7 open source frameworks for AI and machine learning models

The rapid 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 insurance. In this blog, we have compiled a list of finest frameworks and libraries that you can use to build machine learning models.

1) TensorFlow
TensorFlow is an open-source software library built for deep learning or artificial neural networks developed by Google. With TensorFlow, we can create neural networks and computation models using flowgraphs. It is one of the most well-preserved and famous open-source libraries available for deep learning. This framework is available in C++ and Python. Further similar deep learning frameworks that are based on Python include Theano, Torch, Lasagne, Blocks, MXNet, PyTorch, and Caffe. We can use TensorBoard for easy visualization and see the computation pipeline. Its flexible architecture permits you to deploy easily on different kinds of devices.
The gloomy side of TensorFlow is that it does not have symbolic loops and does not support distributed learning. Further, it does not support Windows.

2)Theano
Theano is a Python based library designed for deep learning. Using this tool, you can explain and esitimate mathematical expressions including multi-dimensional arrays. Optimized for GPU, Theano tool comes with features such as integration with NumPy, dynamic C code generation, and symbolic differentiation. Although, to acquire a high level of abstraction, this tool will have to be used with other libraries such as Keras, Lasagne, and Blocks. Theano supports platforms like Linux, Mac OS X, and Windows.

3) Torch
The Torch is an easy to use open-source computing framework for machine laerning algorithms. This tool offers an structured GPU support, N-dimensional array, numeric optimization routines, linear algebra routines, and routines for indexing, slicing, and transposing. Based on a scripting language such as Lua, Torch tool comes with an enough number of pre-trained models. This flexible and structured machine learning research tool supports major platforms such as Linux, Android, Mac OS X, iOS, and Windows.

4) Caffe
Caffe is a famous deep learning tool created for building apps. Built by  Yangqing Jia for a project during his Ph.D. at UC Berkeley, this tool is having a good Matlab/C++/ Python interface. The tool permits you to rapidly apply neural networks to the problem using text, without writing code. Caffe partially supports multi-GPU training. Caffe tool supports operating systems such as Ubuntu, Mac OS X, and Windows.

5) Microsoft CNTK
Microsoft cognitive toolkit is one of the speedy deep learning frameworks with C#/C++/Python interface support. The open-source framework comes with powerful C++ API and is fastest and more precise than TensorFlow. The tool also supports allocated learning with built-in data readers. It supports algorithms like Feed Forward, CNN, RNN, LSTM, and Sequence-to-Sequence. Microsoft CNTK supports Windows and Linux.

6) Keras
Written in Python, Keras is an open-source library built to make the creation of new DL models easy. This high-level unquit network API can be run on top of deep learning frameworks like TensorFlow, Microsoft CNTK, etc.  Known for its user-friendliness and modularity, Keras tool is ideal for speedy prototyping. This tool is optimized for both CPU and GPU.

7) SciKit-Learn
SciKit-Learn is an open-source Python library built for machine learning. The tool based on libraries namely NumPy, SciPy, and matplotlib can be used for data mining and data analysis. SciKit-Learn is accoutered with a variety of ML models as well as linear and logistic regressors, SVM classifiers, and random forests. This tool can be used for multiple Machine learning tasks such as classification, regression, and clustering. SciKit-Learn tool supports operating systems like Windows and Linux. On the downside, it is not very structured with GPU.

Machine learning frameworks come with pre-built components that are simple to understand and code. A good ML framework thus minimize the complexity of defining ML models. With all these open-source ML frameworks, you can build your ML models easily and quickly.