Talks

Abstract

Python "warts" are things for which people have criticised Python, typically aspects & mechanisms of its implementation, because such aspects either, cause irritation for parts of the community in some sense. This talk will be about common pitfalls that people face using Python programming language.

Description

Python may be one of the simplest and most flexible programming languages out there, but it is still a programming language. It still has syntax, datatypes, and some occasional dark corners. Python "warts" are things for which people have criticised Python, typically aspects of the language or mechanisms of its implementation, because such aspects either expose certain surprising inconsistencies, are regarded as omissions, or cause irritation for parts of the community in some sense.

This talk will be about common pitfalls (termed warts/landmines) that people face using Python programming language.

Prerequisites:

The only pre-requisite is that you must have some basic familiarity with Python programming language. Although it'll be good that you have laptops with Python already setup, it's not required as such !

If you are really curious how this talk will go, you can watch a similar talk for Ruby and Javascript at the destroyallsoftware site.

Abstract

Have you ever felt that Python has let you down? Or have you ever found yourself writing code that works but you don't know why? This talk will explore weird behaviors in Python, and in some cases, how to exploit them.

Description

We will talk about obscure mistakes/bugs Python programmers may produce.

• What actually happens and what could go wrong when we use comprehension-style syntax?
• What is so scary about nested functions with duplicate variable names?
• What is wrong with creating a sequence of lambdas from list comprehension syntax (e.g. adders = [ lambda x: x + i for i in range(10) ])?
• What would you do if you want to delete elements in a dictionary according to some predicate functions?
• What happens when you mix generator functions with with-statements?
• and more!!

Abstract

I need share my experience esp82266 with micro-python and 3D-Printer. Start with small problem. "How many coin in your piggy bank"

I resolve problem and make a new Question "how to real time update how many coin in your piggy bank". This project can apply to many project.

Description

Who am I

My name is Montri Sroymukda. I am working for Kaidee. So why I select this topic to share. because it is easy every-one can do in your home. Kids enjoy pushing coins in a machine which then updates in real time to let you know how much you have put in. This project can apply to many many project such as topUp machine , vending machine , Etc.

Abstract

I will be talking about the Capsule Networks which is the latest buzz word in whole deep learning community. The talk will connect the dots from the past methods till Capsule networks and show how to make them work in python.

Abstract

"DL is only for the big techs" might be true previously. Now, with new knowledge like Transfer Learning, open-sourced pre-trained models, and free GPUs like at Google Colab, is it now time for individual ML enthusiasts to enter and build some meaningful ML app? Let's explore together in this talk.

Description

A couple years ago, Machine Learning was thought of as the subject requiring intensive computing power and comprehensive understanding of mathematics. Thus, public assumed that it was the job exclusively for giant tech companies with super-genius researchers.

Today, the community has achieved substantial advancements and they are open enough to share their achievements and computing resources to the public at no expenses. Frameworks like TenserFlow, Keras make building a Deep Learning model require only a Python code snippet. Furthermore, Transfer Learning helps developing models even easier by taking portions of pre-trained more generic models, many of which are open-sourced. This has potentials to accelerate the machine learning apps on mobile devices, one of the emerging trend in the field, where compactness and performance are preferred over accuracy.

With rich on-line resources around, including online courses (many of which are free), blog posts, and computing resources available at no expenses, the talk covers the steps to apply those knowledge to build a small, yet useful app based on Deep Learning model - without Deep Pockets, a Ph.D., and mega infrastructure.

The talk is intended for beginners with background in traditional software developments. Mathematical knowledge in differential calculus (derivatives - not in the context of Stock Futures) is useful but not required as it may be touched briefly. Audiences are not expected to walk away as subject matter experts after finishing the talk but the speaker hope to spark inspiration to learn more on the subject and eventually end up developing something for their own use.

Abstract

This talk discusses Multi-Agent Systems with Blockchain Energy Trading. Python is selected to be the language for the IoT integration and backend, whereas Hyperledger and React Native are chosen to be the Blockchain platform and mobile frontend respectively. Real-life demo is also demonstrated.

Description

Due to the proliferation and continuously improvement of solar PV, energy storage, electric vehicles, Internet of things (IoT) devices, these open up new opportunities and change the ways how end-use customers (smart homes and smart buildings) engage in the electricity market. Rather than passively consume energy by purchasing electricity with a fixed tariff from an electric utility, in the near future they can become prosumers who are able to generate revenue on their excess energy and able to obtain transparently-source, reliable energy. This talk discusses the development of a Multi-Agent System Platform, so called PEA HiVE, with Blockchain Energy Trading application. Python is selected to be the development language for the IoT integration and backend, whereas Hyperledger and React Native are chosen to be the Blockchain development platform and our mobile frontend respectively.

Abstract

ถ้าคุณอยากสร้าง api server โดยใช้ async แต่ยังไม่รู้ว่าจะเลือก async framework ตัวไหน ผมมี framework ตัวใหม่ชื่อ Sanic มานำเสนอ Sanic สร้างโดยพยายามให้เหมือน Flask ซึ่งเป็น framework ที่ใช้ง่าย แต่ Sanic จะเน้นความเร็วที่สุด

Description

This session is about Sanic framework. Sanic is async io framework design like Flask Why Sanic is fast we will look inside Sanic how they design it and why How Kaidee use Sanic.

