<commit_before>---
layout: post
title:  "Blog 702: Machine Learning Developer Summit 2020"
date:   2020-01-22 00:11:31 +0530
categories: jekyll update
mathjax: true
---

# Content

1. TOC
{:toc}
---

[Machine Learning Developer Summit 2020](https://www.mlds.analyticsindiasummit.com/)

# Day 1

## Talk 1: Amazon Web Service

**Speaker:** <NAME> from Amazon

- Advertising AWS and promoting AIML
- Using SHAP analysis for XAI
- Sagemaker Studio IDE
- Amazon Fraud Detection
- Amazon Kendra (Search result with context)

----

## Talk 2: Building Multi-Tenant NLP System by scale  

**Speaker:** <NAME> from Freshworks

Bio: ML Engineer

Problem: Reduce Ticket Assignment and Resolution Time using NLP technique

- NLP Model Deployment in Production
  - Challenge: Different customer has different vocabulary which they need to take care of
- Offline training
   
![image](/assets/images/MLDS2020_day_1/Navaneethan_1.jpg)

- Online Prediction

![image](/assets/images/MLDS2020_day_1/Navaneethan_2.jpg)

---- 


## Talk 3: Building Multi-Tenant NLP System by scale  

**Speaker:** <NAME> from PayTm

Bio: Engineer

- AI in PayTm

----

## Talk 4: Stress Detection using Deep Learning

**Speaker:** <NAME>

Bio: Engineer

- Use EEG data
- Two types of brain waves $\alpha$ and $\beta$ and their frequencies get changed during `relaxed` and `stressed` state.

- Supervised Learning using labelled data
- Collect data using EEG + exterrnal data from Kaggle
- Convert the data using FFT
  - Note: If Frequency of data is high (GHz or MHz) then use FFT, else use Wavenet
- Apply embedding
- Feed into LSTM with attention

**Application:**

- Music generation (Using GAN) based on stress level
- Emotion detection for stressed patient
- Apply facial features, body gestures (image data of the candidate along with the EEG data) for improving the model

----

## Talk 5: Differentiable Programming

**Speaker:** <NAME>

**Bio:** Co-Creator of Julia Language

<img src="https://docs.julialang.org/en/v1/assets/logo.png" alt="Simply Easy Learning" height=100>

>> Julie is for JUlia, by Julia, of Julia

- It's fast compared to Python, R, Matlab as Julia is `multi-threaded` unlike others.
- It's compiler is written in Julia itself which makes it easier for developer to debug and contribute easily. Nothing like under the hood written in separate language, like Python. In Python, lots of packages are written in C or C++. But that's not the case with Julia.
- Julia uses the concept of `Self Hosting Compiler`
  - The trick is bootstrapping. You first write a compiler for your language (or a subset thereof) in some other language.
  - Then you write a compiler for your language (or a large subset of the one you can already handle) in your language. You use the former compiler to compile the new compiler, and then the new compiler can compile itself.
    - `X` and `Y` 2 language 
    - `Compiler_X = Compiler_Y(Language_X)`
    - Now remove `Compiler_Y` 
    - `Compiler_X(Language_X)` 🚀

- Differentiable programming in Julia is very fast

For learning more about Julia, [click here](https://docs.julialang.org/en/v1/)

----

## Workshop: Julia

**Author:** 
  - <NAME>
  - Daairya
  - Deepak



**Learn Julia**
- Run Julia in browser using [juliabox](https://juliabox.com/)
- Learn Julia from [juliaacademy](https://juliaacademy.com/)
- Book: Think Julia

<img src="https://covers.oreillystatic.com/images/0636920215707/lrg.jpg" alt="Simply Easy Learning" height=300>

- Julia uses `multiple dispatcher` concept where methods are chosen based on the inputs of all the arguments.
- **Fun Fact 1:** OOP concept is based on `single dispatcher`
- **Fun Fact 2:** The underlying design principles of Julia are very elegant. For more details see the github issue [#4774](https://github.com/JuliaLang/julia/issues/4774)


![image](/assets/images/MLDS2020_day_1/julia.png)



----<commit_msg>Refactored<commit_after>---
layout: post
title:  "Blog 702: Machine Learning Developer Summit 2020"
date:   2020-01-22 00:11:31 +0530
categories: jekyll update
mathjax: true
---

# Content

1. TOC
{:toc}
---

[Machine Learning Developer Summit 2020](https://www.mlds.analyticsindiasummit.com/)

