Soon, Artificial Intelligence is expected to support all significant development and ultimately transform how things are done today. It is likely to be an essential component in all major industries. Today, businesses need to understand the possible benefits of AI on their business operations. The need for such tools gives humans the freedom to make decisions and allow machines to play other predetermined roles. This article will discuss the top ten AI tools and how they effectively streamline business processes and optimize results.
1. Scikit-learn Scikit-learn is an open-source machine learning (ML) library that supports supervised and unsupervised learning. It also offers tools for model fitting, data preprocessing, model evaluation, model selection, and other utilities. Since it is open-source, Scikit-learn is accessible to everybody and reusable in various contexts.
Scikit-learn expands on two important libraries of Python, NumPy, and SciPy. In addition, it integrates plenty of calculations for regular AI and data mining tasks. Even processes like changing information, feature identification, and ensemble techniques can be done in a couple of lines.
2. TensorFlow TensorFlow is the most popular deep-learning library. This machine learning framework is a Python-friendly open-source library. Surprisingly, it is one of the best AI development tools that help numerical computation make more accurate predictions.
Developers can emphasize the logic portion of the application rather than getting entangled in the most minor details of algorithms. TensorFlow handles everything that goes on the back end. This AI tool enables developers to construct neural networks and create graphical visualization via Tensorboard. TensorFlow applications can run on your local machine, Android, iOS devices, and the cloud.
3. Caffe Caffe open-source was developed at the University of California Berkeley AI Research (BAIR). It also runs on Python. Currently, it has its best features in academic research works and industrial applications. This AI tool is among the best tools on our list. This is attributed to its processing power which exceeds 60 million images per day. Google’s DeepDream depends on Caffe Framework . This structure is a BSD-authorized C++ library with Python Interface.
4. Keras Keras is a high-level open-source neural network library that uses TensorFlow or Theano as its backend. This user-friendly tool is suitable for fast prototyping, facilitating the completion of innovative experiments with little or no delay. Keras is compatible with CPU and GPU, making it one of the best open-source AI tools available 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 creating their scripts, putting no limitations on skills for using the device. So, it all comes down to your intentions. If you need to create a functioning prototype, Keras is your choice. But if you need to get into the low-level computations of it, then TensorFlow is your way forward.
5. Apache MXNet MXNet is an AI tool adopted by Amazon, and its deep learning framework is on Amazon Web Services (AWS). Contrary to other tools, this is not directly owned by a major corporation, which is a suitable environment for an open-source framework. MXNEt can run smoothly on multiple GPUs and machines. In addition, it supports several APIs, including Python, C++, Scala, R, JavaScript, Julia, Perl, and Go.
6. PyTorch Next on our list of AI tools is PyTorch , which is under the Python framework. This is similar to TensorFlow in what kinds of projects are chosen. However, when the focus is on faster development, PyTorch is the better choice. TensorFlow is gone if the project involves more extensive and complex projects.
7. Microsoft Cognitive Toolkit (formerly CNTK) The CNTK tool, now known as Microsoft Cognitive Toolkit , is also built on similar lines as TensorFlow. The sad part is that it is more challenging to deploy. It has a broader range of APIs like Python, Java, C, and C++ and mainly focuses on creating deep-learning neural networks.
8. OpenNN The open Neural Networks library is also an open-source library that simulates neural networks. It is a crucial component of deep learning research. OpenNN is written in the C++ language. This provides an environment for developers wanting to upgrade to advanced analytics
9. Auto ML Auto ML is likely one of the strongest and newest additions to the AI tools available to serve machine learning engineers. As we enter a new realm of meta, wherein software helps build another software, AutoML is a good choice. In addition to time saved, this tool is also helpful for someone new to machine learning.
10. H2O H2O is a business-oriented and open-source deep learning platform. It helps generate insightful decisions on business from the data at disposal. H2O runs in Java and includes Python, R, Java, Scala, CoffeeScript, and JSON interfaces. The tool features predictive modeling, risk analysis, healthcare, and insurance analytics. Likewise, H2O supports the commonly-used statistical and ML algorithms, linear models, deep learning, etc.
Conclusion Companies that have embraced the power of AI in their business enable them to succeed. AI applications include virtual assistants, voice assistants, autonomous vehicles, navigation, robotics, healthcare, and many more. You can even create logos and smart content with the help of AI.
The trend in AI can be likened to the digital process invented to lessen the pain of paper-based transactions. This innovation resulted in a significant change. On the same path, AI will also bring massive change across all industries. It’s time to master these AI tools and see what value they will bring to your business.
