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This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

tensorflow tensorflow-cookbook linear-regression neural-network tensorflow-algorithms rnn cnn svm nlp packtpub machine-learning tensorboard classification regression kmeans-clustering genetic-algorithm odePython codes for common Machine Learning Algorithms

linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithmk-means clustering in Ruby. Uses NArray under the hood for fast calculations. Jump to the examples directory to see this in action.

kmeans-clustering clustering rubymlThe program reads in an image, segments it using K-Means clustering and outputs the segmented image. It is worth playing with the number of iterations, low numbers will run quicker.

kmeans-clustering image-segmentationVisualize and interact with the clustering algorithm k-means. Try it at lettier.com/kmeans. Read more about k-means.

interactive-kmeans kmeans kmeans-clustering kmeans-algorithm machine-learning machine-learning-algorithms ai clustering clustering-algorithm cluster-analysis clustering-methods data-science clustering-evaluation scikit-learn clusterTensorbag is a collection of tensorflow tutorial on different Deep Learning and Machine Learning algorithms. The tutorials are organised as jupyter notebooks and require tensorflow >= 1.5. There is a subset of notebooks identified with the tag [quiz] that directly ask to the reader to complete part of the code. In the same folder there is always a complementary notebook with the complete solution.

cifar-10 generative-adversarial-networks mnist lenet-5 convolutional-neural-networks resnet-18 notebook deep-learning tensorflow resnet tfrecord-format cifar-100 tensorflow-tutorials kmeans-clustering perceptron autoencoder tutorial"The idea behind k-Means Clustering is to take a bunch of data and determine if there are any natural clusters (groups of related objects) within the data. The k-Means algorithm is a so-called unsupervised learning algorithm. We don't know in advance what patterns exist in the data -- it has no formal classification to it -- but we would like to see if we can divide the data into groups somehow.

sklearn scikit-learn kmeans-clustering kmeans machine-learningMLKit is a simple machine learning framework written in Swift. Currently MLKit features machine learning algorithms that deal with the topic of regression, but the framework will expand over time with topics such as classification, clustering, recommender systems, and deep learning. The vision and goal of this framework is to provide developers with a toolkit to create products that can learn from data. MLKit is a side project of mine in order to make it easier for developers to implement machine learning algorithms on the go, and to familiarlize myself with machine learning concepts. This project is under active development and is not ready for use in commercial or personal projects.

regression ridge-regression machine-learning machine-learning-algorithms artificial-intelligence polynomial-regression linear-regression machine-learning-library mlkit kmeans-clustering neural-network feedforward-neural-network backpropagation lasso-regression kmeans genetic-algorithm
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