Python
10 Python Libraries for Machine Learning You Need in Your Toolkit
Elevate your machine-learning game in 2023!
As we enter 2023, it’s time to gear up for a new year of machine learning. And what better way to start than by adding some powerful Python libraries to your toolkit?
This article will introduce you to 10 essential Python libraries that will take your machine-learning skills to the next level.
From statistical modeling to unifying ML frameworks, these libraries will help you streamline your workflow and boost efficiency and speed.
Let’s dive in.
1. Statsmodels
Statistical modeling and econometrics in Python
Statsmodels provides a wide range of statistical and econometric tools for data analysis. It is particularly useful for estimating and testing statistical models and includes functions for linear regression, generalized linear models, time series analysis, and other types of statistical analysis.
Statsmodels also includes a suite of diagnostic tools for checking the assumptions of statistical models and tools for model selection and evaluation. In addition, Statsmodels provides several visualization tools for creating publication-quality plots and graphs.
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2. jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
JAX by Google allows users to easily and efficiently perform mathematical operations on arrays, including linear algebra and differentiation. It is particularly useful for machine learning and scientific computing, as it allows users to perform gradient descent and other optimization algorithms seamlessly.
JAX is highly efficient, using just-in-time (JIT) compilation and hardware acceleration (e.g., via GPU acceleration) to speed up computations. In addition, JAX allows users to easily switch between running their code on the CPU, GPU, or TPU (Google’s custom tensor processing unit) without requiring changes to the code itself. This makes it an ideal tool for quickly prototyping and deploying machine learning models in a production environment.
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3. fastai
Powerful deep learning made easy
fastai is a Python library for building and training deep learning models. It is built on top of the PyTorch library and is designed to provide a high-level interface for working with deep learning models. fastai is particularly useful for rapid prototyping and development, as it provides several tools and features that make it easy to build and train complex neural networks.
Some of the key features of fastai include support for a wide range of architectures and training techniques, automatic differentiation, and data loading and preprocessing tools. fastai is also known for its focus on practicality and ease of use, making it a popular choice for researchers and practitioners in deep learning.
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Want to learn about PyTorch and computer vision? Check out our article on building an image classification model from scratch using PyTorch.
4. lightning
Train, deploy, and ship AI products Lightning fast
Lightning (formerly PyTorch Lightning) is a lightweight PyTorch wrapper for high-performance and scalable deep-learning research. It was developed to help researchers and developers easily and quickly build and train deep learning models while still allowing them to use the full power of the PyTorch framework.
Lightning is designed to be easy to use and highly modular, allowing users to mix and match different components to build customized training and validation loops. In addition to building models, you can now build lightning apps that glue together everything around the models without the pain of infrastructure, cost management, scaling, and everything else.
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5. Jina
Build multimodal AI services via cloud-native technologies
Jina is an MLOps framework that empowers anyone to build multimodal AI services via cloud-native technologies. It uplifts a local PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer.
Applications built with Jina enjoy the following features: universal as it supports all mainstream deep learning frameworks, performant with its intuitive design pattern for high-performance microservices, cloud-native with seamless Docker container integration, and improved engineering efficiency thanks to the Jina AI ecosystem.
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6. MXNet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler
MXNet is a deep learning framework designed to be flexible, efficient, and scalable. It is widely used for research and production applications and has been adopted by many organizations, including Amazon, Microsoft, and Baidu.
MXNet is implemented in various programming languages, including Python, R, Julia, and C++. It offers a range of features that make it suitable for various applications, including support for distributed training, automatic differentiation, and flexible data loading. MXNet is designed to be easy to use, with a high-level API that allows developers to quickly build and deploy deep learning models and a low-level API that gives them more control over the details of the model implementation.
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What were the top ML frameworks used by Data Scientists this year? Read our article here to find out.
7. Ludwig
Data-centric declarative deep learning framework
Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system. Ludwig is suitable for a wide variety of AI tasks and is hosted by the Linux Foundation AI & Data.
The configuration declares the input and output features with their respective data types. Users can also specify additional parameters to preprocess, encode, and decode features, load from pre-trained models, compose the internal model architecture, set training parameters, or run hyperparameter optimization. Ludwig will build an end-to-end machine learning pipeline automatically, using whatever is explicitly specified in the configuration, while falling back to smart defaults for any parameters that are not.
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8. Skorch
A scikit-learn compatible neural network library that wraps PyTorch
The goal of skorch is to make it possible to use PyTorch with sklearn. This is achieved by providing a wrapper around PyTorch with a sklearn interface. scorch does not re-invent the wheel, instead getting as much out of your way as possible. If you are familiar with sklearn and PyTorch, you don’t have to learn any new concepts, and the syntax should be well known. (If unfamiliar with those libraries, it is worth getting familiarized.)
Additionally, skorch abstracts away the training loop, making many boilerplate codes obsolete. A simple net.fit(X, y) is enough. Out of the box, skorch works with many types of data, be it PyTorch Tensors, NumPy arrays, Python dicts, and so on. However, if you have other data, extending skorch is easy to allow for that.
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9. Ivy
The Unified Machine Learning Framework
With Ivy, you can run any code, in any pipeline, with any backend or hardware.
Take any code that you’d like to include. For example, an existing TensorFlow model and some useful functions from both PyTorch and NumPy libraries. Choose any framework for writing your higher-level pipeline, including data loading, distributed training, analytics, logging, visualization, etc. Choose any backend framework which should be used under the hood for running this entire pipeline. Choose the most appropriate device or combination of devices for your needs.
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Have a data science interview coming up? Here are 22 Essential Data Science Interview Questions to help you get prepared!
10. Thinc
A functional take on deep learning, compatible with your favorite libraries
Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow, and MXNet. You can use Thinc as an interface layer, a standalone toolkit, or a flexible way to develop new models.
Previous versions of Thinc have been running quietly in production in thousands of companies via spaCy and Prodigy. We wrote the new version to let users compose, configure and deploy custom models built with their favorite framework.
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That’s all for this article! I hope you discovered some cool new ML libraries to dive into and explore!
Thanks for reading!
Like this article? Here are three articles you may like:
- 10 Tips to Improve Your Regression Modeling
- Top Machine Learning Frameworks used by Data Scientists
- 40 Useful Pandas Snippets
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