Predictive science is a multilevel process requiring observational data and a model/hypothesis which will have to calibrated and validated to eventually the predict the quantity of interest. For decades, the method of choice for model calibration has been the Monte Carlo simulations. Here we present and discuss serial and parallel implementations of a collection of the popular powerful Monte Carlo techniques that can aid inference and uncertainty quantification in Machine learning and Bayesian problems. Emphasis in the development of this open-source package, named ParaMonte, has been on user-friendliness, accessibility from different programming languages and platforms, high-performance, parallelism and scalability, as well as reproductivity and comprehensive post-processing and visualization of the simulation results. The MIT-licensed ParaMonte library is accessible from Python, MATLAB, R, Julia, Fortran, C++, and C, and is permanently located at https://github.com/cdslaborg/paramonte
A scalable physical environment, built using Python and Arduino, is described here. The possible uses are numerous, and the system has been used to teach about color theory, music theory, electronic lighting, and immersive environments. The hardware components are available to the public, and the source code produced is available, so exhibit visitors can duplicate certain elements of the system on their own, and experiment with the technology.
The exhibit equipment currently consists of one RGB LED tetrahedron (six feet tall), three full color RGB stage lights, three full color 3D RGB LED cubes, and one LED assisted bass guitar.
Visitors usually interact with the equipment, creating immersive environments, in one of two ways:
1) They press keys on a computer keyboard corresponding to musical chords. The chords sound out, and corresponding lights are activated, one color per note. Music can then be played and the visitors can play along with the song. By doing so, they learn about color theory, music theory, and song structure. Some visitors can play the LED assisted bass guitar, which shows visitors which frets should be played.
2) They press keys on a computer keyboard to add colors to a list, or to empty the color list. A musical track is played in the background, and the exhibit lights change color in rhythm with the music.
This system is sometimes promoted as "the world's easiest musical instrument". The LED devices can be used to express a variety of colors, patterns, musical chords, rhythms, themes, codes, and emotions.
Accessibility is a big deal in web development, but it’s not always clear what Python developers can do to help make the web more accessible. Projects like Django and Wagtail are showing the way with dedicated accessibility teams. There’s also lots that can be done directly with Python.
Let’s look at practical applications of Python for accessibility!
The curriculum in the general and undergraduate curriculum, in particular, is one of the most important pillars of an education system. The undergraduate curriculum has two main objectives i.e. employability and higher education. The greatest challenge in designing an undergraduate curriculum is achieving a balance between employability skills and laying the foundation for higher education. Generally, the curriculum is a combination of core technical subjects, professional electives, humanities, and skill-oriented subjects. We used natural language processing and machine learning packages in Python to build a curriculum design system.
The steps to build a curriculum design system are described below:<br /> 1. The dataset was built from the job profiles from different job listing websites like stackoverflow.com, indeed.com, linkedin.com, and monster.com. Also from the syllabus of competitive exams and qualifying exams for higher education.<br /> 2. On the dataset, we applied natural language processing techniques to identify the subjects and subject content. For natural language processing, we used spaCy an industrial-strength Natural Language Processing package in Python.<br /> 3. To generate syllabus content for a particular subject, a pointer-generator network was used. The pointer generator network is a text summarization technique that combines extractive and abstractive summarization techniques. The extractive summarization technique extracts keywords from the dataset, whereas the abstractive summarization technique generates new text from the existing text. The pointer-generator network was implemented using the scikit-learn machine learning package in Python.<br /> 4. The generated curriculum was then compared with the existing curriculum to get insights like, how much percent of the curriculum is industry oriented, how much percent of the curriculum is aimed at higher education and job-oriented skills. At this step, we used the ROGUE (Recall-Oriented Understudy Gisting Evaluation) metric to compare the generated curriculum against the reference/proposed curriculum<br /> 5. The above steps can be repeated with modified parameters to get better insights and curriculum.
