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TensorBoardLogger.jl is a native library for logging arbitrary data to Tensorboard, extending Julia's standard Logging framework. Many ideas are taken from UniversalTensorBoard and from TensorBoardX. It is based on ProtoBuf.jl.

https://github.com/PhilipVinc/TensorBoardLogger.jlTags | machine-learning julia logging tensorboard |

Implementation | Julia |

License | Public |

Platform |

TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. TensorBoard currently supports five visualizations: scalars, images, audio, histograms, and the graph.This README gives an overview of key concepts in TensorBoard, as well as how to interpret the visualizations TensorBoard provides. For an in-depth example of using TensorBoard, see the tutorial: TensorBoard: Visualizing Learning. For in-depth information on the Graph Visualizer, see this tutorial: TensorBoard: Graph Visualization.

tensorboard visualization mxnetAutoMLPipeline is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and makes it easy to discover optimal structures for machine learning regression and classification. Just take note that + has higher priority than |> so if you are not sure, enclose the operations inside parentheses.

data-science machine-learning data-mining pipeline julia classification ensemble-learning data-mining-algorithms symbolic-expressions automl stacking chaining machine-learning-models pipeline-optimization pipeline-structure scikitlearn-wrapper symbolic-pipelineThe ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. The workspace requires Docker to be installed on your machine (📖 Installation Guide).

nlp docker kubernetes data-science machine-learning r deep-learning jupyter anaconda tensorflow gpu scikit-learn vscode jupyter-notebook data-visualization pytorch neural-networks data-analysis jupyter-labCrayon is a framework that gives you access to the visualisation power of TensorBoard with any language. Currently it provides a Python and a Lua interface, however you can easily implement a wrapper around the provided RESTful API. Note that the server and the client do not have to be on the same machine.

crayon tensorboard tensorflow pytorch torch7 deep-learning data-visualization dockerAWS Neuron is a software development kit (SDK) enabling high-performance deep learning inference using AWS Inferentia custom designed machine learning chips. With Neuron, you can develop, profile, and deploy high-performance inference predictions on top of Inferentia based EC2 Inf1 instances. Neuron is pre-integrated into popular machine learning frameworks like TensorFlow, MXNet and Pytorch to provide a seamless training-to-inference workflow. It includes a compiler, runtime driver, as well as debug and profiling utilities with a TensorBoard plugin for visualization.

New 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 stackingThis repository, based on AlexeyAB's darknet repro, allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. Training with YOLOv4 has never been so easy. This repository has also cross compatibility with Yolov3 training.

docker gui automation monitoring deep-learning neural-network rest-api yolo tensorboard deeplearning object-detection darknet computervision objectdetection no-code yolov3 alexeyab-darknet yolo-gui yolo-tensorboard yolov4Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on top of TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard.

bayesian-methods deep-learning machine-learning data-science tensorflow neural-networks statistics probabilistic-programmingTensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. This README gives an overview of key concepts in TensorBoard, as well as how to interpret the visualizations TensorBoard provides. For an in-depth example of using TensorBoard, see the tutorial: TensorBoard: Visualizing Learning. For in-depth information on the Graph Visualizer, see this tutorial: TensorBoard: Graph Visualization.

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 odeA wrapper around TensorFlow, a popular open source machine learning framework from Google. See a list of advantages over the Python API.

wrapper gpu julia tensorflow machine-learningJulia.jl aggregates and curates decibans of knowledge resources for programming in Julia, an all-purpose programming language that addresses the needs of high-performance numerical analysis and computational science. For Base packages, check if the package you seek is listed in the built-in package manager on github, or check METADATA for registered Julia packages, then use the built-in package manager to install it after checking the requirements for respective versions. Pkg3.jl is an alpha next-generation package manager for Julia that creates a Manifest.toml file that records the exact versions of each dependency and their transitive dependencies.

julia julialang awesome-listFlux is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable. See the documentation or the model zoo for examples.

flux machine-learning neural-networks the-human-brian deep-learning data-scienceNNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments (e.g. local machine, remote servers and cloud). This command will start an experiment and a WebUI. The WebUI endpoint will be shown in the output of this command (for example, http://localhost:8080). Open this URL in your browser. You can analyze your experiment through WebUI, or browse trials' tensorboard.

automl deep-learning neural-architecture-search hyperparameter-optimization optimizerJulia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source code, including instructions for compiling and installing Julia, below. New developers may find the notes in CONTRIBUTING helpful to start contributing to the Julia codebase.

julia julia-language programming-language scientific-computing high-performance-computing numerical-computation machine-learningWhile research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

tensorlayer tensorflow deep-learning machine-learning data-science neural-network reinforcement-learning neural-networks tensorflow-tutorials tensorflow-models computer-vision tensorflow-framework tensorflow-library tflearn keras tensorboard nlp natural-language-processing lasagne tensorflow-experimentsAugmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independent, which is more convenient, allows for finer grained control over augmentation, and implements the most real-world relevant augmentation techniques. It employs a stochastic approach using building blocks that allow for operations to be pieced together in a pipeline. Augmentor is written in Python. A Julia version of the package is also being developed as a sister project and is available here.

augmentation machine-learning deep-learning neural-networksKnet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code. This allows machine learning models to be implemented by defining just the forward calculation (i.e. the computation from parameters and data to loss) using the full power and expressivity of Julia. The implementation can use helper functions, loops, conditionals, recursion, closures, tuples and dictionaries, array indexing, concatenation and other high level language features, some of which are often missing in the restricted modeling languages of static computational graph systems like Theano, Torch, Caffe and Tensorflow. GPU operation is supported by simply using the KnetArray type instead of regular Array for parameters and data. Knet builds a dynamic computational graph by recording primitive operations during forward calculation. Only pointers to inputs and outputs are recorded for efficiency. Therefore array overwriting is not supported during forward and backward passes. This encourages a clean functional programming style. High performance is achieved using custom memory management and efficient GPU kernels. See Under the hood for more details.

HLearn is a high performance machine learning library written in Haskell. For example, it currently has the fastest nearest neighbor implementation for arbitrary metric spaces (see this blog post). HLearn is also a research project. The research goal is to discover the "best possible" interface for machine learning. This involves two competing demands: The library should be as fast as low-level libraries written in C/C++/Fortran/Assembly; but it should be as flexible as libraries written in high level languages like Python/R/Matlab. Julia is making amazing progress in this direction, but HLearn is more ambitious. In particular, HLearn's goal is to be faster than the low level languages and more flexible than the high level languages.

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