PyCon APAC 2018
| title | author | length |
|---|---|---|
| Opening Keynote | Jordan Dea Mattson | 54m |
Many common datasets lack provenance and long-term consent; a concrete fix is explicit data-as-contract records (version tables that link processors, consent… | ||
| Teaching Computers ABCs: A Quick Intro to Natural Language Processing | Lory Nunez | 44m |
Builds two small apps: an information‑retrieval demo that pulls Wikipedia with Wikimedia + BeautifulSoup, vectorizes with tf-idf + cosine similarity via Gensim… | ||
| GloVe - Python for Word Representation | Loveme Felicilda | 31m |
Shows how to use glove-python (the C implementation wrapped for Python) with a window of 10 and 100–200 dimensions, including loading Stanford’s pretrained… | ||
| Better Care and Feeding of Machine Learning Models | Jeanne Choo | 20m |
Defines reproducibility as data plus code plus documentation and shows a working project template that uses cookiecutter-data-science, scripted downloads and… | ||
| Tracking the tracker: Time Series Analysis in Python From First Principles | Kenneth Emeka Odoh | 37m |
Focuses on forecasting and anomaly detection using online state‑space methods implemented in Python with Kalman / state‑space models and incremental parameter… | ||
| How to deploy machine learning models to production (frequently and safely) | David Tan | 35m |
Shows how to move a model from notebook to production with an automated CI/CD pipeline that trains, evaluates, versions artifacts in a GCP bucket model… | ||
| Closing Keynote | Yves J. Hilpisch | 51m |
An AI-powered trading machine is presented that standardizes data ingestion, backtesting and live execution for mid-frequency (5–10 minute) algo strategies… | ||
| Introduce Syntax and History of Python from 2.4 to 3.6 | Manabu Terada | 31m |
Covers concrete syntax and migration steps from Python 2.4 up through 3.6 and notes 3.7 features backported or upcoming, with code-level comparisons and… | ||
| Artisanal Async Adventures | Jonas Obrist | 33m |
Builds a small async network stack that actually runs: 100 lines of Python using Python 3.6 type hints, standard library sockets and select-based event loop… | ||
| Crypto-AI: A Guide to Building Private and Decentralized AI Systems | Satish Shankar | 45m |
Covers practical recipes and tradeoffs: differential privacy adds calibrated noise to queries with a tunable privacy budget; federated learning uploads… | ||
| Practical Python Design Patterns | Ramanathan Ramakrishnamoorthy | 36m |
Speaker: Ramanathan Ramakrishnamoorthy, Director/Co-Founder at Zentropy Technologies
Any production application requires good use of design patterns and this… | ||
| Test-Driven Development of a Static Code Analyzer in Python | Terry Yin | 45m |
A Python static analyzer called lizard grows from weekend prototypes into a working cross‑language tool that measures cyclomatic complexity, finds code clones… | ||
| Python In The Job Market | Lee Wei Xuen | 20m |
JobTech builds a pipeline that extracts skills from millions of online job posts weekly using Python and packages like FlashText, regular expressions… | ||
| A scalable blueprint for product customization with Python 3 | Chinab Chugh, Ikhsan Rosyidin | 39m |
A concrete recipe for moving a PHP SaaS to Python 3.6 using Flask: replace per-project apps with an application dispatcher behind Nginx + Gunicorn so requests… | ||
| Writing robust, readable, and maintainable concurrent programs in Python | Wilson Tjhi | 35m |
Compares Python concurrency models with code and timings: threading suffers from the global interpreter lock so CPU-bound threads are slower and produce… | ||
| Dockerizing Django | Ernst Haagsman | 34m |
Argues that containers make development identical to production and starts from a concrete Dockerfile and docker-compose that builds a Django app plus… | ||
| Build a Data-Driven Web App That Everyone Can Use | Galuh Sahid | 37m |
Prototype web app turns a trained RandomForest (saved as model.sav via joblib/pickle) into an interactive demo that predicts crowdfunding amount and a… | ||
| Devil is in the details: Microservices, Security and DevOps | Mohammed A Imran | 41m |
Argues that security moves from days to minutes by embedding checks into CI/CD, demonstrating an open‑source DevSecOps Studio that simulates GitLab CI… | ||