This also gives us an idea of how we can have an evolving curriculum that can help us bridge the gap between industry and academia.
Jina is an open-source cloud-native project deep-learning powered search framework in Python, empowering developers to create cross-modal or multi-modal search systems for text, images, video, and audio.
Features:
- Time Saver - Bootstrap an AI-powered system in just a few minutes with cookiecutter.
- First-Class AI models - Jina is a new design pattern for neural search systems with first-class support for state-of-the-art AI models like Faiss, Annoy, Onnx, and more.
- Universal Search - Large-scale indexing and querying data of any kind on multiple platforms. Video, image, long/short text, music, source code, and more.
- Production Ready - Cloud-native features out-of-the-box, like containerization, microservice, distributing, scaling, sharding, python async IO, REST, gRPC.
- Plug & Play - With Jina Hub, you can extend Jina with simple Python scripts or Docker images optimized for your search domain.
Agenda:
Introduction to Jina - [2 min]
Core Jina concepts: Pythonic Flow, Pods in Jina, Jina Executors for Deep Learning models using Python - [6 min]
Jina Demo: Demo of neural search use cases like [5 min]
Multi modal search
Cross modal Search
Visual Semantic Search
Query Language Driver for filtering during search
Advanced Jina Concepts: [10 min]
Recursive Document Structure and Traversal
Compound Indexing with Key Value and Vector indexers
Text document segmentation
Depth levels
Q and A: [7 min]
Open forum for the audience to interact with the speaker
It is sometimes asserted that “Python is a real bad choice for any kind of real-time system”. When I first got my Python NES emulator to boot, only for it to run at 2 frames per second, I felt like agreeing. But is it really true? After just a few days reworking the emulator’s operational core into Cython, the framerate is now above 300fps, proving that Python is a viable choice for emulator development and other performance-dependent projects. This poster looks at the advantages and some challenges of using Cython to achieve realtime performance from an existing Python codebase.
For Python projects, Cython fits with the golden rule of optimization: only do what needs doing! It’s easy to optimize correct code, but it’s hard to correct optimized code, so try to do as little as possible. Cython beautifully allows this in Python projects by letting you move code from Python to Cython piece by piece, down to the level of functions or class methods, while still retaining existing code structure, and interoperability. In addition, systems projects like emulators involve lots of known data types, buses, memory and registers of fixed width, and Cython can actually help make some of that clearer than in Python
The paper of Ian Goodfellow on General Adversarial Network has opened a whole new dimension for machine learning. GAN starts its roots from Generative Modeling and now is progressing fast to attain the category of being a domain all to itself!
How can GAN be a determining factor in the field of technology? What are the challenges one has to face working in this field? What are the reasons behind it? Let’s find out together.
Work on creating something new from the hands of artificial intelligence has been an integral part since the very inception of Machine Learning. From CNN to DNN to GAN, there still stays loads of new methods for us to think about. An insight into the hot topic in the market of Machine Learning would only prepare us for the future of this field.
Artificial intelligence, including machine learning and deep learning, have been increasingly utilized for humanitarian applications, from combating climate change to assessing car accidents. Specifically in the domain of geoscientific analysis, deep learning-based remote sensing has yielded many promising humanitarian applications and results. The occurrence of natural disasters is increasing in frequency and intensity due to climate change, and efficient and accurate computational methods of assessing the building damage caused post-disaster must be in place. This assessment aids in the allocation of resources and personnel. Using Python, we can develop convolutional neural networks and other deep learning architectures to detect and classify levels of infrastructure damage to inform disaster relief and recovery programs. A popular data source for doing so is real-time satellite imagery, which is much more easily gathered than data from on the ground. Other data sources include social media posts. Attend this talk to learn about ongoing and future work using deep learning techniques to remotely sense and assess building damage post-natural disaster, using Python. Even for those that are not familiar with this specific domain of application, this talk will demonstrate the boundless opportunities that Python has for social and humanitarian good.