Keynote (WIP)

• What is Sanic
• Sanic routing architecture
• Sanic design

ถ้าคุณกำลัง หรืออยากสร้าง high load api server สักตัวหนึ่ง นี่เป็น session ที่คุณควรเข้ามาฟัง

Abstract

งานผมคือการตรวจสอบคุณภาพโปรแกรมของบริษัท ปัญหาหนึงของการทำงานนี้ คือ ปริมาณเนื้องานที่ถูกส่งมาให้ตรวจสอบจำนวนมาก

ถ้าหากใช้การประชุมเพื่อทำโคดรีวิว ก็จะไม่มีเวลาสำหรับทำงานอื่นเลย

จนผมได้พบกับโปรแกรม SonarQube ที่สามารถตรวจสอบโคดเป็น หมื่นๆบรรทัดได้ภายในเวลาไม่นานทำให้ชีวิตการทำงานของผมสะดวกขึ้นมาก

Description

Abstract

Some people tends to choose the library or framework without understanding the underlying technology. This talk wants to explain different types of concurrency in python. Take away from this talk would be that the audience can choose the right framework to the right task.

Description

ทอร์คนี้จะพูดถึงหัวข้อต่อไปนี้

• Concurrency หลายๆ แบบใน python * Thread * Process * Event loop
• Characteristic ของ concurrency แต่ละแบบ
• ข้อดี/ข้อเสีย ของ concurrency ในแต่ละแบบ
• สถานการณ์แบบไหน ควรใช้ concurrency แบบไหน
• ตัวอย่างการใช้งาน code example, library example

Abstract

เอา python มาช่วยทำ api ปลอม ๆ ให้หน้าบ้านเรียก เพื่อเอาข้อมูลไปแสดงหน้าบ้านให้ได้ก่อน จะอ้างว่า api ไม่เสร็จหน้าบ้านไม่เสร็จไม่ได้

Abstract

Recognition of individuals within a population is a key issue for most behavioral and ecological studies of wild animals. In this talk, you will understand how Python and OpenCV assist us to counter attack to this problem. Result from experiment show that the proposed method is promising.

Description

Basic biological, ecological, and population demographic information is essential to species conservation and management. The identification of individuals within a population allows the study of growth rates, age structure, sex ratios, survivorship, residency, distribution, movement patterns, and population size, which are important for ecological and behavioral studies. The automatic identification could enhance performance of this process. In this talk, algorithm and the way of using python and OpenCV to assist users for identification using facial photograph is addressed. The experimental result could show the performance and promising of the idea.

Abstract

Some web-developers work on the backend and some on the frontend but after all, it comes down to the usability and bug-free delivery of the updates and new releases of functions to the customers. To test frontend directly while writing reusable test cases using Robotframework is another question.

Description

This talk is a short talk about how a simple Gherkin-styled Python based Robotframework could be used to write test cases. It covers the brief installation using python envwrapper - an environment isolation package and simply showing 2 python scripts which is usable with Robotframework and 1~2 Robotframework's keyword-based testing. It will also cover some important inbuilt functions in Robotframeworks such as sending out keystrokes to the System and using some other libraries to help and make front-end testing a much more awesome experience. Another part of the talk would be describing a single keyword base and some yaml scripts to make the test-cases more reusable.

Abstract

We discuss re-implement some features of standard Python library using a subset of Python itself (e.g. range, namedtuple, property, Enum, dataclass, etc.). This allows us to gain more in-depth understanding the mechanisms inside Python as well as introducing some obscure but useful Python features.

Description

In this talk, we discuss how some features in standard Python library can be manually re-implemented using a subset of Python itself. Examples include:

• built-in range function
• namedtuple class constructor
• property decorator
• Enum class (since Python 3.4)
• dataclass (since Python 3.7)
• etc.

Throughout the process of re-implementing these features, we should gain more in-depth understanding the mechanisms inside Python.

Abstract

My personal experience as a witness of each evolution steps of an E-commerce backend system from a huge Monolith application into the Microservices architecture written mostly in Python. This will cover reason behind each steps, problems we've faced and practices we've learned along the way.

Description

This is my personal experience with one of my previous employer. I joined them at the time that their entire company backend was run on a huge PHP based Monolith application which cannot scale out and required the largest Amazon EC2 instance at that time in order to handle all the workloads. By the time I left them, we have successfully migrated the system into Microservices architecture. This talk will walk you through multiple points of time during that period. Explaining how we gradually pull functionalities out of the legacy system. What kind of challenges have we faced along the way. What have we learned so far.

Abstract

This talk is for someone who wanto to improve the around environment better using Python. Python is a programming language that anyone can easily start with. In this talk, I will talk about how we improved development environment using Python.

Description

Python is a programming language that anyone can easily start with. It is the grammar is simple, included in the standard OS, executing is very easy. By the way, is there anything you find inconvenient in living? For example, for example, doing the same confirmation manually many times during development etc. Many tiny problems that can be automated actually exist. In this talk, I will talk about how we improved development environment using Python.

Target:

• Python beginner
• Someone who wants to make something using Python
• Developers who want to improve the development environment better : )

A speaker talks about the following topics:

• why python is a better tool when improving little problem
• the use case of Python: How to improve the development environment

Abstract

Here are the topics

1. CRUD for frontend guy (DjangoREST)
2. Report Query for management level(Djang-ORM)
3. Send the Push Notification for customers(FCM)
4. Test With Runner (pytest)
5. Deploy (Docker)

This talk will not cover Gitlab-runner, AWS EC2, S3/CloudFront, route53

Description

Each small topics here I estimate about 5-8 minutes each

DjangoREST

1. JWT and headers
2. CRUD
3. Nested Serializer Order/OrderItem
4. IdentityHyperLinked
5. Read/Write fields
6. Search, Filter, Ordering
7. Permission

Django-ORM

1. related_name Order/Invoice
2. related_query_name

FCM

1. Show only setup of django-push-notifications
2. Show redis

Pytest

1. Show simple POST a value

Docker

1. Create DockerFile
2. Build an images of Nginx, Django, Postgres, redis
3. docker-compose up

Abstract

This talk is for software engineers who wants to develop a product with location information and Python. GeoDjango is a powerful geographic web framework. I'd like to introduce a way to start a project and how to use features.

Description

Services dealing with location information in recent years are rapidly increasing. However, a perspective of building development environment that handles geometric information from scratch is very hard work. As one of the solutions, I would like to introduce a module of Django and GeoDjango for easy developing of location information.

A speaker talks about the following topics:

• Overview GeoDjango
• Way to start the project and how to use features (Demo)
• What a Can (and Cannot) Do with GeoDjango

The talk gives you my knowledge to develop applications using location information more easily with GeoDjango.

Abstract

ผมจะเล่าเกี่ยวกับการใช้ Python ในการสอนหนังสือ เรื่องการนำไปประยุกต์ในการเรียนการสอนครับ ในขณะนี้ผมเป็นนักเรียนอยู่ ทำให้ผมเห็นปัญหาหลายๆอย่างของการเรียนเขียนโปรแกรมครับ ผมเพิ่งรู้จัก python ประมาณ 1 ปีที่แล้ว ผมอยากแชร์ความสนุกของการศึกษาpython และวิธีการที่ผมจะสามารถใช้สอนเด็กๆ ให้สนใจ programming

Description

as above You will see how it cool

when talk start.

thank for reviews

Abstract

Using AI (particularly in field of computer vision and face recognition) for use in security in ecommerce and finance.

I gave this talk in few meetups in Bangkok: https://goo.gl/eFdxbt https://goo.gl/ctyuGA

Demo video: https://youtu.be/zZuxExLcA8k Slides: https://goo.gl/BLJgJE

Description

Hollywood vs. Reality in facial recognition

• high traffic areas like the AOT / Suvarnabhumi airport / anywhere where scanning for person of interest might be crucial for security personnel
• why AI fails in traditional sense of security, particularly ID verification, and video surveillance
• democritization of AI , and crazy / weird / non-traditional uses of machine learning, particularly in areas of payment transfers, ecommerce transactions,

Abstract

"Ad Tech" is the use of digital technologies by vendors, brands, and their agencies to target potential clients. One popular case is mining the Web to identify their logos. I will show you how to do this by using PyTorch - a popular deep learning framework in Python.

Description

In this talk. We will walk through the one significant use of deep learning for digital marketing and ad tech, the image recognition, which brands use to identify their potential clients, deliver personalized offerings and analyze the spending in the world of social media. The easiest way to identify the brand is by its logo.

The logo detection can be done by object detection models. We will use PyTorch, a popular deep learning framework in Python, to build the model to identify a brand by its logo in an image. Along the talk, we'll see the relative value of deep learning architectures-Deep Neural Network (DNN) and Convolutional Neural Network (CNN) , learn the effect of data size, augment the data when we don't have much, and use the transfer learning technique to improve the model.

Abstract

ที่ขายดีมีคนมาลงขายของหลายหมื่นชิ้นและมีการเข้าใช้งานรวมกว่า 19 ล้านเพจวิวต่อวัน เราใช้ Flask รองรับการใช้งานหลากหลายทุกวันนี้ และในเซสชั่นนี้เราจะมาเล่าให้ฟังว่า ทำไมเราถึงเลือกใช้ Flask และผลลัพธ์เป็นยังไง

Description

ในทอล์คนี้จะเล่าถึงประสบการ์ณของทีมงาน Kaidee ในการใช้ Flask microframework ในหัวข้อต่างๆ เช่น

• ทำไมเราถึงเลือกใช้ Flask
• มี framework อื่นที่เราใช้มั้ย
• ประสบการ์ณที่เจอจากการใช้งาน

หมายเหตุ: ทอล์คนี้จะไม่ได้ลงลึกทางเทคนิคมากนัก ภาษา: ไทย เหมาะสำหรับ: บุคคลทั่วไป, ผู้ที่ยังไม่เคยใช้งาน Flask ในทราฟฟิคสูงๆ, หรืออยากฟังประสบการ์ณการใช้งานจากทีมอื่น

Abstract

Python offers powerful string and nested data tools. One can parse HTML/XML with only built-ins or light wrappers. While such approaches are appealing there are benefits to borrowing more from the XML world. We discuss XPath and XML DOMs and show how well integrated approaches yield cleaner code.

Description

Python's power in manipulating strings and handling nested data structures is well known. So much so that for many mild XML and HTML processing use cases one can get the job done using only built-ins and common parts of the standard library. But the markup language world offers many powerful tools which do not map so directly onto python's data model. And there are large gains to be had if we use native XML tools alongside python and give each component the chance to shine when it can. At the same time learning new tools takes time and adding new parsing and query engines to a project consumes resources. The aim of this talk is to highlight those situations where the benefits of calling in heavy machinery from the XML world outweigh the costs.

We begin with an overview of the XPath query language and use example queries to highlight differences between python's nested data model and that of common markup languages. For example HTML distinguishes between attributes and content while a nested collection of python dicts, list and tuples only has content. To be sure we can express the same information in both models. But we can write shorter, clearer and more-efficient-to-process queries when we retain the distinction. Similarly we can traverse python's built-in data structures with combinations of various braces and parentheses but it is not so simple to pass references into such nested structures and then navigate around. With an XPath processor and common document object model such actions are straightforward – and arguably more pythonic than a solution built entirely on native language features.

Finally we connect things back together with some simple web-scraping examples. Here we use XPath queries to quickly extract elements of interest and then leverage python's string handling capabilities to swiftly convert that content into native data types.

Examples will employ both the lxml parsing library and the selenium web scraping framework. The goal is to focus on use cases where the XML machinery is worth employing. All the wrappers are similar and we wish to highlight that it does not particularly matter which package you learn – it matters that you learn when to employ XPath and a proper DOM.

Abstract

I will introduce the new syntax and functions between Python 2.4 and Python 3.6 in this talk. I will also compare the old style to the new style. You will learn the best practices for Python coding and how to perform refactoring your old Python code. You can look at the evolution of Python.

Description

I think we began to use Python in some production systems at 2.3 or 2.4. I think Python 2.4 had enough functionality to build any system. Many tools ware created and used.

Python 2.4 was released nearly 15 years ago. Since then Python has been steadily evolving. Although it is possible to create a system with only Python 2.4, it is necessary to know the latest Python trends in order to write code that is better, more maintainable, and more performance-oriented.

There are also features and grammar in the web knockout that are often overlooked, not only by new python users, but also by seasoned Python veterans. I will introduce those features and grammar that I think are important while comparing the grammar and functions incorporated in Python 2.4 and now.

Abstract

Jupyter notebooks are great to prototype ideas, but aren’t shippable product. Learn how a JS developer with limited python experience solved this problem. See how to use python to work closely with product/UI designers, shape a reproducible product workflow and solve a user problem.

Description

Jupyter notebooks are great to prototype ideas, but aren’t shippable product (to real users). To bridge the gap between this (a notebook) and a successful product, you must own the user experience and work with product design and feature planning.

Sometimes you must go on an adventure back to being a junior, get some new tech off the shelf and work your way back to familiar territory. Learn how a JS developer with limited python experience worked through tooling and workflow planning choices to iterate to victory.

See how you can use python to work closely with product/UI designers to shape a reproducible product workflow and solve a user problem.

Ideal audience: People with any level of python experience who want to see how it can fit into the wider picture of product development in the trenches of a startup.

Abstract

I'll give a visually interesting talk on Jupyter, to show how: - exploratory programming is a powerful tool that should be in every Python developer's arsenal, - it's great for beginners to learn Python, - it helps visualise results, - you can install on tmux on Android to run Python on the go.

Description

By Jupyter! I'm gonna show you the power of Jupyter!

By that I mean I'm going to do a live demo of Jupyter notebooks.

When I was a kid I grew up with the BBC Model B. When you switched this thing on, you got a prompt. You could launch into BASIC and write programs. Suddenly you felt powerful. The possibilities were endless. 30 years later, Jupyter again gives me that same feeling of being able to explore, solve problems, and that awesome power was right there at your fingertips.

The exciting thing about this talk is that people will be able to interact with it as I go, and even write some code.

I'll explain what Jupyter is, what it's used for, and then I'll do a demo using Jupyter to mess around with some data and make some plots. Then wow the crowd with an animation.

I'll set up a sandboxed notebook that the audience can access, such that they can follow along and try out the code on their mobile phones or laptops. For Android users, I'll then show them what they need to do to install jupyter running under termux, a debian-like environment for Android (no rooting required). Then I'll show them the awesome power of being able to write Python on a mobile device, by writing a script to log my phone's location and take photos, and show them by SSHing into a jupyter session on my phone and walking around with it.

Live demos galore!! What can possibly go wrong?? But the nice thing is, because the jupyter notebooks will be running locally, I don't have to depend on the internet.

My hope is that this talk will inspire people that programming is fun and exciting, and to remove the barrier to entry of it being hard to set up an environment.

Abstract

Python has gained increasing popularity among bioinformatics researchers and practitioners.

In this talk, I will explain how Python is being used to help scientists decipher the language of Life. Specifically, I will show how to do biological sequence and structure analysis using Python.

Description

Python has gained increasing popularity among bioinformatics researchers and practitioners. In this talk, I will explain how Python is being used to decipher the language of Life.

