Tag Archives: TFUG

Machine Learning Communities: Q1 ‘22 highlights and achievements

Posted by Nari Yoon, Hee Jung, DevRel Community Manager / Soonson Kwon, DevRel Program Manager

Let’s explore highlights and accomplishments of vast Google Machine Learning communities over the first quarter of the year! We are enthusiastic and grateful about all the activities that the communities across the globe do. Here are the highlights!

ML Ecosystem Campaign Highlights

ML Olympiad is an associated Kaggle Community Competitions hosted by Machine Learning Google Developers Experts (ML GDEs) or TensorFlow User Groups (TFUGs) sponsored by Google. The first round was hosted from January to March, suggesting solving critical problems of our time. Competition highlights include Autism Prediction Challenge, Arabic_Poems, Hausa Sentiment Analysis, Quality Education, Good Health and Well Being. Thank you TFUG Saudi, New York, Guatemala, São Paulo, Pune, Mysuru, Chennai, Bauchi, Casablanca, Agadir, Ibadan, Abidjan, Malaysia and ML GDE Ruqiya Bin Safi, Vinicius Fernandes Caridá, Yogesh Kulkarni, Mohammed buallay, Sayed Ali Alkamel, Yannick Serge Obam, Elyes Manai, Thierno Ibrahima DIOP, Poo Kuan Hoong for hosting ML Olympiad!

Highlights and Achievements of ML Communities

TFUG organizer Ali Mustufa Shaikh (TFUG Mumbai) and Rishit Dagli won the TensorFlow Community Spotlight award (paper and code). This project was supported by provided Google Cloud credit.

ML GDE Sachin Kumar (Qatar) posted Build a retail virtual agent from scratch with Dialogflow CX - Ultimate Chatbot Tutorials. In this tutorial, you will learn how to build a chatbot and voice bot from scratch using Dialogflow CX, a Conversational AI Platform (CAIP) for building conversational UIs.

ML GDE Ngoc Ba (Vietnam) posted MTet: Multi-domain Translation for English and Vietnamese. This project is about how to collect high quality data and train a state-of-the-art neural machine translation model for Vietnamese. And it utilized Google Cloud TPU, Cloud Storage and related GCP products for faster training.

Kaggle announced the Google Open Source Prize early this year (Winners announcement page). In January, ML GDE Aakash Kumar Nain (India)’s Building models in JAX - Part1 (Stax) was awarded.

In February, ML GDE Victor Dibia (USA)’s notebook Signature Image Cleaning with Tensorflow 2.0 and ML GDE Sayak Paul (India) & Soumik Rakshit’s notebook gaugan-keras were awarded.

TFUG organizer Usha Rengaraju posted Variable Selection Networks (AI for Climate Change) and Probabilistic Bayesian Neural Networks using TensorFlow Probability notebooks on Kaggle. They both got gold medals, and she has become a Triple GrandMaster!

TFUG Chennai hosted the two events, Transformers - A Journey into attention and Intro to Deep Reinforcement Learning. Those events were planned for beginners. Events include introductory sessions explaining the transformers research papers and the basic concept of reinforcement learning.

ML GDE Margaret Maynard-Reid (USA), Nived P A, and Joel Shor posted Our Summer of Code Project on TF-GAN. This article describes enhancements made to the TensorFlow GAN library (TF-GAN) of the last summer.

ML GDE Aakash Nain (India) released a series of tutorials about building models in JAX. In the second tutorial, Aakash uses one of the most famous and most widely used high-level libraries for Jax to build a classifier. In the notebook, you will be taking a deep dive into Flax, too.

ML GDE Bhavesh Bhatt (India) built a model for braille to audio with 95% accuracy. He created a model that translates braille to text and audio, lending a helping hand to people with visual disabilities.

ML GDE Sayak Paul (India) recently wrote Publishing ConvNeXt Models on TensorFlow Hub. This is a contribution from the 30 versions of the model, ready for inference and transfer learning, with documentation and sample code. And he also posted First Steps in GSoC to encourage the fellow ML GDEs’ participation in Google Summer of Code (GSoC).

