With the fleeting rise in artificial intelligence, the demand for both AI and Machine Learning skills have increased a lot in the market. Almost every industry, be it finance or health, are making use of machine learning-based technology. The complexity of defining a machine learning models can be reduced with the help of a good machine learning framework. We are presenting you with a list of some of the best libraries and frameworks using which you can build machine learning models.
It’s a Python library specifically designed for deep learning, which helps you in defining and evaluating mathematical expressions, with multi-dimensional arrays included. Theano has features that include integration with NumPy, symbolic differentiation, and a dynamic C code generation. Theano can be used with other libraries like Keras and Blocks and supports platforms such as Mac OS X and Linux.
Caffe is known for building deep learning apps which allows you to make use of neural networks. This doesn’t require writing any code. Caffe supports operating systems like Windows and Mac OS X and multi-GPU training, but only partially.
TensorFlow is another open-source library, which is developed by Google, and known for its deep learning networks. You can make use of flowgraphs on TensorFlow to create neural networks and computational models. TensorFlow is said to be one of the most popular and well-maintained libraries for deep learning.
It is available in both Python and C++. Other deep learning frameworks based on Python are Lasagne, Blocks, and Torch. TensorFlow offers TensorBoard, which helps you in easy visualization. It can be easily deployed on different kinds of devices. TensorFlow does not support Windows.
Torch is another open-source framework which is pretty easy to use. What Torch offers is N-dimensional arrays, linear algebra routines, efficient GPU support and routines for slicing and transporting. Torch offers several pre-determined models and is based on Lua, a scripting language. Torch is both efficient and flexible. Platforms like Android, Windows, iOS, and Mac OS X are supported by Torch.
5. Amazon Machine Learning
Amazon Machine Learning helps by providing tools and wizards for developing machine learning models. The major aim of Amazon Machine Learning is to machine learning more accessible to developers by offering easy-to-use analytics and visual aids. AML can easily be connected to any data stored on Redshift or Amazon S3. The interactive charts offered by Amazon Machine Learning can help you visualize and know more about the input datasets. This helps in better data understanding. The infrastructure and workflows required to run and scale model creation are easily managed by Amazon Machine Learning.
The best thing about machine learning frameworks is that they come with pre-built components that help you understand and code the model easily. The better the machine learning framework, the less complexity there will be in defining machine learning models. The open source machine learning frameworks mentioned above can help you immensely in building your machine learning models with efficiency and easy. Try out different models and see which ones work for you the best.
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