[Chennaipy] August Meetup - Minutes

Mon Aug 29 05:20:32 EDT 2016

Anand,

Hope you are getting well now!

I gave my first talk (ah.. finally) after 3 meetups - though it was
unprepared. I encourage you to do the talks sometime. We would love to hear
from you - your thoughts and experiments with python.

Azeez

On 29 August 2016 at 14:31, Anand Surampudi <asinode at zoho.com> wrote:

> Azeez,
>
> You really made me feel so bad. You forced me to see how much I missed.
> Just kidding! ;-)
>
> But from your minutes, I seriously regret not making it yesterday as I was
> down with fever. That was very elaborate record of minutes and thanks a lot
> for initiating this. I will try to make use of the material that is
> hopefully going on github soon.
>
> Anand
>
> On Mon, Aug 29, 2016 at 10:57 AM, hafizul azeez <hafizul.azeez at gmail.com>
> wrote:
>
>> The non-stop drizzle, the quiet IMSc environment and vibrant pythonistas
>> set the context and expectations for the August meetup. However, plans took
>> unexpected turns when the speakers got delayed due to the drizzling rain
>> outside and the traffic created by it. Vijay took the stage to engage the
>> audience with round of introductions and a generic Q&A session on python
>> and the community. All of them took the opportunity to introduce themselves
>> and a few asked some interesting questions. With the speakers not turning
>> up yet, Vijay announced a lightning talk session.
>>
>> Rengaraj from Zilogic systems took the opportunity to present an idea he
>> was working with (DBus), explained the design and asked for feedback and
>> contributions. Kudos to Rengaraj - though it was a lighting talk, taking to
>> the stage with no slides and preparation within few minutes summons respect
>> and appreciation.
>>
>> An introduction to Flask by Hafizul Azeez
>>
>> As an emergency talk, Azeez gave a brief description of Flask and how it
>> can be used for rapid application development. Azeez highlighted the
>> difference between the micro web framework, Flask and how it is compared
>> with a batteries included framework like Django. He gave a brief demo of
>> how a simple Flask web app looks like and explained the code behind the app.
>>
>> He also made slight changes to the code with the inclusion of html
>> templates and how parameters can be passed from the client side to the
>> server side thru Flask routes a.k.a end points. In the process, he said how
>> the Flask framework supports a design pattern called MVT (Models, Views and
>> Templates) and how it all works in orchestration to make the web app.
>>
>> He also gave additional inputs on extending the Flask app with Plugins
>> and highlighted a few prominent plugins like FlaskWTF (for Forms),
>> Flask-SQLAlchemy (for databases), Flask-Login (for managing user logins,
>> authentications, session management and cookies) and few additional modules
>> (like Jsonify). Overall, the session received positive inputs considering
>> that it was planned to be a filler (till speakers arrive) lightning talk
>> but turned to be a 20 minute talk.
>>
>> This talk was followed by tea and networking. The cool weather outside
>> (something Chennai misses too often) and the hot tea and coffee inside
>> added energy to the already pumped up pythonistas. Getting to know new
>> people, shaking hands, answering queries, taking feedback accompanied with
>> good weather - whoa, just awesome! Speakers turned up sometime back and two
>> more talks to go as per schedule.
>>
>> Computer Vision with Deep Learning by Manish Shivanandhan
>>
>> Manish started with an introduction of deep learning and how machine
>> learning and deep learning differs. Machine learning is more of recognising
>> patterns and deep learning is more of learning about patterns. Manish
>> covered the different types of learning - supervised, unsupervised and
>> reinforcement and gave examples for each of these types; along with
>> classification and regression and provided real life examples (housing
>> prices, stock prices etc) to compliment the understanding.
>>
>> Coming to neural networks, Manish hinted various algorithms are used for
>> deep learning and one of them being Neural networks. He also deciphered as
>> to why Neural networks is getting so much traction these days!? - and
>> attributed it to the increasing computer processing power and the exploding
>> amounts of data.
>>
>> He also highlighted the use cases of Neural networks and its advantages
>> and limitations. Prominent examples being:
>> Computer vision - pattern recognition in images
