11 Open-Source Frameworks for AI and Machine Learning Models
11 Open-Source Frameworks for AI and Machine Learning Models
A good ML framework thus reduces the complexity of defining ML models. With these open-source ML frameworks, you can build your ML models easily and quickly.
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The meteoric rise of artificial intelligence in the last decade has spurred a huge demand for AI and ML skills in today’s job market. ML-based technology is now used in almost every industry vertical, from finance to healthcare. In this article, I compiled a list of the best frameworks and libraries that you can use to build machine learning models.
Developed by Google, TensorFlow is an open-source software library built for deep learning or artificial neural networks. With TensorFlow, you can create neural networks and computation models using flowgraphs. It is one of the most well-maintained and popular open-source libraries available for deep learning. The TensorFlow framework is available in C++ and Python. Other similar deep learning frameworks that are based on Python include Theano, Torch, Lasagne, Blocks, MXNet, PyTorch, and Caffe. You can use TensorBoard for easy visualization and see the computation pipeline. Its flexible architecture allows you to deploy easily on different kinds of devices. On the negative side, TensorFlow does not have symbolic loops and does not support distributed learning. Further, it does not support Windows.
Theano is a Python library designed for deep learning. Using the tool, you can define and evaluate mathematical expressions, including multi-dimensional arrays. Optimized for GPU, the tool comes with features including integration with NumPy, dynamic C code generation, and symbolic differentiation. However, to get a high level of abstraction, the tool will have to be used with other libraries such as Keras, Lasagne, and Blocks. The tool supports platforms such as Linux, Mac OS X, and Windows.
Torch is an easy-to-use open-source computing framework for ML algorithms. The tool offers an efficient GPU support, N-dimensional arrays, numeric optimization routines, linear algebra routines, and routines for indexing, slicing, and transposing. Based on a scripting language called Lua, the tool comes with an ample number of pre-trained models. This flexible and efficient ML research tool supports major platforms such as Linux, Android, Mac OS X, iOS, and Windows.
Caffe is a popular deep learning tool designed for building apps. Created by Yangqing Jia for a project during his Ph.D. at UC Berkeley, the tool has a good Matlab/C++/ Python interface. The tool allows you to quickly apply neural networks to the problem using text without writing code. Caffe partially supports multi-GPU training. The tool supports operating systems such as Ubuntu, Mac OS X, and Windows.
5. Microsoft CNTK
Microsoft Cognitive Toolkit is one of the fastest deep learning frameworks with C#/C++/Python interface support. The open-source framework comes with a powerful C++ API and is faster and more accurate than TensorFlow. The tool also supports distributed learning with built-in data readers. It supports algorithms such as feed-forward, CNN, RNN, LSTM, and sequence-to-sequence. The tool supports Windows and Linux.
Written in Python, Keras is an open-source library designed to make the creation of new DL models easy. This high-level neural network API can be run on top of deep learning frameworks like TensorFlow, Microsoft CNTK, etc. Known for its user-friendliness and modularity, the tool is ideal for fast prototyping. The tool is optimized for both CPU and GPU.
scikit-learn is an open-source Python library designed for machine learning. The tool based on libraries such as NumPy, SciPy, and matplotlib can be used for data mining and data analysis. scikit-learn is equipped with a variety of ML models including linear and logistic regressors, SVM classifiers, and random forests. The tool can be used for multiple ML tasks such as classification, regression, and clustering. The tool supports operating systems like Windows and Linux. On the downside, it is not very efficient with GPU.
Written in C#, Accord.NET is an ML framework designed for building production-grade computer vision, computer audition, signal processing, and statistics applications. It is a well-documented ML framework that makes audio and image processing easy. The tool can be used for numerical optimization, artificial neural networks, and visualization. It supports Windows.
9. Spark MLlib
Apache Spark’s MLIib is an ML library that can be used in Java, Scala, Python, and R. Designed for processing large-scale data, this powerful library comes with many algorithms and utilities such as classification, regression, and clustering. The tool interoperates with NumPy in Python and R libraries. It can be easily plugged into Hadoop workflows.
10. Azure ML Studio
Azure ML Studio is a modern cloud platform for data scientists. It can be used to develop ML models in the cloud. With a wide range of modeling options and algorithms, Azure is ideal for building larger ML models. The service provides 10GB of storage space per account. It can be used with R and Python programs.
11. Amazon Machine Learning
Amazon Machine Learning (AML) is an ML service that provides tools and wizards for creating ML models. With visual aids and easy-to-use analytics, AML aims to make ML more accessible to developers. AML can be connected to data stored in Amazon S3, Redshift, or RDS.
Machine learning frameworks come with pre-built components that are easy to understand and code. A good ML framework thus reduces the complexity of defining ML models. With these open-source ML frameworks, you can build your ML models easily and quickly.
Published at DZone with permission of Tharika Tellicherry . See the original article here.
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