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.
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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 to optimizing networks and workflows.
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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:
- Scikit Learn
- TensorFlow
- PyTorch
- CNTK
- Caffe
- Apache MXNet
- Keras
- OpenNN
- AutoML
- H2O
1. Scikit Learn
Scikit-Learn is a widely praised Artificial Intelligence (AI) tool that simplifies the complexities of machine learning tasks. It boasts an intuitive and user-friendly interface that caters to learners across different proficiency levels. Scikit-Learn provides extensive functions encompassing crucial areas like data preprocessing, model selection, and evaluation. Its repertoire spans various algorithms, encompassing classification, regression, clustering, and dimensionality reduction. Leveraging its rich collection of tools and comprehensive documentation, Scikit-Learn equips users with the means to construct and deploy machine learning modeNamaste ls effortlessly.
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2. TensorFlow
Currently, TensorFlow is the most sought-after deep learning library. This machine learning framework 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.
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3. 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.
Want to learn Artificial Intelligence in-depth, check out this Artificial Intelligence Tutorial!
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4. CNTK
This is a Microsoft Cognitive Toolkit, that is also built on similar lines as TensorFlow, but is not as easy to deploy. It has a broader range of APIs such as Python, Java, C, and 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.
Learn more about expert systems in artificial intelligence.
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5. Caffe
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 which exceeds 60 million images per day.
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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.
It can run smoothly on multiple GPUs and machines. Also supports a range of APIs like Python, C++, Scala, R, JavaScript, Julia, Perl, and Go.
Now, you will learn about Keras which is among the most frequently used Artificial Intelligence tools.
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7. Keras
Keras is an AI tool renowned for its simplicity and effectiveness in building and training neural networks. With an intuitive interface and pre-built layers, it empowers users to create powerful models for tasks like image recognition and natural language processing. It offers flexibility by allowing seamless switching between popular backends such as TensorFlow and Theano. It’s accessibility and user-friendly nature have made it a popular choice, enabling individuals to delve into the exciting realm of deep learning with ease and confidence.
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8. OpenNN
OpenNN, an Artificial Intelligence (AI) tool, empowers users to develop and implement neural networks effectively. It provides an intuitive interface and a wide range of functionalities, making it easily accessible to learners and researchers. OpenNN facilitates essential tasks such as data preprocessing, neural network training, and accurate prediction generation. With its user-friendly design and extensive documentation, OpenNN is an ideal choice for individuals new to AI. By harnessing the power of OpenNN, users can explore the vast potential of neural networks across various domains, leveraging its robust capabilities and tools.
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9. AutoML
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.
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10. H2O
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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