You might have heard of the black autoformatter: you agree to cede control over minutiae of hand-formatting, and Black gives you speed, determinism, and freedom from formatting concerns - letting you save time and mental energy for more important matters.
shed thinks the only problem with black is that it doesn't go far enough.
shed does that (via autoflake & isort).shed does that too (via pyupgrade).shed does that too!shed handles .md or .rst files as well as docstrings.And the best part? You don't - in fact, can't - configure `shed.
shed auto-detects your Python version by inspecting the code, your first-party imports by looking for src/ layout packages... and can even find your Python files using git.
Stop bikeshedding. Use shed.
I am a High School Freshman who has a passion for coding and robotics. For the past 3 years, I have taken Artificial Intelligence classes as a student. AI is a buzz word - It is an exciting yet daunting subject. My instructor made these classes fun and less daunting by using real world examples and games. I got so inspired, I started understanding and writing neural networks in Python in no time. Now, I am trying to create the same experience for my students.
I am a volunteer teacher and I teach AI concepts to students in 6th - 12th grade. When I create a course, I think of how to include AI concepts in games. I write games utilizing libraries like turtle and pygame. I consult with my instructor and they help review my courses.
In this poster I would like to:
Here are some examples of my curriculum:
Augmented Reality (AR) for data visualization is technically expensive for a data scientists, but if data visualizations in AR is to be viable this problem must be solved.
Interacting with Augmented Reality requires that a data scientist learns yet another programing language (often Swift or Kotlin), paradigms of mobile development, and after each iteration the entire mobile application needs to be compiled and deployed to a smartphone. PyStar (Python SmootTh Augmented Reality) is a Python module proposed in this poster that allows data scientists to focus on their Python code, analysis and dataset visualizations in AR.
Finally, I also argue that once the barriers to entry on AR for data scientist are lowered, the practitioner can find much value in adding AR to her data visualization toolkit.
Keywords: Python, Data Visualization, Augmented Reality
The word "kapwa" comes from the Philippines and can be translated as seeing another person as part of one's self. This concept is the foundation for a relational ethics which puts priority not so much on individual development but on the strengthening and preservation of human relationships, and ultimately, on building a strong unified group. As is the case with most talk about ethical concepts, however, kapwa and its related virtues are often described in a hazy or anecdotal manner. This is where Python comes in! With Python, we can simulate the connections that develop and grow between individuals and with NetworkX we can keep track of the large multifaceted networks that result. Furthermore, Dash helps us to create an interactive web app that makes foreign terms from a world away come to life in an orderly step-by-step fashion. In short, this project attempts to use technology (Python, of course!) to help us appreciate the little-known treasures of another culture.
Sailboat is a Python developer's best friend. It's a open source Python build tool that can do anything you need it to! It supports a countless number of plugins — you can even make your own. Sailboat is made for anyone, whether you are a beginner on your very first project, or a senior software engineer with years of experience.
Let's say that that you have created a basic game, Guess My Number, and you want to send it to all of your friends. There are a lot of different ways you can do this, but using Sailboat is the easiest (I think). All you have to do is type three commands: sail quickstart, sail build, and sail release, and you can have a Homebrew file, a pip installable package, and a PyInstaller desktop app (plus a ton more). So easy!
This poster and presentation will briefly go through the following:
- Why I decided to make this tool
- A brief example of how it works
- Example code for an example plugin
- Questions
As programmers, we do debugging almost every day. What are the major options for debugging, what advantages and disadvantages do they have? We'll give the audience an overview of existing debugging tools, and talk about two pain points with existing solutions. We'll introduce a tool called "Cyberbrain" that solve these pain points, with basic introduction to bytecode tracing so the audience can learn this useful technique.
Finally, we'll look into the future and talk about why it's important to be more innovative. We hope that by listening to this talk, the audience can be more open-minded thinking about debugging, and programming as a whole.