Open-Source Frameworks for Creating Machine Learning Models

Open-Source Machine Learning Frameworks

With the rise of artificial intelligence (AI), the demand for machine learning capabilities has increased dramatically. A vast array of industries from finance to health are seeing an uptake of machine learning-based technology. Yet, defining machine learning models remains a complex and resource-intensive endeavour for most businesses and organizations. The challenges can be reduced with the help of a good machine learning framework.

Below is a list of some of the best open-source frameworks and libraries that businesses and individuals can use to build machine learning models.

Amazon Machine Learning

Amazon Machine Learning provides tools and wizards for developing machine learning models. AML makes machine learning more accessible to developers by offering easy-to-use analytics and visual aids. It can also be connected to any data stored on Redshift or Amazon S3. The interactive charts offered by AML help in visualizing input datasets for better data understanding. AML also manages the infrastructure and workflows required to run and scale model creation.

Caffe

Caffe is known for building deep learning apps that allow users to make use of neural networks without needing to write any code or have coding knowledge. Caffe supports operating systems like Windows and Mac OS X. It also partially supports multi-GPU training.

Theano

Theano is a Python library specifically designed for deep learning. It helps the user in defining and evaluating mathematical expressions, including multi-dimensional arrays. Theano has features that include integration with NumPy, symbolic differentiation, and dynamic C code generation. Theano can also be used with other libraries like Keras and Blocks and supports platforms like Mac OS X and Linux.

TensorFlow

TensorFlow is an open-source library developed by Google. It is one of the most popular and well-maintained libraries for deep learning. Clients can create neural networks and computational models on TensorFlow by using flowgraphs and a service called TensorBoard, which offers easy visualization. TensorFlow is available in both Python and C++. It can be easily deployed on different kinds of devices. However, TensorFlow does not support Windows.

Torch

Torch is another fairly easy-to-use open-source framework. Torch offers N-dimensional arrays, linear algebra routines, efficient GPU support and routines for slicing and transporting. Torch also offers multiple model templates. It is based on Lua script. Torch is supported by platforms like Android, Windows, iOS, and Mac OS X.

Conclusion

The best thing about machine learning frameworks is they come with pre-built components that help clients understand and code models easily. The better the machine learning framework, the less complex will be the task of defining machine learning models. The open source machine learning frameworks mentioned above can help anyone build machine learning models efficiently and easily.

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