Introduction to AI tools
Before talking about the different 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 coming years, artificial intelligence will help in all major developments and is expected to completely change the way things are done today. It is likely to be of indispensible help in all major industries.
At the moment, businesses need to understand the potential benefits of AI that they can have on their business operations. The need for such tools gives freedom to humans to make decisions and allows machines to perform other predefined roles. These tools not only help you grow but also contribute significantly to optimizing networks and workflows.
1. Scikit Learn
It is one of the most used libraries in the machine learning community. There are certain factors that make it a developer library like cross-validation, feature extraction, supervised learning algorithm, etc. But it runs on a single CPU. This library is built on top of SciPy, which includes Numpy, Matplotlib, Pandas, Sympy, IPython and SciPy. It focuses on data modeling rather than data manipulation.
With this, we have discussed some of the most used AI tools in recent years. There are other AI tools that are becoming more and more popular, such as Google ML suite, Theano, Swift AI, Deeplearning4j, etc. These AI techniques can drive advancements in the field and have the potential to optimize human efforts in all possible areas of application. AI in real life.
2. TensorFlow
Currently, TensorFlow is the most searched library for deep learning. This machine learning framework from Google is a Python-friendly open library. It is one of the best AI development tools that facilitate numerical calculations, making future predictions much easier and more accurate. But how?
Instead of entangling themselves in complex algorithms, developers can focus on the logical part of the application. TensorFlow takes care of everything that goes on the back end. This tool allows developers to construct neural networks and create graphical visualizations using Tensorboard. TensorFlow applications can be conveniently run on your local computer, the cloud, Android and iOS devices. Because it is built at deployable scale, it runs on CPU AND GPU.
3. PyTorch
Another contender for AI tools is PyTorch, which is also built on Python. This is similar to TensorFlow in terms of the nature of the selected projects.
However, if faster development is a priority, PyTorch is a better choice. TensorFlow is gone if the project involves larger and more complex projects.
4. CTK
This is the Microsoft Cognitive Toolkit, which is also built on similar lines to TensorFlow, but it is not as easy to deploy. It has a wider range of APIs such as Python, Java, C and C++ and mainly focuses on building neural networks for deep learning.
5. Cafe
This open-source, developed at the University of California, has a Python interface. It is best used in academic research projects and industrial applications. It is among the best tools used in artificial intelligence. This is attributed to its processing power, which exceeds 60 million images per day.
6. Apache MXNet
This AI tool has been adopted by Amazon as its deep learning framework on AWS. Unlike other tools, this is not directly owned by a large company, which provides a favorable environment for an open-source framework.
It can run smoothly on multiple GPUs and machines. It also supports a number of APIs such as Python, C++, Scala, R, JavaScript, Julia, Perl and Go.
7. Keras
It is an open-source high-level neural network library that has a Python interface. This extremely user-friendly tool is built on top of TensorFlow and is also quite easy to use. It is used for rapid prototyping, making it easy to complete state-of-the-art experiments from start to finish with little or no delay. Keras runs smoothly on CPU and GPU. Keras is one of the best open-source artificial intelligence tools in use today.
Since the back-end is handled by the tool itself, it attracts developers from a wide range of backgrounds to create their own scripts without limiting the skills to use the tool. So it all depends on your intentions. If what you need is to create a working prototype, Keras is your choice. Otherwise, if you need to get into low-level computing, then TensorFlow is your way forward.
8. Open the NN
The Open Neural Networks library is another open source library that is used to simulate neural networks and is thus an important part of deep learning research. This library is written in C++. This offers a platform for developers who want to upgrade to advanced analytics
9. AutoML
It is one of the AI tools available to machine learning engineers today. Automate the process of discovering real-world problems using machine learning techniques. This allows data scientists to shift their focus from mundane and repetitive tasks like modeling to solving the problem at hand. This tool paved the way for machine learning for everyone, as even those with little machine learning experience could easily navigate the field.
10. H2O
This business-oriented AI tool is an open source deep learning platform. It helps you make informed business decisions from the data you have. H2O is written in Java and includes interfaces for Python, R, Java, Scala, CoffeeScript, and JSON. This tool is used for predictive modeling, risk analysis, healthcare and insurance analysis.