| Taming System with Python: A Sysadmin perspective | Tang Ing Wei | 35m |
Daily printing problem becomes a tiny client/server: Linux laptop POSTs files, Redis stores metadata and acts as Redis as queue server, a Flask web front end… | ||
| Opening Keynote | Katharine Jarmul | 59m |
A live demo shows a Python bird-or-not classifier implemented in under 50 lines using a Keras-style library and ImageNet-pretrained weights, illustrating… | ||
| Detecting offensive messages using Deep Learning: A micro-service based approach | Alizishaan Khatri | 37m |
A deployed system converts text to numbers with word Tyvek embeddings and one‑hot characters, runs a word‑level CNN and a character‑level CNN (optional MLP) in… | ||
| Data Ingestion in Python using Apache Airflow | Sowmya Ganapathi Krishnan, Yew Wee | 34m |
Apache Airflow orchestrates ETL pipelines by encoding DAGs in Python, storing task state in a metadata DB, and dispatching work via an executor like Celery to… | ||
| How to understand user behaviour using Multiple Linear Regression? | Sarthak Deshwal | 34m |
Pipeline that turns raw event logs into a single actionable metric using Python code (label encoder, one‑hot/dummy encoding, train/test split) and multiple… | ||
| Python For Self-Trackers: How To Become A Data-Driven You | Mark Koester | 40m |
Argues personal data can change habits and gives concrete tools and results: Python code on GitHub pulls Fitbit, Kindle clippings, YouTube history and… | ||
| Implementing Reinforcement Learning for Marketing Channel Optimization with Python | Novia Listiyani Wirhaspati | 37m |
Implements a production-oriented pipeline: a negative binomial emulator (Bayesian/Stan-style) models impressions and clicks to simulate the environment, a… | ||
| Run your ICO using Python | Abhishek Pandey | 42m |
Shows how to skip Truffle and compile, deploy and run smart contracts entirely from Python: uses solc/python‑solc to compile, web3.py to talk to nodes, and… | ||
| Concurrency vs Parallelism | Narendran R | 37m |
Speaker: Narendran R, Senior Software Consultant at Tarka
A good code should use resources efficiently, which means not over utilise them as well as not… | ||
| Pumping up Python modules using Rust | Vigneshwer Dhinakaran | 36m |
Shows how replacing hot Python paths with Rust extensions yields concrete gains: a string word‑count example gives about 10x speedup when rewritten in Rust and… | ||
| Inside a PyPy JIT: is that even legal? | Aleksandr Koshkin | 17m |
Claims that the RPython toolchain automatically converts an interpreter into specialized code by applying partial evaluation: opcode dispatch and stack-state… | ||
| Elements of Programming Interviews in Python | Tsung-Hsien Lee | 57m |
Covers the interview pipeline from resume referral through recruiter phone screen to a full day of 3–5 on-site interviews and why internships act as extended… | ||
| Game Programming with Python | Sony Valdez | 57m |
Live code builds a 640x480 pygame demo with keyboard movement, sprite blits from Microsoft Paint art, and a 60 FPS clock; collision uses axis-aligned rectangle… | ||
| Implementing a decorator for thread synchronisation | Graham Dumpleton | 38m |
Shows how to implement Java-style synchronized in Python by building a decorator that locks per-instance, per-class or per-function, using wrapt to handle… | ||
| Testing Complex Python Dependencies With Docker Containers | Jonathan Reiter | 28m |
Uses Docker to encapsulate external, stateful dependencies (browsers, databases) so tests run against exact versions and controlled environments, with ban… | ||
| Applying serverless architecture pattern to distributed data processing | Denys Makogon | 41m |
A container-based serverless pipeline that splits uploaded videos into per-second frame chunks, runs per-chunk OpenCV preprocessing and TensorFlow COCO object… | ||
| Streaming data processing pipelines with Apache Beam [in Python, naturally!] | Markku Lepisto | 53m |
Speaker: Markku Lepistö, Solutions Architect - APAC and Japan, Google Cloud Platform at Google
Everything is about data. Data drives your decisions, your… | ||
| Learn Guitar Via Python Programming | Rishabh Shah | 39m |
Speaker: Rishabh Shah, Associate Tech. Architect at Systango Technologies
Ever wanted to play your favourite song on a guitar quickly even when you don't know… | ||
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