>> Creative usage - generating text/music/speech
>>
>> One interesting exampling Manish gave is the JK Rowling (Author of Harry
>> Potter series) case and how Neural networks helped identify when one of her
>> books was written in another pen name (which was not JK Rowling). This
>> captivated the audience much more as this is some thing almost all of the
>> audience can correlate with. He also stressed the importance of Neural
>> networks in the health care domain in finding cure for diseases.
>>
>> He covered how neural networks can be used in Computer vision and deep
>> learning. He gave insights into how to take a problem and represent it in
>> numbers so that deep learning can be used. He also hinted that if any
>> problem can be represented in numbers, deep learning can be used. He demoed
>> with an image, flattening it and showing the numbers behind it and
>> highlighted that with enough numbers and processing power, patterns can be
>> learnt by Neural networks. He complimented that with the Prisma case study
>> where researchers took a lot of art manually, scanned it and fed neural
>> networks to learn how the great artists like Picaso would have painted the
>> picture (the brush strokes, the pressure applied etc). So when an image
>> (like selfie) is fed into the Prisma application, the computer generates
>> the art form of the image- i.e. how the image would look like if it was a
>> painting from Picaso and the likes. This further stressed how deep learning
>> can be used and how neural networks can be trained provided sufficient
>> clean data is fed into it.
>>
>> Finally, he gave an introduction to TensorFlow and its distinct abilities
>> when compared to other frameworks like Theano. Manish finished his talk
>> with resources and references for further exploration of Neural networks
>> and details about his upcoming webinar. Oh yes, he answered a lot of
>> questions on deep learning from an inquisitive audience who were awed by
>> the potential of deep learning and bitten by Manish's enthusiasm.
>>
>> Behaviour Driven Development by Naren Ravi
>>
>> Naren provided the background of the talk with a short description of
>> what Behaviour Driven Development (BDD) is all about - i.e. testing the
>> code with the user in mind and meeting the expectation of the stakeholders
>> rather than just testing the code.
>>
>> He started with the waterfall model, the advantages and it's limitations.
>> He gave insights into why testing in the later stages of the cycle makes
>> life difficult - if bugs encountered and to finally discover that the
>> design itself is flawed bringing up frustrations.
>>
>> He then covered how the first optimisation on the waterfall model was
>> done with testing the code and informing the development and how further
>> optimisation was done to the waterfall model with both testing and
>> construction (coding) done parallely. Though these optimisations were done,
>> Naren stated that there was an inherent disadvantage that was left with -
>> i.e. the design cannot be tested. The solution is to bring the design into
>> the development i.e testing, coding and design all tested parallely which
>> is the Test Driven Development (TDD).
>>
>> Naren then added that even TDD won't suffice as the requirement analysis
>> stage is completely left out. He then questioned the possibility of scope
>> (requirements) change and how the SDLC model would adopt it!? Bringing the
>> analysis cycle into the above cycle of testing, code and design becomes the
>> BDD, he concluded. This gave an overall picture of the BDD - testing (test
>> cases) first, construction (coding) and the design and finally checking if
>> all of it matches the requirements.
>>
>> He added that in some context, this is how lean startup works. Develop a
>> product with a new feature, send it to market, get feedback and then add a
>> new feature, send it to market, gauge the reactions and the cycle goes on.
>> Overall, it was a well structured talk starting with the traditional
>> waterfall model to TDD to BDD and what optimisations were made on the way.
>> He answered a few questions later to help bring more clarity into BDD.
>>
>> The meetup ended with Vijay thanking the venue and networking over tea
>> sponsors, speakers and the rest who made the meetup a successful event. He
>> also asked attendees to register in the mailing list to keep abreast of the
>> happenings in the Chennaipy community.
>>
>> Regards
>> Azeez
>>
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>>
>
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