The talk is divided into three parts. In the first part, I will briefly describe how biological molecules (e.g. DNA, Protein) are represented in computer programs. In the second part, I will present key Pythonic software tools in bioinformatics. Finally, in the third part, I will talk about two Biological data analysis tasks solved with Python, namely molecular sequence analysis and molecular structure analysis.

Abstract

Yannix provides Visual Effects (VFX) preparation services for Hollywood blockbusters including Black Panther, Star Wars: The Last Jedi and Thor: Ragnarok. We present cool ways in which we have used Python in running our business, we hope you will find something of value to you from this talk.

Description

Yannix has been providing Visual Effects (VFX) preparation and support services for over 2 decades. Located in suburban Bangkok, Yannix currently has about 350 employees. We work on the latest Hollywood blockbuster movies, many of which have won or been nominated for Academy Awards in VFX. Examples of movies we have worked on recently include Black Panther, Spider-Man: Homecoming, Star Wars: The Last Jedi and Thor: Ragnarok. We develop completely custom, proprietary software packages for the computer graphics and image processing work we do, which includes match moving services such as 3D camera tracking (input: video, output: how the camera moved in 3D per frame), rotoscoping (output: cut-out of characters/objects per frame), VR 360 motion reconstruction and image stitching, and many more. For the past 12 years, we have been using Python as our primary application programming language, with specific code optimization for computationally intensive inner loops optimised into C++ library calls or small GPU programs. We start our talk by showing a sizzle reel of the VFX-prep work we have done for our Hollywood movie clients. We will then talk about the many cool things that can be done using Python by showing how we have used it. We hope that by sharing this information, you will gain some insight into how Python can be used to effectively run your business, especially if, like Yannix, you need heavy compute power for algorithms requiring parallel processing, data mining, machine learning, or computer vision.

We have used Python in many cool ways. We will briefly present several cases here. As part of our earlier tracking application, we have an application called Ptrack which not only is written primarily in Python, also uses Python scripting as a file format for application data storage. Our current flagship application Obsidian (which is also written primarily in Python) uses Python as a macro language where all UI widgets write out the corresponding Python API calls to a scripting window so that users can add their own Python macros to buttons by a simple cut/paste. Obsidian is a GUI-based software developed in Python with parts such as image processing, multidimensional minimization and real-time interactive display functionality optimized in C++ or CUDA and called via Python. Among its functions are feature matching and 2D motion analysis, 3D camera tracking, rigid object tracking, and rotoscoping services. While we extensively use C++ as our inner loop optimization for parts of the code with high computational complexity, we also take full advantage of multiprocessing in the computational intensive sections. Python is intentionally weak at high performance multiprocessing, but we will present how one can easily make Python applications that are truly multiprocessing by spawning threads in Python that call C++ functions which release the global interpreter lock, or simply using OpenMP from C++ functions. Our C++ function calls are sometimes SIMD optimized to get up to about 4 times speedup for many of our image processing routines.

We have also created advanced technologies such as automatic feature matching using Bayesian classifiers that learn from data mining previously processed images. Along the same lines, we have studied the creation of strong classifiers from a number of weak ones through the AdaBoost classifier we use via the open source scikit-learn library, a machine learning Python library. Nearly all aspects of our business operations are modeled through OpenERP (now called Odoo), also completely scripted in Python. We will show the various unique features we have added to OpenERP to localize it for Thailand and to add our own innovative modules for workflow management, task/project management, company policy management, budget management, and many more. We will also introduce a preview of the Yannix ERP (YnxERP) system currently under development to replace our OpenERP system by the end of 2018. Even the distributed datastore/database used for the YnxERP was completely developed inhouse and has many innovative features we can share. We will also introduce how we used Python scripting to integrate our software with Autodesk Maya, a popular 3D animation and modeling software commonly used in the VFX industry. Additionally, we will also present how we have successfully implemented GPU-based programming for image processing and other algorithms requiring massively parallel architectures, and called it from Python applications. Having seen the many ways we have used Python in our business, we hope you will find something from this talk that will be of value to you and your business.

Abstract

CoderDojo is a community organized free programming club for kids aged 7-17, presently in 1000+ locations all over the world.

Description

For many of us, programming wasn't something we studied in the classroom but a passion driven by curiosity, a love for technology and the urge to share. CoderDojo takes these principles and combines them into a self-directed learning environment. This talk covers the overall structure and concepts of CoderDojo, why you should set one up, lessons learned after operating CoderDojo for a year and why community-driven education is the future.

Abstract

This talk, aims to make machine learning simple, fun and accessible to anyone. (The hard part, although honestly it is close to impossible). Working examples in Python and couple of tools such as word2vec (Google) and fasttext (facebook), might be demoed to prove some love/points.

Description

Machine learning is ranked numero uno in Gartner’s Top 10 Strategic Technology Trends for 2017. In recent years, it has received a lot of attention and already revolutionized many areas from finance to image recognition to transportation. This talk, aims to make machine learning simple, fun and accessible to anyone with working examples covering couple of tools.

Abstract

The sequence of nucleotides in a DNA can be represented as a one-dimensional array. A necessary task in genomic data science is finding all subsequences in a DNA sequence that match an input string. As the sequence to be searched is extremely long, finding the most efficient solution is a priority.

