Introduction to AI Tools
Before we talk about the various types of Artificial Intelligence tools available in the market, let us first give a brief understanding of AI tools.
It is clear that in the years to come, Artificial Intelligence is expected to assist in all major developments and completely change the way things are done today. It is likely to be an inseparable helping hand in all the major industries.
At the moment, businesses need to understand the probable benefits of AI that they can have on their business operations. The need for such tools arises giving humans the freedom to make decisions and let a machine play other predefined roles. These tools not only help you develop but also make significant contributions in optimizing networks and workflows.
List of Artificial Intelligence Tools
Here, let’s learn about the numerous AI automation tools that are used in the industry today.
This blog covers the following tools:
1. Scikit Learn
This is one of the most widely used libraries in the machine learning community. There are certain factors that make it the go-to library for developers, such as cross-validation, feature extraction, supervised learning algorithm, etc. However, it runs on a single processor CPU. This library is built on SciPy, which includes Numpy, Matplotlib, Pandas, Sympy, IPython, and SciPy. It’s focused on modeling the data, rather than manipulating it.
With this, we have discussed some of the most used tools in Artificial Intelligence in recent years. There are other AI tools that are growing in popularity like Google ML kit, Theano, Swift AI, Deeplearning4j, etc. These Artificial Intelligence tools and techniques can further the advancement in the field and have the potential to optimize human effort in every possible area of application of AI in real life.
Currently, TensorFlow is the most sought-after deep learning library. This machine learning frameworks by Google is a Python-friendly open source library. It is one of the best AI development tools that facilitate numerical computation making future predictions much easier and more accurate. But how?
Instead of getting entangled in the nitty-gritty of algorithms, developers can focus on the logic part of the application. TensorFlow takes care of everything that goes on the back end. The tool allows developers to construct neural networks and create graphical visualization using Tensorboard. TensorFlow applications can be run conveniently on your local machine, cloud, Android, and iOS devices. As it is built on a deployable scale, it runs on CPU AND GPU.
Check out this YouTube video tutorial on AI:
Next in the list of Artificial Intelligence tools is PyTorch.
Next in competition for AI tools is PyTorch, which is also built on Python. This is similar to TensorFlow in terms of the nature of the projects chosen. However, when the priority is for faster development, PyTorch is the better choice. TensorFlow is gone in case the project involves larger and more complex projects.
Now, let us learn about CNTK, which is also one of the best Artificial Intelligence tools.
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This is a Microsoft Cognitive Toolkit, that is also built on similar lines as TensorFlow, but not as easy to deploy. It has a broader range of APIs such as Python, Java, C, C++ and mainly focuses on creating deep learning neural networks.
Further, let’s learn about Caffe which is yet another popular Artificial Intelligence tool in the market today.
This open-source, developed at the University of California, has a Python interface. It has its best application in academic research projects and industrial disposition. It is among the best tools used in Artificial Intelligence. This is attributed to its processing power that exceeds 60 million images per day.
6. Apache MXNet
This Artificial Intelligence tool is adopted by Amazon as its deep learning framework on AWS. Unlike other tools, this is not directly owned by a major corporation, which provides a conducive environment for an open-source framework.
Now, you will learn about Keras which is among the most frequently used Artificial Intelligence tools.
Learn about key differences between Keras and TensorFlow in this comparison blog on Keras vs TensorFlow.
This is a high-level open-source neural network library that has a Python interface. This extremely user-friendly tool is built on top of TensorFlow and is comparatively easier to use as well. It is used for fast prototyping that facilitates the completion of state-of-the-art experiments from start to end with little or no delay. Keras run seamlessly on CPU and GPU. Keras is one of the best open-source Artificial Intelligence tools used today.
As the back end is dealt with by the tool itself, it attracts developers from a wide range of backgrounds to get their hands on to create their own scripts, putting no limitation on skills for using the tool. So, it all comes down to your intentions. If what you need is to create a functioning prototype, Keras is your call. Else if you need to get into the low-level computations of it, then TensorFlow is your way forward.
Learn about the production rule system to create AI algorithms in this comprehensive blog on Production Systems in AI.
Open Neural Networks library is another open-source library that is used to simulate neural networks and hence an important component of deep learning research. This library is written in the C++ language. This offers a platform for developers wanting to upgrade to advanced analytics
This is one of the top Artificial Intelligence tools currently at the disposal of a machine learning engineer. It automates the processes involved in articulating a real-world problem using machine learning techniques. This helps a data scientist to shift his focus from mundane repetitive tasks like modeling to handling problems at hand. The tool has cleared the way for machine learning for everybody, as someone without much ML experience can easily navigate in the field.
This business-oriented Artificial Intelligence tool is an open-source deep learning platform. It helps draw insightful decisions on business from the data at disposal. H2O is written in Java and includes interfaces for Python, R, Java, Scala, CoffeeScript, and JSON. The tool finds application in predictive modeling, risk analysis, healthcare, and insurance analytics.
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Companies that have realized the potential of AI in their business early on have been able to achieve greater heights. AI not only powers the business but also makes it more efficient within a short period. The trend of AI in businesses can be compared to the digital process that was introduced taking away the pain of paper-based processes. Like this revolution brought in a major change, similarly, AI is also going to bring in a huge change across all the industries.
At the heart of this change are the AI-powered tools that help businesses to focus on efficiency and find better ways to generate revenues. However, one herculean task that companies need to pull off is finding suitable Artificial intelligence tools based on their needs. The right AI tool can help businesses make bigger leaps in terms of saving costs and increasing net profit.
Prepare yourself for the industry by going through this Top Artificial Intelligence Interview Questions and Answers!