Top 7 Artificial Intelligence Frameworks to Learn in 2022

Artificial Intelligence has now become synonymous with digital transformation and its impact is evident in the modern-day world. As per the survey, the global AI market is expected to reach $190.61 billion by the year 2025. Digital natives and technology enthusiasts are revolutionizing the digital space by making it more synergistic and interactive. Today, even the average Joe can learn about AI frameworks and contribute to changing the digital fabric of the globe.

An artificial intelligence framework renders faster and easier development of AI applications. These include deep learning, machine learning, NLP solutions, and neural networks. AI is swiftly becoming the norm. Organizations have been collating massive amounts of data, also termed as “big data,” and have hence demanded technologies that are robust enough to evaluate and use that data. That’s the key reason why most technologies in today’s digital age need an AI framework.

In this blog, we will discuss the Top 10 Artificial Intelligence Frameworks to learn in 2022. So let’s get started.

1. TensorFlow

Minimal research on AI frameworks is almost assured to lead you to TensorFlow. It was developed by the team of Google Brain for internal Google use in production and research. The primary version was released in 2015 under the Apache License 2.0. Google released an updated version – Tensor 2.0, in September 2019. It can be used within an array of programming languages, particularly Python, JavaScript, C++, and Java as well. 

Key Features:

• Computational Power: Can be used on any CPU or GPU and therefore has high computational power.
• Usage of Graph Abstraction: Uses graph abstraction for creating machine models. TensorFlow permits the building of neural networks with graphs that depict operations as nodes.

To get more information about TensorFlow click here.

2. PyTorch

PyTorch is one of the most opted frameworks as it is super-adaptable in terms of integrations and languages. It was developed by Facebook’s AI Research lab and was released in January 2016 as an open-source library chiefly used in deep learning, computer vision, and natural language processing software. Another notable feature of Pytorch is its great affinity with iOS as well as Android.  

Key features:

• Computational Graph: During runtime, it has computational graph support. Therefore expressing and evaluating a mathematical expression is easier in PyTorch.
• Easy Debugging: It uses debugging tools like IPDB and PDB. Programmers can use Python’s IDE PyCharm for debugging due to the computational graph at runtime.

3. Theano

Theano is a python based AI framework capable of delivering peak precision for networks that need high computational power. It is used for manipulating and evaluating mathematical expressions, typically matrix-valued expressions. Expressions in Theano are evaluated faster because of dynamic code generation. It also supports high-level modules like Lasagne.  

Key features:

• Execution Speed: It can use GPUs and execute fragments of expressions in GPU or CPU, which is faster than Python.
• Impeccable Accuracy Ratio:  It is known for its accuracy and provides a great accuracy ratio regardless of how minimal the values are, which makes it highly preferable.

To know more about this amazing AI framework click here.

4. Microsoft CNTK

Microsoft Cognitive Toolkit is a versatile framework supporting voice remodeling, message, and text. The initial release of this framework was made on 25 Jan 2016. This framework provides an efficient expression of low leveled and high leveled networks and its resources assure high efficiency due to optimum utilization. Microsoft CNTK is Python and C++ compatible and hence can work with various servers at a time, shortening the span of the entire process.  

Key features:

• Highly advanced: It’s highly advanced as it supports GAN, CNN, and RNN.  Also, CNTK is highly optimized to provide accuracy, scalability, and high-level integration.
• Faster Evaluation: It offers a faster evaluation of machine models that provides a scalable environment.

5. Scikit-learn

Scikit-learn offers an array of supervised and unsupervised algorithms and is based on Python. Its latest release was on 25 December 2021. Through SciKit, the veracity of supervised models on unseen data can be checked. Also, particular features from images and text can be extracted using this framework.  

Key features:

• Dimensionality Moderation: Reduction of attributes in data for feature selection and summarizing.
• Supervised Learning Algorithms: Has a plethora of supervised learning algorithms like Support Vector Machines and Decision Trees.
• Unsupervised Learning Algorithms: Also has unsupervised algorithms like cluster analysis, factoring, and unsupervised neural networks.

6. Apache Mahout

Apache Mahout aims at the development of scalable frameworks and is one of the most preferred open-source networks. It allows applications to analyze large data volumes in a relatively short period. It also enables evolutionary programming as Mahout comes with distributed function capabilities. It is developed in Java and Scala.  

Key features:

• Ready-to-use framework: Mahout is very favorable for developers in terms of conducting data mining on sizable sets of data, due to its ready-to-use framework.
• Mathematically Expressive: It uses Scala DSL that is mathematically very expressive. Hence the width of ideas that can be portrayed in that specific language is more.

7. Amazon Machine Learning

Amazon Machine Learning is a new player in the AI sphere but is quickly becoming famous due to its apt features. It was initially released on 29 November 2017. The fundamental operations of this framework consist of data analysis, model training, and evaluation. Amazon Machine Learning is very beginner-friendly as it has customized tools for every experience level. Hence it’s a preferable option for developers of all skill levels.  

Key features:

• Wide Usage: This framework is widely used by developers, data scientists, and machine learning researchers as it allows to discover patterns in end-user data through algorithms and construct mathematical models based on these patterns.
• Abstracted Framework: Amazon Machine Learning provides less versatility due to its abstracted framework.
• AI-powered framework: This framework has less code due to its AI-powered framework and APIs. It permits business analysts to develop ML models and forecast accurate predictions without much ML expertise.