# Day 1

## Talk 1: Amazon Web Service

**Speaker:** <NAME> from Amazon

- Advertising AWS and promoting AIML
- Using SHAP analysis for XAI
- Sagemaker Studio IDE
- Amazon Fraud Detection
- Amazon Kendra (Search result with context)

<a href="#Top" style="color:#2F4F4F;background-color: #c8f7e4;float: right;">Content</a>

----

## Talk 2: Building Multi-Tenant NLP System by scale  

**Speaker:** <NAME> from Freshworks

Bio: ML Engineer

Problem: Reduce Ticket Assignment and Resolution Time using NLP technique

- NLP Model Deployment in Production
  - Challenge: Different customer has different vocabulary which they need to take care of
- Offline training
   
![image](/assets/images/MLDS2020_day_1/Navaneethan_1.jpg)

- Online Prediction

![image](/assets/images/MLDS2020_day_1/Navaneethan_2.jpg)

<a href="#Top" style="color:#2F4F4F;background-color: #c8f7e4;float: right;">Content</a>

---- 


## Talk 3: Building Multi-Tenant NLP System by scale  

**Speaker:** <NAME> from PayTm

Bio: Engineer

- AI in PayTm

<a href="#Top" style="color:#2F4F4F;background-color: #c8f7e4;float: right;">Content</a>

----

## Talk 4: Stress Detection using Deep Learning

**Speaker:** <NAME>

Bio: Engineer

- Use EEG data
- Two types of brain waves $\alpha$ and $\beta$ and their frequencies get changed during `relaxed` and `stressed` state.

- Supervised Learning using labelled data
- Collect data using EEG + exterrnal data from Kaggle
- Convert the data using FFT
  - Note: If Frequency of data is high (GHz or MHz) then use FFT, else use Wavenet
- Apply embedding
- Feed into LSTM with attention

**Application:**

- Music generation (Using GAN) based on stress level
- Emotion detection for stressed patient
- Apply facial features, body gestures (image data of the candidate along with the EEG data) for improving the model

<a href="#Top" style="color:#2F4F4F;background-color: #c8f7e4;float: right;">Content</a>

----

## Talk 5: Differentiable Programming

**Speaker:** <NAME>

**Bio:** Co-Creator of Julia Language

<img src="https://docs.julialang.org/en/v1/assets/logo.png" alt="Simply Easy Learning" height=100>

>> Julie is for JUlia, by Julia, of Julia

- It's fast compared to Python, R, Matlab as Julia is `multi-threaded` unlike others.
- It's compiler is written in Julia itself which makes it easier for developer to debug and contribute easily. Nothing like under the hood written in separate language, like Python. In Python, lots of packages are written in C or C++. But that's not the case with Julia.
- Julia uses the concept of `Self Hosting Compiler`
  - The trick is bootstrapping. You first write a compiler for your language (or a subset thereof) in some other language.
  - Then you write a compiler for your language (or a large subset of the one you can already handle) in your language. You use the former compiler to compile the new compiler, and then the new compiler can compile itself.
    - `X` and `Y` 2 language 
    - `Compiler_X = Compiler_Y(Language_X)`
    - Now remove `Compiler_Y` 
    - `Compiler_X(Language_X)` 🚀

- Differentiable programming in Julia is very fast

For learning more about Julia, [click here](https://docs.julialang.org/en/v1/)

<a href="#Top" style="color:#2F4F4F;background-color: #c8f7e4;float: right;">Content</a>

----

## Workshop: Julia

**Author:** 
  - <NAME>
  - Daairya
  - Deepak



**Learn Julia**
- Run Julia in browser using [juliabox](https://juliabox.com/)
- Learn Julia from [juliaacademy](https://juliaacademy.com/)
- Book: Think Julia

<img src="https://covers.oreillystatic.com/images/0636920215707/lrg.jpg" alt="Simply Easy Learning" height="300">

- Julia uses `multiple dispatcher` concept where methods are chosen based on the inputs of all the arguments.
- **Fun Fact 1:** OOP concept is based on `single dispatcher`
- **Fun Fact 2:** The underlying design principles of Julia are very elegant. For more details see the github issue [#4774](https://github.com/JuliaLang/julia/issues/4774)


![image](/assets/images/MLDS2020_day_1/julia.png)



----

<a href="#Top" style="color:#023628;background-color: #f7d06a;float: right;">Back to Top</a>
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