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Artificial intelligence open-source frameworks to learn

The spectrum of artificial intelligence is much broader and includes machine learning, artificial neural networks, deep learning, and machine memory. Add the inclusion of AI in education and the workforce of the future will be better prepared to face the unknown challenges of the workplace of tomorrow.

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Written By:  Ankush Singla, Co-founder, Coding Ninjas

Artificial Intelligence is one of the emerging technologies which tries to simulate human reasoning in AI systems. These times demand future-proofing yourself with the AI technology that is on the edge of becoming the next big evolution. When John McCarthy invented the term Artificial Intelligence in the year 1950 he wouldn’t have predicted the wide future of this technology and how far it would travel.

Experts predict that networked artificial intelligence will increase human effectiveness, but also threaten human autonomy, agency and ability. The spectrum of artificial intelligence is much broader and includes machine learning, artificial neural networks, deep learning and machine memory.

To define Artificial Intelligence is the ability of a computer program to learn and think. Everything can be considered Artificial intelligence if it involves a program doing something that we would normally think would rely on the intelligence of a human.

Few trending and open-source networks to rely on are as follows:

  • Scikit-learn: It is a free machine learning library for Python. It features various algorithms like support vector machine, random forests and k-neighbours and it also supports Python numerical and scientific libraries like NumPy and SciPy. Its library contains a lot of efficient tools for machine learning and statistical modelling including classification, regression, clustering and dimensionality reduction.
  • TensorFlow: It is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. It features easy model building, robust model production and can hold a powerful research experiment for future projects.
  • Keras: It is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load as it offers consistent & simple APIs, it minimises the number of user actions required for common use cases and it provides clear & actionable error messages. It also has extensive documentation and developer guides.
  • Theano: It is a Python library that allows us to evaluate mathematical operations including multi-dimensional arrays so efficiently. It is mostly used in building Deep Learning Projects. It works way faster on Graphics Processing Unit (GPU) rather than on CPU.
  • OpenNN: It is a comprehensive implementation of the multilayer perceptron neural network in the C++ programming language. It includes several objective functional and training algorithms, as well as different utilities for the solution of a wide range of problems. Open NN also provides an effective framework for the research and development of neural networks algorithms and applications.

The World Economic Forum estimates that, by 2022, a large proportion of companies will have adopted technologies such as machine learning, and therefore strongly encourages governments and education to focus on rapidly raising education and skills, with a focus on both STEM (science, technology, engineering and mathematics) and non-cognitive soft skills, to meet this impending need.

Microsoft’s recent study shows that, by 2030, students will need to have mastered two facets of this new world by the time they graduate which includes knowing how to utilise ever-changing technology, such as AI, to their advantage and understand how to work with other people in a team to problem-solve effectively.


If you have an interesting article / experience / case study to share, please get in touch with us at [email protected]

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1 Comment
  1. Karan says

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