PyCon Singapore 2016
2016-06-23
Singapore, Singapore
| title | author | length |
|---|---|---|
| Keynote: How to Write a Popular Python Library by Accident | DANIEL ROY GREENFELD | 01h 11m |
Explains how tiny, copy‑paste snippets become real libraries by showing the exact tools and steps used: Cookiecutter template to generate package boilerplate… | ||
| Warpdrive, making Python web application deployment magically easy | Graham Dumpleton | 41m |
Warpdrive automates the tedious steps: it creates a virtualenv, installs requirements, runs Django migrations and collectstatic, and captures setup commands so… | ||
| Design and implement a scalable and high performance tcp server with tornado | Nhu Dinh Tuan | 30m |
Uses Tornado as the network layer and splits work into a single-threaded network module that accepts connections and multiple worker processes that handle… | ||
| Using Artificial Life Simulation to Gain Insights into Contradictory Field Evidence | Maurice Ling | 32m |
Speaker: Maurice Ling
Description
Antibiotics resistance is a serious biomedical issue and there are contradictory results on the prevalence of resistance… | ||
| Visualising Machine Learning Models | Amit Kapoor | 43m |
Shows concrete techniques and a small prototype that actually plots models against data, inspects parameter sweeps and fitting dynamics, and maps decision… | ||
| Sentiment Analysis, its techniques and applications | Mimansa Jaiswal | 39m |
Overview of practical sentiment work using NLTK movie reviews corpus for a Naive Bayes baseline that reaches about 72% accuracy, moving to distributed… | ||
| Deep Learning Hands-on Workshop | Martin Andrews | 01h 34m |
Hands-on setup hands out a pre-configured VirtualBox VM that boots Fedora and serves Jupyter notebooks with runnable Theano experiments; notebooks use Theano +… | ||
| Using Python to Build a GIS Data Pipeline for Rural-Urban Classification | Tung Whye Loon | 41m |
Python pipeline downloads map tiles, stitches and clips them, thins road rasters and polygonizes to vectors using GDAL and image thinning with Pillow, then… | ||
| Deep Learning With Python & Tensorflow | Ian Lewis | 49m |
Shows how to build and train models with TensorFlow (Python), including a live MNIST demo that uses 28x28 = 784 input tensors, one hidden layer and 91%… | ||
| Python Pants - A build system for large codebases with multiple dependencies | Angad Singh | 28m |
Pants is presented as a build system for large monorepo Python codebases that produces immutable PEX files containing all Python deps, demonstrated by building… | ||
| Building a simple-to-use Database Management tool | Chinab Chugh | 41m |
Jublia builds a two‑way sync that treats Google Sheets as a live mirror of an SQL backend, using the Google Sheets API plus a central middle layer to batch… | ||
| Python Robotics for Education | Max Ong Zong Bao | 24m |
A hands on classroom robot built from a $35 Raspberry Pi runs simple Python that exposes PWM motor control, RPi.GPIO pin reads, an ultrasonic obstacle sensor… | ||
| Keynote | Yves J. Hilpisch | 01h 18m |
Argument: open source code, open data APIs and cloud compute let finance move from closed, slow toolchains to reproducible, fast workflows. Examples: Python +… | ||
| Customer Segmentation in Python | Mao Ting | 35m |
Walks through concrete Python code (pandas, scikit-learn, scipy) on open booking data: loads CSV, cleans anomalies, computes booking rates per channel (Channel… | ||
| music2vec: A smart music recommendation and discovery | Laam Pham | 38m |
Uses a million playlists and ~300k song IDs to train a Python implementation that maps songs, playlists, albums and users into a single vector space; combines… | ||
| Lightning Talk: Bitcoin mining on Python | Yves J. Hilpisch | 14m |
Shows a concrete Python implementation using hashlib (md5 for demo, double SHA-256 for Bitcoin) that iterates a numeric nonce and recomputes the header hash… | ||
| Lightning Talk: Openshift | Graham Dumpleton | 08m |
OpenShift is now a Docker-based platform that builds and runs a Wagtail Django app plus Postgres from a template in under five minutes; the demo shows source… | ||
| Lightning Talk: Pycon Malaysia | Swee Meng | 03m |
Brief pitch for PyCon Malaysia: a regional conference with a tutorial day followed by two conference days Aug 26–28, tickets listed at S$50, sessions include a… | ||
| Lightning Talk: Python Decorators | Graham Dumpleton | 03m |
Decorators commonly break introspection lost — name and __doc__ are not preserved — and simple wrappers fail for methods, classmethods and staticmethods. The… | ||
| Lightning Talk: Singapore Python User Group | Martin Brochhaus | 07m |
Local community overview lists the official site pugs.sg, a Facebook group with ~2,300 members but ~20 really active posters, and monthly meetups with RSVPs… | ||
| Lightning Talk: Software Carpentry | Ivan Zimine | 05m |
Software Carpentry is a nonprofit running 2–3 day workshops with ready-made, reusable lesson plans that claim to teach scientists practical skills: Unix shell… | ||
| Lightning Talk: Sprints | DANIEL ROY GREENFELD | 05m |
Explains what a Sprint actually looks like: a Sprint kickoff with 30‑second project pitches, lots of bite-sized issues created on GitHub for new contributors… | ||
| Creating map visualizations with open data and Folium | Grace Tang | 30m |
Project uses Folium to build an interactive map of 101 MRT points from LTA shapefiles, converts SVY21 coordinates to lat/lon, geocodes URA and HDB transactions… | ||
| Machine Learning Pipeline using Luigi and Scikit Learn | Atreya Biswas | 35m |
Builds a Luigi-orchestrated ML pipeline that uses Scikit-Learn to train a model, persists intermediate CSV states and enforces atomic file operations, and… | ||
| Python in the MicroServices world | Hari Krishnan | 44m |
Built an all‑Python microservice (a simple lookup pulled out of a monolith) using Flask + Redis for metadata, then moved metadata into a separate store… | ||
| Deploying scalable Django application on AWS | Lalit Nama | 30m |
Step-by-step recipe: move the Django DB to RDS MySQL, send static and uploads to S3 for static and media, put servers on EC2 behind an ELB with an Auto Scaling… | ||
| A tale of Ansible fanboy | Pawan Darda | 28m |
Shows how Ansible runs real AWS scale operations: control via no agents or daemons and SSH key based control, with human-readable playbooks, inventory files… | ||
| nothing matches | ||