Saturday, March 6, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Machine Learning

Here is How Julia Computing Is Making AI And ML Better

March 18, 2019
in Machine Learning
Here is How Julia Computing Is Making AI And ML Better
587
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

Credit: Google News

You might also like

Explainable Machine Learning, Model Transparency, and the Right to Explanation « Machine Learning Times

How to Boost Machine Learning in Healthcare Market Compound Annual Growth Rate (CAGR)? – KSU

Comprehensive Report on Machine Learning Market 2021 | Size, Growth, Demand, Opportunities & Forecast To 2027



Python and R are popular for applications of machine learning. But in recent years, Julia is acquiring its place and has become the new de-facto for machine learning. Is the language really going to take over our old, trusted Python and R, in the machine learning world?  According to Julia Computing, Julia offers the best-in-class support for modern machine learning frameworks such as TensorFlow and MXNet, making it easy to adapt to existing workflows. In 2017, during JuliaCon, Mike Innes gave an overview of Flux.jl, a Julia package that expands Julia’s flexibility in ML use cases. He demonstrated how the programming language provided lightweight abstractions on top of Julia’s native GPU and Automatic Differentiation support, while remaining fully hackable. The language was built keeping in mind the high-performance numerical model analysis required for machine learning applications and is therefore very suitable for machine learning applications.

Why Julia For Machine Learning?

According to a survey by Analytics India Magazine and Great Learning, Python and R are the most popular languages for data science and ML, both used by 44 percent and 35 percent of the survey audiences respectively. When it comes to Julia, it is not necessarily looked upon as a language for machine learning, probably because it’s new in the market and is hence not well established as the other two.



Advertisement


Since Julia is a new language built with a mission to overcome the drawbacks faced in the other languages today, it has the best parts of other languages like Python, R, Matlab, SAS and C. It is easy to write mathematical symbols in Julia, which is what ML has in a lot of amount. With packages like ArrayFire, generic code can be run on GPUs. With just a little code, Julia can run effectively and also faster than other languages like Python and R. Using TensorFlow in Julia rather than any other language has an advantage of the code looking much simpler. For example, one doesn’t have to do tf.while_loop or tf.loop to introduce a loop. During JuliaCon 2017, in a talk by Jonathan Malmaud, the MIT researcher working on cutting edge machine learning technologies, demonstrated how Julia’s interfaces to popular Machine Learning frameworks makes it seamless to use, and showed this with an example of Julia’s TensorFlow.jl.

Julia’s multiple dispatch prototype allows the many object-oriented and functional programming patterns easy to express and easy to change the behaviour of functions based on the run time’s state of more than one of its arguments. It is a best fit to define the number and array-like data types. Another feature of Julia is its automatic garbage collection, which is a collection of libraries for mathematical calculations, linear algebra, random number generation and regular expression matching, which adds to its advantage in ML.

Its scalability makes it easier to be deployed quickly at large clusters, again something crucial for ML programmers. Its powerful tools like MLBase.jl, Flux.jl, Knet.jl are of great use to ML. Moreover, it has tools like ScikitLearn.jl, TensorFlow.jl and MXNet.jl, apt for ML applications. A few lines of code goes a long way in Julia. Julia is a high-level language, that gives performance of a low-level language. So you code with an ease of Python but performance ease like C and C ++.

A common workflow in many organisations is that, when you try to run models in languages like Python and R, you eventually end up running them in C or C++. This is common in the banking sector and is also seen in other industries like that of pharmaceuticals. Julia aims to combine these two worlds and try to avoid two languages, high-level for writing the code and low-level for running, for one task. It aims to solve this ‘two-language’ problem.

Applications

Julia today has a huge variety of applications. Here are some of its use cases:

1.Aviva, one of the biggest insurance firms in the UK, uses Julia to solve their complex Monte Carlo risk models and they have found that they are a thousand times faster than they were before with other language.

2.NY Fed which is a central bank in the US uses Julia to do ML modelling, which are very large and used to understand the US economy. They have published many papers to show how they use Julia for this application.

3.The Federal Aviation Authority (FAA) in the US who are the regulators for all flights in the US have a system to sense aircraft nearby. The specification now has a text but the next generation will use Julia as a specification language, so the specification would be much more accurate in code, rather than in text. Julia is perfect in applications like this, it being a high and a low level language.

4.Diebetic retinopathy, a disease affecting more than 126 million diabetics and accounting for more than 5 percent of blindness cases worldwide, is using ML to predict eye disease. Julia Computing, along with IBM analysed eye fundus images provided by Drishti Eye Hospitals in Bangalore, and built a deep learning solution that provides eye diagnosis and care to thousands of rural Indians.

 


Related

Provide your comments below

comments


Credit: Google News

Previous Post

IMPACT - Part 2: Be a problem solver first, an engineer second.

Next Post

A Beginner’s Guide to Big Data and Blockchain

Related Posts

The ML Times Is Growing – A Letter from the New Editor in Chief – Machine Learning Times
Machine Learning

Explainable Machine Learning, Model Transparency, and the Right to Explanation « Machine Learning Times

March 5, 2021
How to Boost Machine Learning in Healthcare Market Compound Annual Growth Rate (CAGR)? – KSU
Machine Learning

How to Boost Machine Learning in Healthcare Market Compound Annual Growth Rate (CAGR)? – KSU

March 5, 2021
Comprehensive Report on Machine Learning Market 2021 | Size, Growth, Demand, Opportunities & Forecast To 2027
Machine Learning

Comprehensive Report on Machine Learning Market 2021 | Size, Growth, Demand, Opportunities & Forecast To 2027

March 5, 2021
2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms
Machine Learning

2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms

March 5, 2021
UVA doctors give us a glimpse into the future of artificial intelligence
Machine Learning

UVA doctors give us a glimpse into the future of artificial intelligence

March 5, 2021
Next Post

A Beginner’s Guide to Big Data and Blockchain

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Zigbee inside the Mars Perseverance Mission and your smart home
Internet Security

Zigbee inside the Mars Perseverance Mission and your smart home

March 6, 2021
Mazafaka — Elite Hacking and Cybercrime Forum — Got Hacked!
Internet Privacy

Mazafaka — Elite Hacking and Cybercrime Forum — Got Hacked!

March 6, 2021
Autonomous Cars And Minecraft Have This In Common  
Artificial Intelligence

Autonomous Cars And Minecraft Have This In Common  

March 5, 2021
The ML Times Is Growing – A Letter from the New Editor in Chief – Machine Learning Times
Machine Learning

Explainable Machine Learning, Model Transparency, and the Right to Explanation « Machine Learning Times

March 5, 2021
FTC joins 38 states in takedown of massive charity robocall operation
Internet Security

FTC joins 38 states in takedown of massive charity robocall operation

March 5, 2021
Google Cloud Certifications — Get Prep Courses and Practice Tests at 95% Discount
Internet Privacy

Google Cloud Certifications — Get Prep Courses and Practice Tests at 95% Discount

March 5, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Zigbee inside the Mars Perseverance Mission and your smart home March 6, 2021
  • Mazafaka — Elite Hacking and Cybercrime Forum — Got Hacked! March 6, 2021
  • Autonomous Cars And Minecraft Have This In Common   March 5, 2021
  • Explainable Machine Learning, Model Transparency, and the Right to Explanation « Machine Learning Times March 5, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates