SimpleAI

Further Reading-

1. Predictive test selection by Facebook AI-- https://research.fb.com/publications/predictive-test-selection/
2. Deep learning to translate between programming languages by facebook AI- https://ai.facebook.com/blog/deep-learning-to-translate-between-programming-languages/

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This is episode 9 and today we are going to talk about an interesting library in the Julia programming language ecosystem.

The library about which I am going to talk is known as "Transducers". So What is this package named Transducers?

I first heard this term in the Julia language community. There is a package in the Julia ecosystem known as "transducers". As per the readme file of this particular package the definition is as follows-

"Transducers are transformations of "sequence" of input that can be composed very efficiently. The interface used by transducers naturally describes a wide range of processes that is expressible as a succession of steps." In short Transducers can be thought of as software which takes in some input in sequence and then it transforms them into sort of pipeline.

To know more about transducers I looked up the origin of this concept.

My search led me to a blog post by "Rich Hickey' who is the creator of the "Clojure" programming language. This particular blog post was dated August 6,2014 where he had laid down the idea of transducers for the "Clojure" programming language. The equivalent package in Julia is the port of the transducers from the Clojure ecosystem.

In this elaborate blog post, Rich Hickey gives a very simple explanation of a 'transducer'. Which is as follows--

"Transducer is a function that takes one reducing function and returns another".

Building on this definition we can further simplify our earlier description of transducers to this--

" A transducer takes in a function, computes it's output, combines the function with another input in the sequence (which is a fucntion) and gives back a new reducing function."

So, this resembles a pipeline but the Real power of transducers comes from the fact that they are unaware of the input being fed to them and the job which the input objects are supposed to do.

This is a very thoughtful design due to which there is an inherent decoupling present in the Transducers, they have this plug and play ability which makes it easier to customize this in any package/project.

So, this was all about transducers and why I find them interesting.

References

https://cognitect.com/blog/2014/8/6/transducers-are-coming

https://juliafolds.github.io/Transducers.jl/dev/

How To Show Your Support To My Work🤗

Creating a content requires a lot of research, planning, writing and rewriting . This is important because I want to deliver practical content to you without any fluff.

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I often hear this-->.

1) How do I find project for practicing deep learning?
2) Where should I look for projects to which I can apply deep learning?
3) I want to implement a deep learning project in my organization but i can not find a suitable project?

The answer to all this question is to try out something in your own domain. The domain where you expertise belongs to.

You might be a lawyer, a chemist or a software test analyst but there are tons of problem areas in your domain where there are chances of applying deep learning.

Problem areas where your traditional approaches fail or are simply not sufficient enough.

This is the topic which we will be exploring in this episode.

How To Show Your Support To My Work🤗

Creating a content requires a lot of research, planning, writing and rewriting . This is important because I want to deliver practical content to you without any fluff.

If you like my work and my content and want to support me then the following are the ways to show your support →

Transformers are a familiar name in NLP related tasks. If you search over the internet for the term NLP the only neural network architecture that you would find would be Transformers based or at-least that’s what my experience have been.

Yet, Transformers have only been showing off their powers in the NLP area and their impact on computer vision problem was next to none. At least till the month of May’20.

"Transfer learning". This concept has revolutionized deep learning, but what is it actually ? This is what we are going to explore in today's episode. Today's episode will provide you a soft and practical introduction to the concept of transfer learning.

The concept of transfer learning is very practical and effective as it minimizes the need of big data for training neural networks. This is the reason why I have created my deep learning course entirely using Transfer learning. 

Machine Learning is the hottest buzz word today. These days you cannot go around anywhere without hearing this word or without coming across a device or service where you won’t find machine learning being used. It’s in your phone, it’s in your television, it’s in your speakers , in your cars and literally everywhere. But, how does this technology works, how does a machine “learns” something? This is what we shall explore in today's episode.

Before I start any further I would like to request you to bear with me if you notice any glitches in audio quality or any form of grammatical errors in this podcast. I am just starting out and learning the tools of the trade so it is possible that there might be some misses while recording this episode. However, I am trying to improve myself with each episode and you can expect better quality in upcoming episodes.