[Chennaipy] August Meetup - Minutes

Tue Aug 30 02:23:31 EDT 2016

Thank you Azeez. I began learning Flask. I have been wanting to learn Flask
& Django since I learned about RoR becaming so famous and industry
application. I did not imagine my idea could make use of it.

Will update you once I find myself stuck.

Anand

On Mon, Aug 29, 2016 at 4:13 PM, hafizul azeez <hafizul.azeez at gmail.com>
wrote:

> Anand,
>
> It's easily doable. You can use Flask web framework to do it. You can send
> a request via ajax from the client (browser) to the server with the input
> being a random number between 1 and 100 (or the max number of quotes) you
> have in your db or for that matter in a text file.
>
> The server takes the request, checks the paramater (the random number) and
> picks the appropriate row id from the database and return it as json or as
> a python object which you can format (using jinja templates) and write to
> the DOM of the browser.
>
> I suggest you start with a Flask tutorial - which will give you a general
> idea : http://code.tutsplus.com/tutorials/creating-a-web-app-
> from-scratch-using-python-flask-and-mysql--cms-22972
>
>
> Thanks
> Azeez
>
> On 29 August 2016 at 15:59, Anand Surampudi <asinode at zoho.com> wrote:
>
>> Sure Azeeze. I will work on that. Thanks for your constant push.
>>
>> Meanwhile, can you or anybody suggest a resource for learning how to
>> achieve a small task in python. What I want to do is to build a web page
>> that randomly generates a quote on every click of a button. Lets just say I
>> want to host this page on github pages. I know how content-based github
>> pages work since I maintain my blog there. But this is something I want to
>> learn using github pages and python. This is it.
>>
>> I am sure this sounds pretty silly. But as a beginner, I would like to
>> give myself this kind of tasks for my learning.
>>
>> On script level, I can do it. I mean I run the script on terminal and it
>> definitely throws the random quote as an output. But I want the same thing
>> to happen on a web page, but random printing should happen on every click
>> of a button, say something like, "Surprise me!" or something.
>>
>> Thanks.
>> Anand
>>
>> On Mon, Aug 29, 2016 at 2:50 PM, hafizul azeez <hafizul.azeez at gmail.com>
>> wrote:
>>
>>> 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
>>>>>
>>>>> _______________________________________________
>>>>> Chennaipy mailing list
>>>>> Chennaipy at python.org
>>>>> https://mail.python.org/mailman/listinfo/chennaipy
>>>>>
>>>>>
>>>>
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