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 <asin...@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.az...@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@python.org >> https://mail.python.org/mailman/listinfo/chennaipy >> >> > > _______________________________________________ > Chennaipy mailing list > Chennaipy@python.org > https://mail.python.org/mailman/listinfo/chennaipy > >
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