Best Machine Learning Frameworks with A High Level Programming Language Julia
In the present era, thousands of organisations are switching to machine learning or artificial intelligence methodologies to improve their data storage patterns and overall improvement in the workflow as well.
However, Google was the first company to realize the importance of incorporating machine learning in business processes. And today more than 70% enterprises prefer to employ individuals who are working as machine learning professionals and familiar with the relevant knowledge and skills as well. If you are still not aware of machine learning then this blog is just for you so read it till the end, but at first let’s learn what machine learning is?
What is Machine learning?
Machine learning is a part of artificial intelligence (AI) that allows systems great learning ability for automatically functioning and improves with the experience without being explicitly programmed. Basically, machine learning focuses on the development of computer programs that can access data and use it to learn skills for themselves.
Machine learning framework in Julia
Machine learning is something that doesn’t stop at any given point, it keeps modifying its algorithms to suit the needs of its users better continuously. So let’s learn some machine learning frameworks that professionals use to improve business processes with the help of julia. Well, julia is a high level programming language, which is fast, flexible, easy-to-use, scalable, and supports high-speed mathematical computation. Also, supports all hardware, including GPUs and TPUs on every cloud network.
Flux
Flux is a deep machine learning library that provides a single, intuitive way to define models, just like mathematical notation. Any existing Julia libraries are differentiable and can be incorporated directly into Flux models. The intuitive features include compiled eager code, differentiable programming, GPU support, ONNX, among others.
Knet
Knet is a deep learning framework implemented in the Julia programming language. It help AI models to be defined by describing their forward computation in Julia language and allows them to do the use of loops, conditionals, recursion, closures, tuples, dictionaries, array indexing, concatenation and other language features as well. The library supports GPU operations and automates differentiation using dynamic computational graphs for models defined in plain Julia.
ScikitLearn.jl
ScikitLearn.jl is a Julia wrapper for the popular Python library Scikit-learn, which implements the Scikit-learn interface and algorithms in Julia. It helps the professionals of machine learning to uniform the interface for training and using models, as well as a set of tools for chaining (pipelines), evaluating, and tuning model hyperparameters.
So now it’s time to end this blog here, I hope it will help all the individuals to learn about some of the machine learning frameworks. In my opinion, individuals who are genuinely interested in this kind of career should take a look at the online IT training courses available at ProICT Training. This is the place where you can get advice from experienced IT professionals. Visit the website to read more about artificial intelligence training and certification courses.
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