Description

I became immersed in the template-matching problem after I encountered it while studying genomic data science. However, this application area will only be mentioned briefly, as the template-matching problem is also relevant to other application areas. The main focus of my talk is how the multi-dimensional array-handling ability of NumPy (not found in the core Python) provided an efficient solution to the problem of template-matching. Finally, I consider alternative solutions and compare them with that of NumPy.

Abstract

You can do so much with a single Python function: from answering web requests to building chatbots. And we can do that completely without maintaining a server, using Functions-as-a-Service! Let's look at the pros and cons—hint: billing is special!—and look at a chatbot built with an AWS Lambda.

Description

Just a Python function. That's all we usually need to answer web requests, to process uploaded images or to have our chatbot reply to chat messages. To run a Python function, you don't want to create and maintain a server. Instead, you want FaaS: Functions-as-a-Service!

In my talk, I'll explain the idea and power of FaaS. We'll also consider potential challenges and limitations to be aware of. Persisting data and pricing are obvious ones, but you likely might want to organize your code differently, too, if you want to build an entire app on FaaS and think about versioning, too. Finally, I'll demo how to answer an HTTP request with a AWS Lambda function, for example to build a chatbot or answer a SMS.

Abstract

Python is not only a language, it's also a runtime! Let's have a look at Hy, a LISP dialect for the Python platform. I'll demo a small Flask app written in Hy—and we'll explore the expressive power of combining Python's standard modules and libraries with LISP macros.

Description

Python is so much more than just a readable language: It's also a powerful platform with an amazing standard library and tons of libraries! Let's use that platform to run another language, a LISP.

Hy is a LISP running on the Python platform. In this Lightning Talk I'll demo a small Flask webapp written in Hy, and show how Hy macros allow us to express our intention clearly.

Abstract

Smart contracts can go beyond the payment method in proposing to embed contracts in all sorts of asset. Until now, Solidity and Go are play vital role in a dApp development. What if we can use thousands of AI /IoT libraries that already available in Python to develop an inclusive innovative project.

Description

The application of the blockchain technology is revolutionary and for visionary developer like you, it is a good time to hear what we are going to share.

Smart property is the main application we are going to talk. It is the extension of smart contracts reaching out into the practical world that includes the IoT. We will share a story that how we used Python to implement a smart property that maintain correctness of the ownership information and prevent unauthorized access. The real advantage to making property smart is that it can be traded, accessed and controlled in a near trust-less way, reduce cost as well as fraud. We will cover how Python and NEO blockchain provide the integration between P2P network and IoT devices in high-level overview then give you how to use Python’s SDK and tools to help you jump-start on smart contracts creation.

The talk will cover (1) basic blockchain and cryptocurrency (2) Neo blockchain and Python SDK (3) Smart contract and its applications (4) Smart property and IoT (5) ICO , crowdfunding and NEP5 token and hope that after this talk you will know more about the blockchain development and kick-off the idea for your next project.

Abstract

Creating automated API tests should not be a chore. Enter Tavern, a pytest plugin and Python library that was just released last year. Tavern is very easy to learn and use that even testers with no coding experience can immediately start writing tests! See how Tavern works and let's dive in!

Description

Being able to write and run automated API tests is something that should not be a struggle for teams practicing continuous integration. For a startup like us, the tools we adopt need to be well-maintained, easy to use, cost effective and ideally open source. Tavern fits the bill, and it's also a pytest plugin, which is a huge plus as well.

In this workshop, we will cover how to get started with Tavern, how to start writing basic tests, how to use variables and reuse requests. Participants should walk away from the workshop with a solid understanding of how to use Tavern for API testing.

Abstract

Are you a newbie in NLP? Have you recently been intrigued by the hype of deep learning? Do you want to get your hands dirty? This talk is for you! I will teach you the basic ideas of NLP, basic building blocks of deep learning, and how to assemble them into a piece of workable code in Python.

Description

Natural Language Processing (NLP) is a challenging subfield of Artificial Intelligence, in which human's ability to understand and produce language is imitated. With the advent of deep learning in mid-2000s, many NLP tasks previously done in traditional statistical methods have gained significant accuracy improvement, thanks to its powerful feature extraction.

This talk will go into the basic ideas of natural language processing, some building blocks of neural networks for deep learning, and how to assemble them into a piece of runnable code for various NLP tasks. PyTorch will be used as the main gear, because we can easily cope with both static and dynamic network architectures while maintaining the code readability. The speaker kindly assumes the audience to have some knowledge in Python (intermediate: especially classes and objects), university calculus (basic), and linguistics (very basic).

The talk will be (preferably) conducted in English. The Q/A session can be done in both English and Thai.

Abstract

Python is a great language for teaching (and learning!) computer science. Check out the tools of the trade I use as a CS teacher to engage students in meaningful learning. Diving into discussions of pedagogy can benefit educators and learners alike.

Description

This talk will feature discussions of how Python can be used to teach principles of computer science but it isn't limited to educators. Learning about learning is an increasingly important skill in today's world of rapidly evolving technology. I will share my experience teaching computer science and computer programming starting with being a part of the team that changed my university's Intro to Programming course from using C++ to Python. Then, I'll share tools I've used to engage students in Python programming including repl.it and Raspberry Pi and run through some of the lessons I've taught with them. I'll also discuss education best practices and how learners can apply them to their own self-education.