ML GDE Merve Noyan (Turkey) trained 40 models on keras.io/examples; built demos for them with Streamlit and Gradio. And those are currently being hosted here. She also held workshops entitled NLP workshop with TensorFlow for TFUG Delhi, TFUG Chennai, TFUG Hyderabad and TFUG Casablanca. It covered the basic to advanced topics in NLP right from Transformers till model hosting in Hugging Face, using TFX and TF Serve.

Machine Learning Communities: Q4 ‘21 highlights and achievements

Posted by HyeJung Lee, DevRel Community Manager and Soonson Kwon, DevRel Program Manager

Image shows graphic illustrating Q4 success. Includes an arrow pointing to a group of stick figures

Let’s explore highlights and achievements of vast Google Machine Learning communities over the last quarter of last year! We are excited and grateful about all the activities that the communities across the globe do.

Image of the Jax logo  next to images of animals and objects. The animals and objects are labelled Predictions

India-based Aakash Nain has completed the TF-Jax tutorial series with Part 9 (Autodiff in JAX) and Part 10 (Pytrees in JAX). Aakash also started a new tutorial series to learn about the existing methods of building models in JAX. The first tutorial Building models in JAX - Part1 (Stax) is released.

Christmas tree made of code next to words that say Advent of Code

On Dec 12th, ML GDE Paolo Galeone started to solve puzzles of the Advent of Code series using “pure TensorFlow” (without any other library). His solution has been updated in a series of 12 on his blog. He explained how he designed the solutions, how he implemented them, and - when needed - focused on some TensorFlow features not widely used. (Day 1, Day 2, Day 3, Day 4, Day 5, Day 6, Day 7, Day 8, Day 9, Day 10, Day 11, Day 12, Wrap up)

Detailed  diagram of batch prediction/evaluation pipeline leading to model training pipeline

ML GDE Chansung Park (Korea) & Sayak Paul (India) published an “Continuous Adaptation for Machine Learning System to Data Changes” article on TensorFlow blog. They presented a project that implements a workflow combining batch prediction and model evaluation for continuous evaluation retraining In order to capture changes in the data.

Image of Elyes Manais' Google Cloud Certification

ML GDE Elyes Manai from Tunisia wrote an article on GDE blog about his experience on the Google Cloud ML Engineer certification covering guide to certificate and tips.

Image collage of medical staff wearing PPE

TFUG organizer Ali Mustufa Shaikh and Rishit Dagli released “CPPE-5: Medical Personal Protective Equipment Dataset” (paper, code). This paper got featured on Google Research TRC's publication section on January 5, 2022.

Image of a Google slide with text reading Ok, but what are transformers?

TFUG New York hosted a series of events in Dec. End-to-End NLP Workshop with TensorFlow. Brief introduction to the Kaggle competition for Great Barrier Reef challenge by Google(Slide). TF idea for ML Projects with GCP.

Left side of image shows a screenshot  from the Google for Startups Accelerator:MENA page. Right side of mage shows man with glasses holding a piece of paper in front of a wall that has signs on it that say hashtag creativity and hashtag technology

ML GDE Elyes Manai from Tunisia wrote an article “The ability to change people’s lives and leave one’s mark“. Are you facing difficulties growing in constrained environments? And do you think you're not a first-class student and you don't have connections in the industry? Then, check out Elyes’s story. He shared how Google helped him accelerate his impact.

Image shows a graph with data. Labels are on the side to denote wing, body, and tail

ML GDE Sayak Paul (India) and Soumik Rakshit’s Point Cloud Segmentation implemented the PointNet architecture for segmenting 3D point clouds using the ShapeNetCore dataset with TensorFlow 2.x. It is a winner of #TFCommunitySpotlight too.

Screenshot from a paper titled What Should Not be Contrastive in Contrastive Learning

Annotated Research Papers by ML GDE Aakash Kumar Nain (India) is an effort to make papers more accessible to a wider community. It also supports the web version and includes papers from Google Research and etc. This repository is popular enough to have a +2k star and a +200 fork.

Graphic wih text that reads A DevLibrary video interview wth Shai Reznik

Interview series of DevLibrary contributors : Meet the ML GDE Shai Reznik (Israel) and Doug Duhaime. And check out what they built with Google technology and what made them passionate.

