Machine learning is a field of Artificial Intelligence. In this article we are going to discuss about the top 3 machine learning frameworks in 2022. Also, other popular machine learning frameworks are listed.
The libraries or tools which allow the developers to build Machine Learning models without knowing the core algorithms is known as machine learning frameworks.
Most popular machine learning frameworks are listed below:
- Sci-kit Learn
- Apache MXNet
Top 3 machine learning frameworks in 2022 to learn about:
It includes a comprehensive library for programming, classifications, regression models, neural networks, and a suite for writing software algorithms. It also works with both CPUs as well as GPUs. TensorFlow has the benefit of being able to be accessed in two ways: through Script Tags or through NPM installation.
Key features of TensorFlow:
- It’s an open-source library
- It could run on GPUs and CPUs.
- It can be accessed through tags or through installation of NPM.
- Ready-to use library wherein user doesn’t need to have much knowledge of the algorithm at back-end.
- Easy debugging
- High-level APIs
- Robust ML production anywhere
- Powerful experimentation for research
Most popular companies using TensorFlow are aribnb, Google, intel, Twitter, DeepMinds.
To install and import the TensorFlow library, following commands must be executed (in Python):
!pip install tensorflow
Import tensorflow as tf
One of the most extensively used machine earning frameworks is Scikit learn library. It offers a set of fast tools for machine learning and statistical modelling, such as classification, regression, clustering, and dimensionality reduction, via a Python interface. This mostly Python-written package is based on NumPy, SciPy, and Matplotlib.
Some of the major features of Scikit learn library are:
The main focus in scikit learn library is towards modeling of data. Various popular groups of models that are provided by the Scikit learn learn are:
- Supervised machine learning algorithms: All the machine learning algorithms such as Linear Regression. Decision Trees, SVM, Naïve Bayes, Neural Networks, Random Forests, etc. are the part of Scikit learn library.
- Unsupervised machine learning algorithms: Along with the supervised, unsupervised learning algorithms such as K-means, PCA, Factor analysis, etc. Are also the part of Scikit learn library.
- The other models include of clustering, cross-validation, dimensionality reduction, feature extraction, ensemble methods and much more are the part of Scikit learn framework.
Scikit learn machine learning framework is most commonly used for data analysis and data mining purposes. It is an open- source machine learning framework which is completely free of costs to use. It is a beginner friendly machine learning framework wherein it provides complete documentation of how to use it. Additionally, it allows developers to adjust any pre-set parameters for algorithms while they are in use or running. The framework is highly suited for unsupervised as well as administrative calculations.
To install and import the Scikit learn library, following commands must be executed (in Python):
!pip install scikit-learn
PyTorch is an open-source machine learning library. It is widely and most popularly used for development and training of the neural network based deep learning models. It also supports cloud-based software development and also supports Lua for the development of User Interface. The basis of this library is Torch and Caffe2 frameworks. It consists of almost all the optimization algorithms in machine learning. The large organizations already using PyTorch are Meta and IBM. Its front-end development is hybrid, making it adaptable for constructing computational graphs. The platform was created to be Python-compatible and compatible with libraries like as Numba and Cython. Initially it was developed by the Facebook.
Some of the key features of PyTorch library includes:
- Open-source and completely free framework
- It is very well suitable for building and developing all the machine learning models specially the neural networks and NLP.
- They could be used to design computational graphs
- Supports cloud-based software development.
- Easy debugging
- Easy to learn