Abstract

So you heard the name "Python" and now want to "speak" the language? This talk will give you a glimpse of what Internet has to offer.

Description

Python is considered one of the programming language that is easy to start yet is capable of accomplishing complex tasks due to the strong support from the community. It is one of the languages recommended to be the first to learn to understand how digital computers work without worrying too much on the language subtleties.

Undoubtedly there are many resources available on the Internet, both for those who want to get the taste of the programming world as well as those who are already well-versed in other programming languages. In this talk, the speaker will share experiences on a subset of them.

Abstract

"LINE" is by far the most popular tool in Thailand for instant communication - isn't it about time we allow our Python apps to use it, too? In this talk we'll show you how easy it is to build something amazing, using smart services, Open APIs, and the wonderful Python Requests library.

Description

Machine Learning & Artifical Intelligence are intriguing topics - however, the complexity of these concepts makes it impossible to just quickly use those to build an app. The good news is: you do not have to be a data scientist to build intriguing apps that are able to listen, speak and see.

What we will cover:

• Open APIs and Cognitive Services
• Visualizing Data
• Building an application to use with LINE messenger
• Python Requests Library

Abstract

A quick yet reasonably thorough introduction to Scrapy, the high-level web crawling & scraping framework for Python. Includes a demo, cause I like living on the edge.

Description

Talk outline:

• Speaker introduction
• What is scraping?
• What is Scrapy?
• Core concepts demo
• Problems and solutions
• Resources

The "core concepts demo" is a hands-on introduction, starting from a simple example and progressively adding more features. It covers the following topics:

• Spiders (Spider, CrawlSpider, etc)
• Selectors (XPath, CSS, Regex, etc)
• Items & item loaders
• Input & output processors
• Pipelines

Slides from the previous version of this talk: https://speakerdeck.com/citizen428/aaaarrgghh-spider-web-scraping-with-scrapy

Abstract

Writing functional-style Python can be challenging ranging from minor nuisance such as verbose lambda syntax to more serious problems such as iterator chaining and pattern matching. Coconut is a functional superset of Python that aims to enable elegant and Pythonic functional-style code.

Description

With functions being first-class citizens, Python allows us to build programmes with higher-order functions. However, it is often cumbersome to do something that would be bread-and-butter in a typical functional language. The lack of concise syntax for lambdas, currying and function compositions is a nuisance. The lack of boilerplate-less pattern matching and pattern matching could be a deal breaker. This talk presents Coconut, a functional superset of Python, which aims to enable writing elegant functional code, whilst staying with the familiar Python environment and libraries. We will identify various pain points of writing functional code in Python, and demonstrates how Coconut addresses the problems. In particular, we will start with a basic coding problem, and move on to designing a machine-learning pipeline with a functional approach.

Abstract

Often late, the time to debug that particular bug/issue occurring in production with respect to your application is increasing. It might also cause business disruption and financial loss. In this talk, I'd explain how you could use Application Performance Monitoring to understand your application

Description

Application Performance Monitoring (APM) is a solution built on Elastic Stack. APM helps you to build/store data points in Elasticsearch and visualize. It automatically collects information from your python application/service.

This talk mainly targets at introducing the solution, why it is needed and what you can do with data. It ends with once data is stored within Elasticsearch, what else you can use (ex. Infrastructure Monitoring, Machine Learning).

Agenda

• What is APM?
• Why APM?
• What it can do to your Application?
• Demo

Abstract

New research papers for object detection coming out every other day made really difficult to decide on one algorithm.I chose YOLO after analysis and OH BOY,how much i love my decision!Here,I'll try to give an intuitive explanation behind the choice of YOLO,challenges faced and how to overcome them.

Abstract

I build open source natural Language toolkit for thai language.

Description

I am a PyThaiNLP founder. PyThaiNLP is a python module similar to nltk , but it's working primarily on Thai language instead of English. It supports both Python 2.7 and Python 3. GitHub : https://github.com/PyThaiNLP/pythainlp

Abstract

Can personal data help us become more productive, healthier, and happier?

It’s easy to track with a smartphone, wearable, or sensor. But can data help us become a better self?

I’ll show you how to track, and, with python, how to engage with your data to become a data-driven you!

Description

Can self-tracking and personal data help us become more productive, healthier, and happier?

It’s easier than ever to track our lives, work and bodies with a smartphone, wearable, home sensor or computer. But what can we do with all this data? Can it help you become a better version of yourself?

While we often hear about data in terms of data leaks and surveillance, personal data and self-tracking can be empowering too. With smartphones, wearables, tracking apps, home sensors, and many other methods, it’s easier than ever to collect a lot of data on our lives. But we are still struggling to engage and find meaning in all of the data we collect.

Python and its data science toolset can help transform personal data into a personal dashboard of data visualizations and predictive models. How can python help us better collect, visualize, understand and find patterns in our personal data and self-tracking?

In this talk, I’ll show you how to track your life in different ways, and, with python’s data science toolkit, how to engage and understand that data. The stated goal of the quantified self and self-tracking is to “measure or document something about your self such that it gains meaning.” I think we can go one step further and use data to become better; use data to become data-driven!

