Node.js Desktop Automation. Control the mouse, keyboard, and read the screen.RobotJS supports Mac, Windows, and Linux.
automation gui mouse keyboard screenshot image pixel desktop robotjs screen recognition autohotkey machine learning colorThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
jvm virtual machineBy its nature, JavaScript lacks the performance to implement Computer Vision tasks efficiently. Therefore this package brings the performance of the native OpenCV library to your Node.js application. This project targets OpenCV 3 and provides an asynchronous as well as an synchronous API. The ultimate goal of this project is to provide a comprehensive collection of Node.js bindings to the API of OpenCV and the OpenCV-contrib modules. An overview of available bindings can be found in the API Documentation. Furthermore, contribution is highly appreciated. If you want to get involved you can have a look at the contribution guide.
nodejs opencv face-detection async node cv typescript computer-vision face detection recognition machine learning neural networkLudwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data.
deep-learning deeplearning deep-neural-networks deep learning machine-learning machinelearning machine natural-language-processing natural-language-understanding natural-language natural-language-generation computer-vision python3This library is a compilation of the tools developed in the mljs organization. It is mainly maintained for use in the browser. If you are working with Node.js, you might prefer to add to your dependencies only the libraries that you need, as they are usually published to npm more often. We prefix all our npm package names with ml- (eg. ml-matrix) so they are easy to find. It will be available as the global ML variable. The package is in UMD format and can be "required" within webpack or requireJS.
machine-learning ml machine learning data mining dataminingMIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Printing seems to work best printing directly from the browser, using Chrome. Other browsers do not work as well.
learning pdf machine-learning good mit deep-learning neural-network book machine linear-algebra neural-networks deeplearning print excercises lecture-notes chapter clear thinking printableStately.js is a JavaScript based finite-state machine (FSM) engine for Node.js and the browser. Both will return a new stateMachine object, with all events from all states attached to it. The machine will transition into the initial state initialStateName or the first attached stateObject if initialStateName is omitted. In addition to the events the stateMachine object has a getMachineState() method, returning the current name of the machines state, getMachineEvents(), returning possible events in the current state.
state-machine transition fsm automata finite machine stateYou want your framework listed here? Check out the Emitters section and learn how to integrate it with Kuker. If you build software you probably know that debugging what you just wrote is really important. Without seeing how your code works on a lower level you can't say that something is done. Finding and fixing bugs is also important. And without a proper tool it becomes difficult and time consuming. Kuker is here to help by improving your workflow.
stent state machine dev tools chromeI will update your MacOS machine with Better™ system defaults, preferences, software configuration and even auto-install some handy development tools and apps that my developer friends find helpful. You don't need to install or configure anything upfront! This works with a brand-new machine from the factory as well as an existing machine that you've been working with for years.
dotfiles configuration developer-tools machine iterm2 osx automation setup developer iterm commandline bootstrapRecent advances in deep neural networks have enabled algorithms to compose music that is comparable to music composed by humans. However, few algorithms allow the user to generate music with tunable parameters. The ability to tune properties of generated music will yield more practical benefits for aiding artists, filmmakers, and composers in their creative tasks. In this paper, we introduce DeepJ - an end-to-end generative model that is capable of composing music conditioned on a specific mixture of composer styles. Our innovations include methods to learn musical style and music dynamics. We use our model to demonstrate a simple technique for controlling the style of generated music as a proof of concept. Evaluation of our model using human raters shows that we have improved over the Biaxial LSTM approach. Clone Python MIDI (https://github.com/vishnubob/python-midi) cd python-midi then install using python3 setup.py install.
deep learning machine music composition generation keras tensorflowThis is a generative art project I made for my high school's programming club - which I'm the president/founder of I was the president/founder of until I graduated the other month. It's a neural network that has been trained on Kanye West's discography, and can use any lyrics you feed it and write a new song word by word that rhymes and has a flow (to an extent).
neural-network lyrics songs rhymes machine learning rap rap-songs generative-art songwriting mp3Stent is combining the ideas of Redux with the concept of state machines. State machine is a mathematical model of computation. It's an abstract concept where the machine may have different states but at a given time fulfills only one of them. It accepts input and based on that (plus its current state) transitions to another state. Isn't it familiar? Yes, it sounds like a front-end application. That's why this model/concept applies nicely to UI development.
state machine finite mealy-machine stent state-machine mealy redux react connect transitionsMoviebox is a content based machine learning recommending system build with the powers of tf-idf and cosine similarities. Initially, a natural number, that corresponds to the ID of a unique movie title, is accepted as input from the user. Through tf-idf the plot summaries of 5000 different movies that reside in the dataset, are analyzed and vectorized. Next, a number of movies is chosen as recommendations based on their cosine similarity with the vectorized input movie. Specifically, the cosine value of the angle between any two non-zero vectors, resulting from their inner product, is used as the primary measure of similarity. Thus, only movies whose story and meaning are as close as possible to the initial one, are displayed to the user as recommendations.
movie recommender machine unsupervised learning tf-idfThis Visual Studio solution demonstrates a complex implementation, which is a simple order process system, utilizing Windows Workflow (state machine), duplex WCF services(TCP duplex and Silverlight polling), WPF and Silverlight.
duplex machine polling state tcp wcfPlease send PullRequests. These need to pass a automated Test first and after it will get reviewed and on that review either denied or accepted. Feel free to add your own libraries.
neural network machine learning educationA TensorFlow backed FaceNet implementation for Node.js, which can solve face verification, recognition and clustering problems. FaceNet is a deep convolutional network designed by Google, trained to solve face verification, recognition and clustering problem with efficiently at scale.
facenet openface deepface face recognition verification clustering machine deep learning neural network tensorflowMoviebox is a content based machine learning recommending system build with the powers of tf-idf and cosine similarities.Initially, a natural number, that corresponds to the ID of a unique movie title, is accepted as input from the user. Through tf-idf the plot summaries of 5000 different movies that reside in the dataset, are analyzed and vectorized. Next, a number of movies is chosen as recommendations based on their cosine similarity with the vectorized input movie. Specifically, the cosine value of the angle between any two non-zero vectors, resulting from their inner product, is used as the primary measure of similarity. Thus, only movies whose story and meaning are as close as possible to the initial one, are displayed to the user as recommendations.
movie box recommender machine unsupervised learning content based tf-idf moviebox recommendation-system
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