Image of a TensorFlow 2.0 Global Docs Sprint event invite with Vikram Tiwari

ML DevFest 2021 by GDG Cloud San Francisco. There are 5 sessions that walk you through framing ML problems, researching ML, building proofs of concepts using existing ML APIs and models, building ML pipelines and etc. ML GDE Vikram Tiwari (USA) presented Vertex, ML Ops and GCP.

The words using Machine Learning for COVID19 helpline with Krupal Modi next to a picture of a man holding a microphone

Krupal Modi (India)’s blog article and #IamaGDE video shares how he’s been leading the machine learning initiatives at Haptik, a conversational AI platform, and how the team paired with the Indian Government and WhatsApp to build a COVID-19 helpline.

Hashtag I am a GDE next to a photo of a woman with sunglasses on her head

Leigh Johnson from USA is the founder of Print Nanny, an automated failure detection system and monitoring system for 3D printers. Meet Leigh in this blog and video!

ML Olympiad: Globally Distributed ML Competitions by the Community

Posted by Hee Jung, DevRel Community Manager

Blog header image shows graphic illustration of people, a group, and a medal

We are happy to announce ML Olympiad, an associated Kaggle Community Competitions hosted by Machine Learning Google Developer Experts (ML GDE) and TensorFlow User Group (TFUG).

Kaggle recently announced "Community Competitions" allowing anyone to create and host a competition at no cost. And our proud members of ML communities decided to dive in and take advantage of the feature to solve critical issues of our time, providing opportunities to train developers.

Why the ML Olympiad?

To train ML for developers leveraging Kaggle’s community competition. This is an opportunity for the participants to practice ML. This is the first 2022 global campaign of the ML Ecosystem team and this helps build stronger communities.

Image with text that reads Community Competitions make machine learning fun

ML Olympiad Community Competitions

Currently, 16 ML Olympiad community competitions are open, hosted by ML GDEs and TFUGs.

Arabic_Poems (in local language) link

  • Predict the name of a poet for Arabic poems. Encourage people to practice on Arabic NLP using TF.
  • Hosts: Ruqiya Bin Safi (ML GDE), Eyad Sibai, Hussain Alfayez / Saudi TFUG & Applied ML/AI group

Sky Survey link

  • Stellar classification with the digital sky survey
  • Hosts: Jieun Yoo, Michael Mellinger / NYTFUG

Análisis epidemiológico Guatemala (in local language) link

  • Make an analysis and prediction of epidemiological cases in Guatemala and the relations.
  • Hosts: Alvin Estrada, Julio Monterroso / TensorFlow User Group Guatemala

QUALITY EDUCATION (in local language) link

  • Competition will be focused on the Enem (National High School Examination) data. Competitors will have to create models to predict student scores in multiple tests.
  • Hosts: Vinicius Fernandes Caridá (ML GDE), Pedro Gengo, Alex Fernandes Mansano / Tensorflow User Group São Paulo

Landscape Image Classification link

  • Classification of partially masked natural images of mountains, buildings, seas, etc.
  • Hosts: Aditya Kane, Yogesh Kulkarni (ML GDE), Shashank Sane / TFUG Pune

Autism Prediction Challenge link

  • Classifying whether individuals have Autism or not.
  • Hosts: Usha Rengaraju, Vijayabharathi Karuppasamy, Samuel T / TFUG Mysuru and TFUG Chennai

Tamkeen Fund Granted link

  • Predict the company funds based on the company's features
  • Hosts: Mohammed buallay (ML GDE), Sayed Ali Alkamel (ML GDE)

Hausa Sentiment Analysis (in local language) link

  • Classify the sentiment of sentences of Hausa Language
  • Hosts: Nuruddeen Sambo, Dattijo Murtala Makama / TFUG Bauchi

TSA Classification (in local language) link

  • We invite participants to develop a classification method to identify early autistic disorders.
  • Hosts: Yannick Serge Obam (ML GDE), Arnold Junior Mve Mve

Let's Fight lung cancer (in local language) link

  • Spotting factors that are link to lung cancer detection
  • Hosts: abderrahman jaize, Sara EL-ATEIF / TFUG Casablanca

Genome Sequences classification (in local language) link

  • Genome sequence classification based on NCBI's GenBank database
  • Hosts: Taha Bouhsine, Said ElHachmey, Lahcen Ousayd / TensorFlow User Group Agadir

