Tools You Should Learn to Become an AI (Artificial Intelligence) and ML (Machine Learning) Master

Some sixty years ago, artificial intelligence was just a concept that research scientists had in mind. But ever since the idea of super-computers-capable-of-thinking-like-humans has been floated, it has occupied a particular part in the public consciousness.

Over recent years, we have seen tremendous growth and rapid evolution of artificial intelligence. Today, there is a vast amount of high-quality open-source libraries and software tools available to AI and ML experts. Every day, new ideas and concepts on AI are being discovered, and new applications of AI are being explored. We see how AI is slowly being used in business and everyday life. According to Ottawa IT services experts from Firewall Technical, AI technology will continue to be a significant force in many IT solutions in the next few years. Many tech experts agree that AI has a very bright future ahead, and some even predict the drastic changes AI can bring to the future generations.

Considering all these great news, now is the best time to become an AI master. But for you to become an AI expert, you’ll need to learn some valuable tools in building AI algorithms.

5 AI tools you should learn

1. TensorFlow

This is an open-source platform of tech giant Google for high-performance numerical computation. It has a versatile architecture that allows seamless deployment of computation in different platforms. It can be used in computers, servers, mobile, and edge devices. TensorFlow has very reliable support for deep learning and machine learning. The numerical computation core is very flexible and can be used in various scientific domains.

2. Ai-one

Ai-one works as a chatbot API that allows developers to create intelligent assistants. This can be integrated with other applications such as Skype, Facebook, Slack, Amazon Echo, and more. This tool is equipped with ‘Analyst Toolbox’ that helps create software agents that codify your knowledge management system. The Analyst Toolbox is powered by Nathan ICE, miming the human brain’s complex pattern recognition capability. Using the programming tools of ai-one, developers can build their intelligent software agents that are capable of deciphering the contextual meaning of the text, visual data, and time series.

3. Caffe

Popular among researchers and enterprise users, Caffe is a deep-learning framework that features speed, extensible code, striking architecture, and a broad community of users. Yangqing Jia, a former Ph.D. student from the UC Berkeley and currently the lead engineer for Facebook AI platform, developed this framework. Using a single Nvidia K40 GPU, this AI tool can process over 50 million images per day. Caffe has an impressive learning speed of 4 ms/image and inference of 1 ms/image, which makes it the preferred deep learning network for visual recognition. The updated versions with the broader library can process at a much faster rate. Since it uses C++ programming, it can be compiled in different devices.

4. Apache Mahout

This AI tool is specially designed for developers who want to create machine-learning solutions. It is best suited for data mining categories of the large data landscape. A mahout is an exciting tool that is capable of extracting actionable tasks in a massive data set. This tool allows developers to utilize existing algorithms from Apache Spark, Apache Flink, and H2O. Mahout is integrated into a vast network of companies, such as Twitter, Intel, Accenture, Adobe, LinkedIn, and many more.

5. Apache PedictionIO

Backed up by a free library for data scientists and developers, Apache PredictionIO is an accessible machine-learning server that is aimed to create predictive engines for various machine-learning tasks. It has three core components that, include: PredictionIO platform, EventServer, and Template Gallery.

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