples/ObjectDetection.cs) | |||||
| * [Text Classification](test/TensorFlowNET.Examples/TextClassificationWithMovieReviews.cs) | |||||
| * [CNN Text Classification](test/TensorFlowNET.Examples/CnnTextClassification.cs) | |||||
| * [Named Entity Recognition](test/TensorFlowNET.Examples/NamedEntityRecognition.cs) | |||||
| * [Linear Regression](test/TensorFlowNET.Examples/BasicModels/LinearRegression.cs) | |||||
| * [Logistic Regression](test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs) | |||||
| * [Nearest Neighbor](test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs) | |||||
| * [Naive Bayes Classification](test/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs) | |||||
| * [Image Recognition](test/TensorFlowNET.Examples/ImageProcess) | |||||
| * [K-means Clustering](test/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs) | |||||
| * [NN XOR](test/TensorFlowNET.Examples/BasicModels/NeuralNetXor.cs) | |||||
| * [Object Detection](test/TensorFlowNET.Examples/ImageProcess/ObjectDetection.cs) | |||||
| * [Text Classification](test/TensorFlowNET.Examples/TextProcess/BinaryTextClassification.cs) | |||||
| * [CNN Text Classification](test/TensorFlowNET.Examples/TextProcess/cnn_models/VdCnn.cs) | |||||
| * [Named Entity Recognition](test/TensorFlowNET.Examples/TextProcess/NER) | |||||
| ### Contribute: | ### Contribute: | ||||
| @@ -133,4 +133,4 @@ grace_hopper.jpg: 466 bulletproof vest, 0.005350832 | |||||
| 2/18/2019 3:56:25 AM Completed InceptionArchGoogLeNet | 2/18/2019 3:56:25 AM Completed InceptionArchGoogLeNet | ||||
| ``` | ``` | ||||
| You can find the full source code from [github](https://github.com/SciSharp/TensorFlow.NET/tree/master/test/TensorFlowNET.Examples). | |||||
| You can find the full source code from [github](https://github.com/SciSharp/TensorFlow.NET/tree/master/test/TensorFlowNET.Examples/ImageProcess). | |||||
| @@ -87,4 +87,4 @@ When we visualize the graph in TensorBoard: | |||||
|  |  | ||||
| The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/LinearRegression.cs). | |||||
| The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/BasicModels/LinearRegression.cs). | |||||
| @@ -13,4 +13,4 @@ The dependent variable of logistics regression can be two-category or multi-cate | |||||
| Softmax regression allows us to handle  where K is the number of classes. | Softmax regression allows us to handle  where K is the number of classes. | ||||
| The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/LogisticRegression.cs). | |||||
| The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs). | |||||
| @@ -1,3 +1,5 @@ | |||||
| # Chapter. Nearest Neighbor | # Chapter. Nearest Neighbor | ||||
| The nearest neighbour algorithm was one of the first algorithms used to solve the travelling salesman problem. In it, the salesman starts at a random city and repeatedly visits the nearest city until all have been visited. It quickly yields a short tour, but usually not the optimal one. | |||||
| The nearest neighbour algorithm was one of the first algorithms used to solve the travelling salesman problem. In it, the salesman starts at a random city and repeatedly visits the nearest city until all have been visited. It quickly yields a short tour, but usually not the optimal one. | |||||
| The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs). | |||||