Deeplearning4j Alternatives
Deeplearning4j is described as 'Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs' and is an app. There are nine alternatives to Deeplearning4j for Linux, Mac, Windows, Online / Web-based and Python. The best alternative is TensorFlow, which is both free and Open Source. Other great apps like Deeplearning4j are PyTorch, mlpack, Training Mule and Darknet.
TensorFlow is an open source software library for machine learning in various kinds of perceptual and language understanding tasks. It was originally developed by the Google and later released under the Apache 2.0 open source license on Nov 9, 2015.
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PyTorch enables fast, flexible experimentation and efficient production through a hybrid front-end, distributed training, and ecosystem of tools and libraries.
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mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features.
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Training Mule allows you or your team to easily label images, providing you with the datasets that you require for the best results. With your images labelled, we will train the network of your choice and provide you with the model ready for use.
Darknet: Open Source Neural Networks in C Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
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CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R
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MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity.
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PyCaret is an open source low-code machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a ML experiment. It enables data scientists to perform end-to-end experiments quickly and efficiently.
No screenshots yetCloud AutoML helps you easily train high quality custom machine learning models with limited machine learning expertise needed.
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