In the first part of this talk, we will introduce what is self-tracking and the quantified self as well as various methods that can be used to track and collect data on our lives using wearables, smart phones, apps, computers, sensors, professional testing, etc. We will use python to collect, clean up, process and combine the data together. In the second part, our objective is to create a comprehensive dashboard of our life in data. We will begin our exploration of our personal data by creating a series of data visualizations. In the last part, we will use some statistical and machine learning techniques to search for categories, patterns, relationships and predictions in all of our tracking data.

By the end of the talk, you should be equipped with some ways to track your life, health, productivity and more as well as how to use Python’s data science tools to engage, visualize, find patterns, and make predictions with your data.

Abstract

ผมกำลังทำงานอยู่ในบริษัทหุ่นยนต์สัญชาติไทย ก็เลยอยากจะแชร์ความรู้เกี่ยวกับ การพัฒนาหุ่นยนต์ ด้วยภาษา Python ว่ามันช่วยให้เร็วขึ้นยังไง

I'm working in Thai's autonomous robotic company. I share knowledge about how fast to develop robot with python.

Description

ยกตัวอย่าง ว่าใช้ Python ยังไงและมันเข้ามาช่วยให้ การพัฒนา, การทดสอบ, การประมวลผลข้อมูล, visualization ข้อมูล, Simulation สำหรับ AUV (หุ่นยนต์ใต้น้ำอัตโนมัติ) เร็วขึ้นยังไง รวมถึงข้อดีข้อเสียด้วย

Show example how to use python and how it fast in Developing, Testing, Data Processing, Visualization Data, Simulation for AUV (Autonomus Underwater Vehicle) , pros, cons

Abstract

Data scientists increasingly encounter problems whereby data objects exhibit network-like inter-connectivity. Graph-theoretic computation enables feature extraction from relationship graphs. This talk shows how easy it is to leverage readily available Python libraries/functions to perform such tasks

Description

Much of 'Big Data' revolution has to do with dealing with non-traditional datatypes and feeding machine learning engines with non-numerical variables. Just as Convolutional Neural Networks excel at object recognition task precisely because the convolution layers expressly capture grid-array information embedded in the 2-dimensional visual field, analysis involving network-patterned objects (individuals in an online social network, transportation nodes, banking accounts with payment network, etc.) stands to benefit from the ability to extract graph-theoretic properties from the network connectivity patterns, either to provide insights in and of themselves, or as pre-processing to further machine learning task down the line.

Abstract

In this session, I will talk about how we can use Flask with Flask RESTplus to create robust, performant, well-documented and user-friendly APIs. I have used this technology on numerous projects during a continuous learning span of 3 years, and I will be sharing all I have learned during this talk.

Description

I have been using Flask with Flask RESTplus to create REST API based backend projects for around 3 years now. For those who don't know, Flask RESTplus is an amazing library for REST API development with features like automatic live documentation, input payload validation, neat code organization, automatic response marshaling and a lot of extensibility. You can easily extend it to include other features like response pagination, ETag caching, rate limiting, custom authentication and authorization system and basically anything else you can imagine. In this talk, I will share my hard gained knowledge with code snippets on how to quickly get started and do all these amazing things in Flask RESTplus.

Abstract

In today's world, loads are ever changing while providing service constantly is a must. MySQL has the tools to help you maintain your backend database available and consistent at all times.

Description

MySQL High Availability clusters allow you to develop any application that can scale to accommodate for any load your application need to serve. We will explore the new InnoDB Cluster, understand how it works and how to build it. Next we will talk about MySQL NDB Cluster and how it's different to InnoDB Cluster.

Finally, we will do a live demo of both to show in real life how you can use them both with your Python application.

Abstract

Have you ever need to figure out which type of variable and arguments is?. Would you believe if I tell you that the most exception that raises in Python is TypeError. This talk will bring you to one of the Python features that people rarely know about a Type Annotations.

Description

Maintaining large and fast-growing codebase is always hard. Especially on dynamic type language like Python. The good news is Python is now offering type annotations and it will help you understand your code more easily.

In this talk, We'll introduce you to the new syntax of type annotations and the tooling around type annotations ecosystem which will make your development workflow more productive. We'll share our experience on how we embrace type annotation to our development team and also some common pitfalls you might want to avoid.

Abstract

Asynchronous networking may seem hard to understand at first, in this talk I will show this is not the case. In 100 lines of code, I will write a low level async network stack in front of your eyes, explaining how things work as we go. By the end, you should understand how and why async code works!

Description

If you want to write an efficient server, asynchronous networking is an excellent strategy to do so. To build a production ready system, you would use asyncio, tornado, twisted, trio or another popular implementation. However, to fully understand how and why it works, I will ignore these solutions and instead craft an artisanal asynchronous implementation in front of your very eyes. Step by step we will turn a blocking socket server, only capable of handling a single connection at a time, into a beautiful non blocking server that has no trouble dealing with many connections at once. By the end, you should see that asynchronous code is not magical at all, but nevertheless incredibly useful.

Description

The workshop is organised as follow:

Part 1 (30 min)

Elasticsearch Basic - REST API, cluster etc

Part 2 (60 min)

Indexting elasticsearch with PyThon Client

Part 3 (30 min)

Elasticsearch APM service