Machine Learning Libraries

While working in the field of Machine Learning and Deep Learning for a long time, I found a lot of libraries, some of them we use very often and others we hardly use anymore, but knowing about them will still very helpful. There are multiple articles stating same old libraries, although they are quite good but generally they are not prominent. We can divide the libraries in three parts:

1. ML Model libraries.
2. Preprocessing and helper libraries.
3. Data visualization libraries.
4. MLops libraries.

ML model libraries :

• Transformer: 🤗 transformers is a library maintained by Hugging Face and the community, for state-of-the-art Machine Learning for Pytorch, TensorFlow and JAX. It provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. (click here to know more)
• Ultralytics: Ultralytics is an open-source Python library designed for facilitating computer vision research and development. It offers a comprehensive suite of tools for training, evaluating, and deploying state-of-the-art deep learning models for various vision tasks. (click here to know more)
• Scikit-learn: built on NumPy and SciPy, is a renowned machine learning library supporting a wide range of supervised and unsupervised learning techniques, as well as data mining and analysis. (click here to know more)
• Tensorflow 2.0 and Keras: Developed by Google these libraries are among the best in the world, it is easy to use. (click here to know more)
• PyTorch: Amazing Model developing libraries by Meta, it is most used modeling library in the world of A I. (click here to know more)
• timm: timm, also known as pytorch-image-models, is an open-source collection of state-of-the-art PyTorch image models, pretrained weights, and utility scripts for training, inference, and validation. (click here to know more)

Preprocessing libraries

• Torchio: TorchIO is an open-source Python library for efficiently loading, preprocessing, augmenting, and patch-based sampling of 3D medical images in deep learning, designed to align with PyTorch principles. (click here to know more)
• Albumentation: Albumentations is a powerful and flexible image augmentation library for machine learning, primarily used in computer vision tasks. It provides a wide range of augmentations, including geometric transformations, color adjustments, and advanced techniques like mixup and cutout. (click here to know more)

Data visualization libraries

• Matplotlib: a powerful data visualization library, enables the creation of a wide range of plots, charts, and histograms, making it a staple tool for visualizing data from NumPy, SciPy, and pandas
• Seaborn: leveraging pandas data structures and built on matplotlib, specializes in data visualization and is frequently employed in machine learning for generating informative plots.
• Plotly: Plotly is an open-source Python library for creating interactive, publication-quality graphs and dashboards. It provides a versatile platform for visualizing data in various forms, including scatter plots, line plots, bar charts, and more.
• Pandas, another library built on NumPy, simplifies the preparation of high-level datasets for training and machine learning tasks, enabling efficient data manipulation and analysis.

That's all for today see you soon