Artificial Intelligence has made waves in the domain of app development. In the last few years, AI developments support the IT sector beyond their expectations. With seamless support and predictive capabilities, AI is winning the heart of developers. Artificial Intelligence is now influencing everything from finance and healthcare to movies and education.
Hence, it is the right time to talk about this technology and its frameworks. We believe that every business has its requirements. Therefore, we have listed the top ten AI frameworks to help you in choosing the best. These top AI tools are constantly changing the digital world. Without further delay, lets us dive into the pool of Artificial Intelligence.
Even if you’re new to app production, you should be familiar with this framework. TensorFlow is the most widely used open-source AI system around the world. TensorFlow’s architecture enables computation on either CPU or GPU. It is accessible through a laptop or even a mobile device. Moreover, it provides a scalable multi programming interface to make the programming easy. It also offers extensive manuals to people.
Features of TensorFlow includes:
- The topmost quality of TensorFlow is to build a neural network.
- Machines can perform logically by using these neural networks.
- It is an excellent deep learning framework.
- It also describes the basic calculations regardless of how simple the measures are.
- TensorFlow can work in both Ios and Android environments.
- Moreover, it manages massive data such as big data.
2: Microsoft Cognitive Toolkit:
How can we forget Microsoft CNTK after TensorFlow? Developers can reap the benefits of Microsoft CNTK as a library in their C++, Python, and other programs. Moreover, users can use the model description language as an ML (Machine Learning) tool.
Features of CNTK includes:
- Microsoft CNTK is flexible because it allows you to try new ideas.
- It supports multiple tasks such as image, speech, and more.
- It provides the best performance on production systems.
- Also, it quickly trains the production system.
Next, we have Torch on the list. It is another computer framework that is based on the Lua programming language. You get a diverse set of algorithms with this framework. Several Torch framework features include an N-dimensional array, fast GPU support, linear algebra routine, and more.
Features of Torch includes:
- It provides you the efficient GPU support
- With Torch, you get the solid N-dimensional array.
If you spend some amount of time studying deep learning, you will eventually come across the word Caffe. Caffe is a well-known framework for developing, preparing, testing, and deploying deep neural networks.
Features of Caffe includes:
- Caffe’s expressive architecture promotes innovation and advancement.
- Its extensible code encourages active growth.
- The architecture keeps up with the latest developments in both code and models.
- Caffe’s speed makes it ideal for scientific studies.
Keras, like TensorFlow, is a Python-based open-source Artificial Intelligence platform. It offers a user-friendly interface. Keras is at the top of TensorFlow, and other frameworks thanks to its high-level neural network API.
Features of Keras includes:
- Keras is specially designed for humans. One of the best parts of this framework is that it makes the best user experience by keeping it at the center.
- Developers can add easily add new modules.
- It also reduces the cognitive load with simple APIs.
What do you think when you hear about Theano? A programing language? AI framework? A Python library? And what else? Well, Theano is the combination of all of these. It is one of the strong competitors to TensorFlow. A robust library that allows for numerical operations. Theano has been used in powering large-scale computational operations.
It is also empowering the massive scientific investigations. Theano can combine computer algebra with the computer’s aspects. Moreover, it allows the developer to execute the code by using both CPU and GPU efficiently.
Features of Theano includes:
- Integration with the NumPy.
- Transparent usage of GPU.
- Dynamic generation of C code.
The most potent open-source framework for Artificial Intelligence development is Scikit-learn. It encourages academic and commercial use. Before using the Scikit-learn framework, you need to install some stacks. These stacks are SciPy, Matplotlib, Sympy, IPython, Pandas, and more. These stacks are used for different purposes, including comprehensive 2D/3D plotting, data analysis, enhancing interactive console, and many other benefits.
Moreover, Scikit-learn is a simple and practical data analysis framework. You can conduct several tasks, including preprocessing, model selections, clustering, and a lot more. Furthermore, Scikit-learn can host tasks such as hotel reservations, music listening, and many more. As a result, users can integrate algorithms from the framework into their applications.
Features of Scikit-learn includes:
- It identifies the texts and data attributes.
- It is simple to define meaningful characteristics when building supervised models.
- To build and evaluate datasets for a particular property.
Based on the internet Machine Learning framework Accord.Net has several ready-to-use libraries. The developer prefers this framework for commercial use. It can develop production-grade computer vision. Moreover, it also creates statistics applications.
Features of Accord.Net includes:
- Net allows developers to solve the most fundamental problems. It supports logistic regression, vector machines, and other features.
- It converts audio signals, including audio filters and spatial domain.
- Moreover, it has over 35 mathematical hypothesis checks, including the Sign test, ANOVA test, and non-parametric tests.
It is another open-source deep learning framework. MXNET is powerful enough to support different giant industries, including healthcare, transportations, and manufacturing. Moreover, it supports multiple languages and allows faster model training.
Features of MXNET includes:
- MXNET supports multi-machine training and GPU training.
- It offers both pace and versatility.
- With TVM support, the developer can run the software on a variety of new computers.
- It supports a variety of language bindings, including deep integration with Python and others.
10: Google AutoML:
Last but not the least, we have the Google AutoML AI framework. It is one of the most powerful frameworks. It has the most accessible graphical user interface. This user interface helps the developers to improve the models based on the data.
Features of AutoML includes:
- It provides optimal results by accelerating the performance of machine learning models.
- AutoML can also make accurate forecasts.
- Furthermore, it enables the developer to train models based on market requirements.
The Future of Artificial Intelligence:
- Artificial Intelligence enables technical knowledge so that users can manage the websites without any difficulties.
- AI not only supports professional developers but also guides beginners in development projects.
Undoubtedly the Artificial Intelligence is transforming day by day. It is one of the essential components of web development. Hence, we can say that it will make a significant change in the web development services. That is the reason why Artificial Intelligence services are increasing day by day. Almost every software company around the globe adopting AI to develop smart web applications.
AI is going to rule the world soon by improving the user experience. Also, allowing faster web application development. Before you hire any AI developers’ company, make sure that they understand your project’s requirements.