AutoGluon - AutoML for Text, Image, and Tabular Data

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AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, and tabular data.

data-science machine-learning natural-language-processing computer-vision deep-learning mxnet scikit-learn tabular-data pytorch image-classification ensemble-learning object-detection

determined - Determined: Deep Learning Training Platform

•    Python

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Determined integrates these features into an easy-to-use, high-performance deep learning environment — which means you can spend your time building models instead of managing infrastructure. To use Determined, you can continue using popular DL frameworks such as TensorFlow and PyTorch; you just need to update your model code to integrate with the Determined API.

kubernetes machine-learning deep-learning tensorflow pytorch hyperparameter-optimization hyperparameter-tuning hyperparameter-search distributed-training ml-infrastructure ml-platform

test-tube - Python library to easily log, track machine learning code, experiments and parallelize hyperparameter search

•    HTML

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Test tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use. If you're a researcher, test-tube is highly encouraged as a way to post your paper's training logs to help add transparency and show others what you've tried that didn't work.

deep-learning machine-learning tensorflow hyperparameter-optimization neural-networks data-science keras pytorch caffe2 caffe chainer grid-search random-search

auto_ml - Automated machine learning for analytics & production

•    Python

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auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.

machine-learning data-science automated-machine-learning gradient-boosting scikit-learn machine-learning-pipelines machine-learning-library production-ready automl lightgbm analytics feature-engineering hyperparameter-optimization deep-learning xgboost keras deeplearning tensorflow artificial-intelligence

tpot - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

•    Python

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Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

machine-learning data-science automl automation scikit-learn hyperparameter-optimization model-selection parameter-tuning automated-machine-learning random-forest gradient-boosting feature-engineering xgboost genetic-programming

adatune - Gradient based Hyperparameter Tuning library in PyTorch

•    Python

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AdaTune is a library to perform gradient based hyperparameter tuning for training deep neural networks. AdaTune currently supports tuning of the learning_rate parameter but some of the methods implemented here can be extended to other hyperparameters like momentum or weight_decay etc. AdaTune provides the following gradient based hyperparameter tuning algorithms - HD, RTHO and our newly proposed algorithm, MARTHE. The repository also contains other commonly used non-adaptive learning_rate adaptation strategies like staircase-decay, exponential-decay and cosine-annealing-with-restarts. The library is implemented in PyTorch. The goal of the methods in this package is to automatically compute in an online fashion a learning rate schedule for stochastic optimization methods (such as SGD) only on the basis of the given learning task, aiming at producing models with associated small validation error.

machine-learning deep-learning pytorch neural-networks hyperparameter-tuning automl learning-rate-scheduling

dist-keras - Distributed Deep Learning, with a focus on distributed training, using Keras and Apache Spark

•    Python

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Distributed Deep Learning with Apache Spark and Keras. Distributed Keras is a distributed deep learning framework built op top of Apache Spark and Keras, with a focus on "state-of-the-art" distributed optimization algorithms. We designed the framework in such a way that a new distributed optimizer could be implemented with ease, thus enabling a person to focus on research. Several distributed methods are supported, such as, but not restricted to, the training of ensembles and models using data parallel methods.

machine-learning deep-learning apache-spark data-parallelism distributed-optimizers keras optimization-algorithms tensorflow data-science hadoop

evalml - EvalML is an AutoML library written in python.

•    Python

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EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.

data-science machine-learning optimization feature-selection model-selection feature-engineering hyperparameter-tuning automl

Awesome-System-for-Machine-Learning - A curated list of research in machine learning system

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A curated list of research in machine learning system. Link to the code if available is also present. Now we have a team to maintain this project. You are very welcome to pull request by using our template.

infrastructure distributed-systems machine-learning deep-neural-networks system deep-learning optimization paper deep-reinforcement-learning inference automl computer-system edge-computing model-database resouce-management

automl-gs - Provide an input CSV and a target field to predict, generate a model + code to run it.

•    Python

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Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native Python code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see exactly how the data is processed, how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and TPOT, offers a zero code/model definition interface to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks, with minimal Python dependencies (pandas + scikit-learn + your framework of choice). automl-gs is designed for citizen data scientists and engineers without a deep statistical background under the philosophy that you don't need to know any modern data preprocessing and machine learning engineering techniques to create a powerful prediction workflow.

machine-learning tensorflow keras xgboost automl

polyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes

•    Python

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Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ai dl ml k8s

Auto-PyTorch - Automatic architecture search and hyperparameter optimization for PyTorch

•    Python

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While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search. To bring the best of these two worlds together, we developed Auto-PyTorch, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch is mainly developed to support tabular data (classification, regression), but can also be applied to image data (classification). The newest features in Auto-PyTorch for tabular data are described in the paper "Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL" (see below for bibtex ref).

awesome-automl-papers - A curated list of automated machine learning papers, articles, tutorials, slides and projects

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Awesome-AutoML-Papers is a curated list of automated machine learning papers, articles, tutorials, slides and projects. Star this repository, and then you can keep abreast of the latest developments of this booming research field. Thanks to all the people who made contributions to this project. Join us and you are welcome to be a contributor. Automated Machine Learning (AutoML) provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.

hyperparameter-optimization automl neural-architecture-search automated-feature-engineering

client - 🔥 A tool for visualizing and tracking your machine learning experiments

•    Python

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Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models. If you have any questions, please don't hesitate to ask in our Slack community.

machine-learning reinforcement-learning deep-learning tensorflow keras pytorch hyperparameter-search experiment-track

tensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists

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TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.

tensorlayer deep-learning tensorflow machine-learning data-science neural-network reinforcement-learning artificial-intelligence gan a3c tensorflow-tutorials dqn object-detection chatbot tensorflow-tutorial imagenet google

ml-workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.

•    Jupyter

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The 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-lab

AdaNet - Fast and flexible AutoML with learning guarantees

•    Jupyter

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AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models.

machine-learning deep-learning tensorflow gpu ensemble automl learning-theory neural-architecture-search distributed-training tpu

autokeras - AutoML library for deep learning

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AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. Official website tutorials.

machine-learning deep-learning tensorflow keras automl automated-machine-learning neural-architecture-search autodl

machineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

•    Python

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I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competition

deep-learning-book - Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

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Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

deep-learning neural-network machine-learning tensorflow artificial-intelligence data-science pytorch