5 Open Source Frameworks for Machine Learning
  • 24 Jan 2020
  • Admin

5 Open Source Frameworks for Machine Learning

In the present scenario, AI technologies are vastly transforming over every sphere of human lives. From how we communicate to the means we acquire for transportation, we seem to be getting increasingly addicted to them.

 

Due to rapid developments, the massive throughput of resources and talent is dedicated to accelerating the growth of technologies. Whether you’re a student, developer, data scientists or entrepreneur, Machine Learning is really important in today’s era. According to statistics, MI and AI are going to create 2.3 million Jobs by 2021. Hence, this growth will contribute to the popularity of Machine Learning Frameworks.

 

Let’s understand, Machine Learning-

 

Before knowing its frameworks, let’s know what it is? Machine learning is something that acts or behaves naturally like humans. It allows software applications to learn from the data, image, text or sound and improve it without being explicitly programmed. Thus, the algorithm of ML teaches a machine to look for a pattern and use that experience to make better decisions without human intervention.

 

The article is about the best frameworks and libraries of Machine Learning. Although, each of these frameworks consumes much time to learn; all these frameworks are based upon User base & community, linear algebra tools and focused on deep learning.

 

Let’s parse through the list:

 

1. TensorFlow

TensorFlow an open-source software library for data-based programming across a range of tasks, which was developed by Google Brain team and initially released on the 9th of November 2015, though the stable release was made available only on 27th of April this year. It is capable of doing regressions, classifications, neural networks, etc. very effectively and is even capable of running both on CPUs and GPUs. TensorFlow is hard to grasp at early stages due to its complex functions, as the user would need to understand Numpy arrays well. Numpy is a Python framework that helps in working with n-dimensional arrays.

 

2. TORCH

TORCH is also a machine learning open-source library, a suitable scientific computing framework. It makers brag it as the easiest ML framework, however, it’s complexity is relatively simple that comes from its scripting language interface from Lua programming language interface. There are just numbers (no int, double or short) in it which are not categorized further like in any other language. So its ease many operations and functions. The torch is adopted by AI Research Group of Facebook, the Idiap Research Institute, Yandex and IBM; as it renders a high level of efficiency and speed.

 

3. CAFFE

 

CAFFE (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, mainly written in CPP. It supports several types of architecture mainly focuses on image classification and segmentation. Usually, it supports all main schemes and fully connected neural network designs. As similar to Tensorflow, it also offers a CUP and GPU based accelerations.

Caffe supports operating systems like Windows and Mac OS X. In addition to this, it is mainly used in academic research projects as well as designing startups Prototypes.

 

 

 

 

4. Scikit-Learn

It is very powerful and free to use a Python library for ML that is vastly acquired in building models. Scikit-Learn is identified and built on foundations of further libraries, such as-  matplotlib, Scipy, and Numby. It’s also an efficient tool that is acquired for statistical modeling techniques, i.e. clustering, classification, and regression.

The framework has come with features like supervised and unsupervised learning algorithms and even cross-validations. It is widely written in Python with certain core algorithms written in Cython in order to achieve performance. Super vector machines are implemented by Cython wrapper around LIBSVM.

 

 

5. 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.

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.