Julia is a popular programming language for data analysis, artificial intelligence, modeling, and simulation. The language combines Python's and R's simplicity of use with C++'s speed, as well as parallel computing features and also supports hardware like TPUs and GPUs. Julia allows high-speed mathematical calculation and is fast, general, composable, open source, reproducible and Dynamic. It is designed to work with the object-oriented programming paradigm. It is now widely used in Machine Learning and Deep Learning applications, such as computer vision and natural language processing (NLP).
With over 34.8 million downloads of the language, it has over 7,400 Julia packages registered for community usage. Various mathematics libraries, data processing tools, and general-purpose computer programs are among them. In this blog, we will be briefing you on 7 such prominent Machine Learning libraries of Julia in 2022.
Mocha.jl is a script created by Julia and for Julia. It provides a native Julia interface, allowing it to communicate with both core Julia functionality and external Julia packages. To execute or add('Mocha') in the Julia console, you don't require root rights or any other dependencies installed. Mocha.jl also has a GPU backend, which combines customized kernels with NVIDIA's very efficient libraries (cuBLAS, cuDNN, etc.). The GPU backend is fully compatible with Julia, and switching back and forth is as easy as altering one line of code. It has a modular architecture that makes it simple to compose, adapt, and extend. This also allows unit tests to be used to confirm the validity of all connections.
Flux.jl, sometimes popularly referred to as Flux, is a beautiful machine learning package written entirely in Julia. It gives researchers, industry practitioners, and developers lightweight abstractions on top of Julia's native GPU and automated differentiation support, allowing them to construct performant and flexible machine learning programs. Flux is built entirely in Julia, which allows it to have a minimal footprint while yet delivering a strong feature set and easy syntax. Flux was built to interact with other Julia packages, even if they weren't created with machine learning in mind. This enables developers to work at a level of productivity that is unrivaled. Flux is also widely utilized for applications such as neural ordinary differential equations. It may be scaled up to run on anything from a CPU to a massive cluster of GPUs, exactly like Julia's core programming language. Because of its Julia-only codebase and high-level API, Flux is simple to use while staying configurable, scalable, and fully connected with the Julia ecosystem and beyond. Furthermore, Flux is being developed by a global community of inclusive users and developers that encourage contributions of all kinds to the ecosystem.
Knet is a deep learning framework for models described in plain Julia, it provides GPU operation and automated differentiation utilizing dynamic computational graphs. Knet differentiates (nearly) any Julia code using dynamic computational networks built at runtime. This enables machine learning models to be developed by describing only the forward calculation (that is, the computation from parameters and data to loss) while utilizing Julia's full power and expressivity. Knet records basic actions during forwarding calculation to create a dynamic computational graph. For efficiency, just pointers to inputs and outputs are recorded. As a result, array overwriting during forwarding and backward passes is not supported. These features pushes Knet to be among the top Julian Libraries
In the last few years, machine learning experts and data scientists have flocked to the scikit-learn Python package. It includes a collection of tools for chaining (pipelines), assessing, and tweaking model hyperparameters, as well as a unified interface for training and utilizing models. Julia now has these features thanks to ScikitLearn.jl. Its main purpose is to bring together Julia and Python-defined models in the scikit-learn framework. It works perfectly in sync with Julia's models as well as those from the scikit-learn package. ScikitLearn.jl can be assumed to be a replica of the Python scikit-learn project, however, the API was developed for Julia and adheres to Julia's norms only.
Tensorflow is a well-known open-source python framework for creating machine and deep learning models whereas Tensorflow.jl is an open-source Julia wrapper for a machine learning framework. You usually have to wait for Python developers to write IO kernels in C when using Python TensorFlow, but with the Julia Tensorflow API, you can get quick data intake, especially for data in unusual forms. TensorFlow.jl can also be used for quick inference postprocessing, such as computing different statistics and visualizations that don't have a readymade vectorized implementation. Its multiple dispatch capability makes it simple to express models using Julia code that looks natural.
MLBase a Julia package offers machine learning applications with essential capabilities. When it comes to creating machine learning programs, it may be compared to a Swiss knife. In machine learning, we frequently need to assign an integer label to each class. The LabelMap type in this package represents the relationship between discrete data (such as a finite collection of strings) and integer labels. The classify function and its associates in this package are used to complete the second stage, which is to predict labels based on scores. This package also includes tools for evaluating a machine learning algorithm's performance. K-fold, LOOCV, and RandomSub are three cross-validation strategies implemented in this package.
It's a Julia-based deep learning framework for training deep neural networks. This package is underappreciated. This would be a mistake, as Merlin.jl is an enigmatic framework that can save a lot of time on a variety of occasions. Merlin, like Flux.jl, is typically lightweight and built entirely in Julia code. Merlin will generally outperform Flux in many tasks, but this does not mean that it will always be faster. With CUDA, it has implicit GPU support. In comparison to Flux, Merlin models are simple to implement. The library's goal is to create a machine learning package that is versatile, quick, and compact.
We've discussed the best 7 Machine Learning libraries in Julia for 2022 in this blog. Although, the different Julia Libraries mentioned here may have a different ranking based on different factors but mostly the list of top 7 Julia Libraries will include these libraries only.