Artificial intelligence is growing in popularity, and ChatGPT is at the trend’s forefront. Nonetheless, there are numerous applications of Artificial Intelligence (AI) beyond language-based models and chatbots.
We decided to ask ChatGPT itself to tell us which are the top 6 major Artificial Intelligence (AI) protocols that everyone should know about.
The Artificial Intelligence (AI) came back with some trending names, but it’s notable that none of them are crypto-specific. Nonetheless, they have broad applications and are likewise commonly used by corporations in the digital currency field.
Nevertheless, we have a special guide that you can take a look at in regard to the top 5 Artificial Intelligence (AI) coins.
That stated, let’s dive in.
TensorFlow: Google’s Deep Learning Framework
TensorFlow is an end-to-end open-source platform for machine learning (ML) developed by Google.
In essence, the tool can be used to:
- Prepare large sets of data
- Build machine learning (ML) models
- Deploy ML models
- Implement MLOps and much more.
Its ecological system of tools, libraries, and resources for developing Artificial Intelligence (AI) applications is broad and comprehensive.
PyTorch: Meta’s Stab at Deep Learning
PyTorch is another open-source machine learning framework, and it’s aimed at accelerating the path from research prototyping to production deployment.
It was developed by Meta ( previously known as Facebook), and it brings forward the next features:
- Distributed Training.
To deliver research and production, the torch.distributed backend offers both scalable and distributed training and performance optimization.
- Cloud Support
PyTorch is is well-supported on some of the major cloud platforms, which as a result provides for frictionless development and easy scaling.
- Production Ready
The transition betwixt eager and graph modes with TorchScript is seamless. Additionally, teams can likewise accelerate the path to production using TorchServe
ONNX: The Open Neural Network Exchange
ONNX brings forward an intermediary machine learning framework. It is used to transform betwixt numerous ML frameworks.
For instance, if you’re using TensorFlow and you want to get to TensorRT, ONNX will provide a good intermediary to transform your model while you are essentially going through the numerous ML frameworks.
The group has worked hard to implement a range of different neural network functions and functionalities.
Keras: Google at it Once Again
You can tell that Google is pushing many of resources in this direction. Keras is another high-level, deep-learning API that’s developed by the tech behemoth.
Keras is written in Python (one of the most comprehensive programming languages) and is used to make the implementation of numerous neural networks easy.
Additionally, Keras likewise supports numerous backend neural network computations. Per ChatGPT:
It porvides a user-friendly interface for building and training deep-learning models. Keras is frequently used in conjunction with TensorFlow as a higher-level abstraction.