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Updating large and possibly responsively designed sites can be a hassle. You never know whether your change breakes anything on the other end of your sitemap, or in a certain resolution, except if have a look at every individual page...in every resolution you care about.The idea of review is not to test everything (and visual testing is hard) but rather use the human mind's excellent ability to quickly scan information and filter out what you need, i.e. what is broken.

review regression visual phantomjs testing testThis is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

tensorflow machine-learning regression convolutional-neural-networks logistic-regression book reinforcement-learning autoencoder linear-regression classification clusteringAiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

fp-growth apriori mahchine-leaning naivebayes svm adaboost kmeans svd pca logistic regression recommendedsystem sklearn scikit-learn nlp deeplearning dnn lstm rnnThis repo contains the implementation of YOLOv2 in Keras with Tensorflow backend. It supports training YOLOv2 network with various backends such as MobileNet and InceptionV3. Links to demo applications are shown below. Check out https://experiencor.github.io/yolo_demo/demo.html for a Raccoon Detector demo run entirely in brower with DeepLearn.js and MobileNet backend (it somehow breaks in Window). Source code of this demo is located at https://git.io/vF7vG.

convolutional-networks deep-learning yolo2 realtime regressionPlease cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.

machine-learning data-science tuning cran r-package predictive-modeling classification regression statistics r survival-analysis imbalance-correction tutorial mlr learners hyperparameters-optimization feature-selection multilabel-classification clustering stackingThis 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 odeIn these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

neural-network pytorch-tutorial batch-normalization cnn rnn autoencoder pytorch regression classification batch tutorial dropout dqn reinforcement-learning gan generative-adversarial-network machine-learningIn these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

tensorflow tensorflow-tutorials gan generative-adversarial-network rnn cnn classification regression autoencoder deep-q-network dqn machine-learning tutorial dropout neural-networkA JavaScript implementation of descriptive, regression, and inference statistics. Implemented in literate JavaScript with no dependencies, designed to work in all modern browsers (including IE) as well as in node.js.

statistics regression math descriptive linear probabilitymlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. In addition to its powerful C++ interface, mlpack also provides command-line programs and Python bindings.

machine-learning-library c-plus-plus deep-learning nearest-neighbor-search regression machine-learning"Natural" is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.

natural language porter lancaster stemmer bayes classifier phonetic metaphone inflector wordnet tf-idf logistic regression doublemetaphone double jaro-winkler levenshtein distance taggerThe only library you need to integrate mathematical functions into your applications. It is a self-contained library in pure PHP with no external dependencies. Composer will install MathPHP inside your vendor folder. Then you can add the following to your .php files to use the library with Autoloading.

finance statistics algebra math information-theory vector matrix linear-algebra probability mathematics regression combinatorics number-theory numerical-analysis distributionsNew to MLJ? Start here. Wanting to integrate an existing machine learning model into the MLJ framework? Start here.

data-science machine-learning statistics pipeline clustering julia pipelines regression tuning classification ensemble-learning predictive-modeling tuning-parameters stackingA Machine Learning library for Java.

nlp machine-learning deep-learning clustering ml regression classificationOwl is an emerging numerical library for scientific computing and engineering. The library is developed in the OCaml language and inherits all its powerful features such as static type checking, powerful module system, and superior runtime efficiency. Owl allows you to write succinct type-safe numerical applications in functional language without sacrificing performance, significantly reduces the cost from prototype to production use. Owl's documentation contains a lot of learning materials to help you start. The full documentation consists of two parts: Tutorial Book and API Reference. Both are perfectly synchronised with the code in the repository by the automatic building system. You can access both parts with the following link.

matrix linear-algebra ndarray statistical-functions topic-modeling regression maths gsl plotting sparse-linear-systems scientific-computing numerical-calculations statistics mcmc optimization autograd algorithmic-differentation automatic-differentiation machine-learning neural-networkPractice and tutorial-style notebooks covering wide variety of machine learning techniques

numpy statistics pandas matplotlib regression scikit-learn classification principal-component-analysis clustering decision-trees random-forest dimensionality-reduction neural-network deep-learning artificial-intelligence data-science machine-learning k-nearest-neighbours naive-bayesregression-js is a JavaScript module containing a collection of linear least-squares fitting methods for simple data analysis. This module works on node and in the browser. It is available as the 'regression' package on npm. It is also available on a CDN.

regression data fiting modeling analysisOrbit is a Python package for Bayesian time series forecasting and inference. It provides a familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.

time-series orbit regression forecast forecasting probabilistic-programming bayesian stan arima probabilistic pyro changepoint pystan exponential-smoothingThe datamining Support Vector Machine (SVM) plug-in in MS SQL Server Analysis Services 2008. This plug-in is the SVM classification algorithm in addition to the shipped data mining algorithms with SQL Server.

analysis-services classification classifier data-mining datamining regression smoThe mission of ThunderSVM is to help users easily and efficiently apply SVMs to solve problems. ThunderSVM exploits GPUs and multi-core CPUs to achieve high efficiency. It supports all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs. It can use same command line options as LibSVM. It supports Python, R and Matlab interfaces.

svm classification regression one-class-learning parallelism cuda support-vector-machine
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