Machine learning (ML) is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it learn for themselves. Being a complex discipline, implementing its models is daunting and difficult. But rather lesser than it used to be, thanks to ML frameworks called Libraries. Libraries help optimize things for better production uses.
- Scikit learn
Each library, and hence ML, on the whole, has these use cases:-
- Data and Personal Security
- Financial Trading
- Recommendation and Online Searches
- Natural Language Processing (NLP)
- Marketing Personalisation and Smart Cars
- Fraud Detection
The use cases of Tensorflow, a Python library for fast numerical computing and creating Deep learning models directly or by using wrappers, include the process of:-
- Acquiring data
- Training models
- Refining future results
Scikit-Learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python. The library is built upon the SciPy (Scientific Python) that must be installed before you can use Scikit-learn.
Pytorch is used for applications such as natural language processing. It is primarily developed by Facebook's artificial-intelligence research group, and Uber's "Pyro" software for probabilistic programming, is built on it.
OpenCV is an open source C++ library for image processing and computer vision. It is a library of many inbuilt functions mainly aimed at real-time image processing.
Caffe2 aims to provide an easy and straightforward way to experiment with deep learning and leverage community contributions of new models and algorithms. Scaling of creations using the power of GPUs in the cloud or to the masses on mobile can be done with Caffe2's cross-platform libraries.
Python Remote Objects (Pyro) is a library that enables you to build applications in which objects can talk to each other over the network, with minimal programming effort. You can just use normal Python method calls to call objects on other machines.
To make a software smarter, ML libraries should be used, and they are all different. One should choose the one that is acceptable for their scenario.