GOOD HEALTH AND WELL BEING link

  • Using ML to predict heart disease - If a patient has heart disease or not
  • Hosts: Ibrahim Olagoke, Ahmad Olanrewaju, Ernest Owojori / TensorFlow User Group Ibadan

Preserving North African Culture link

  • We are tackling cultural preservation through a machine learning model capable of identifying the origin of a given item (food, clothing, building).
  • Hosts: elyes manai (ML GDE), Rania Boughanmi, Kayoum Djedidi / IEEE ESSTHS + GDSC ENIT

Delivery Assignment Prediction link

  • The aim of this competition is to build a multi-class classification model capable of accurately predicting the most suitable driver for one or several given orders based on the destination of the order and the paths covered by the deliverers.
  • Host: Thierno Ibrahima DIOP (ML GDE)

Used car price link

  • Predicting the price of an imported used car.
  • Hosts: Armel Yara, Kimana Misago, Jordan Erifried / TFUG Abidjan

TensorFlow Malaysia User Group link

  • Using AI/ML to solve Business Data problem
  • Hosts: Poo Kuan Hoong (ML GDE), Yu Yong Poh, Lau Sian Lun / TensorFlow & Deep Learning Malaysia User Group

Navigating ML Olympiad

You can search “ML Olympiad” on Kaggle Community Competitions page to see them all. And for further info, look for #MLOlympiad on social media.

Google Developers support ML Olympiad by providing swag for top 3 winners of each competition. Find your interest among the competitions, join/share them, and get your part of the swag for competition winners!

Machine Learning Communities: Q3 ‘21 highlights and achievements

Posted by HyeJung Lee, DevRel Community Manager and Soonson Kwon, DevRel Program Manager

Let’s explore highlights and achievements of vast Google Machine Learning communities by region for the last quarter. Activities of experts (GDE, professional individuals), communities (TFUG, TensorFlow user groups), students (GDSC, student clubs), and developers groups (GDG) are presented here.

Key highlights

Image shows a banner for 30 days of ML with Kaggle

30 days of ML with Kaggle is designed to help beginners study ML using Kaggle Learn courses as well as a competition specifically for the participants of this program. Collaborated with the Kaggle team so that +30 the ML GDEs and TFUG organizers participated as volunteers as online mentors as well as speakers for this initiative.

Total 16 of the GDE/GDSC/TFUGs run community organized programs by referring to the shared community organize guide. Houston TensorFlow & Applied AI/ML placed 6th out of 7573 teams — the only Americans in the Top 10 in the competition. And TFUG Santiago (Chile) organizers participated as well and they are number 17 on the public leaderboard.

Asia Pacific

Image shows Google Cloud and Coca-Cola logos

GDE Minori MATSUDA (Japan)’s project on Coca-Cola Bottlers Japan was published on Google Cloud Japan Blog covering creating an ML pipeline to deploy into real business within 2 months by using Vertex AI. This is also published on GCP blog in English.

GDE Chansung Park (Korea) and Sayak Paul (India) published many articles on GCP Blog. First, “Image search with natural language queries” explained how to build a simple image parser from natural language inputs using OpenAI's CLIP model. From this second “Model training as a CI/CD system: (Part I, Part II)” post, you can learn more about why having a resilient CI/CD system for your ML application is crucial for success. Last, “Dual deployments on Vertex AI” talks about end-to-end workflow using Vertex AI, TFX and Kubeflow.

In China, GDE Junpeng Ye used TensorFlow 2.x to significantly reduce the codebase (15k → 2k) on WeChat Finder which is a TikTok alternative in WeChat. GDE Dan lee wrote an article on Understanding TensorFlow Series: Part 1, Part 2, Part 3-1, Part 3-2, Part 4

GDE Ngoc Ba from Vietnam has contributed AI Papers Reading and Coding series implementing ML/DL papers in TensorFlow and creates slides/videos every two weeks. (videos: Vit Transformer, MLP-Mixer and Transformer)

A beginner friendly codelabs (Get started with audio classification ,Go further with audio classification) by GDSC Sookmyung (Korea) learning to customize pre-trained audio classification models to your needs and deploy them to your apps, using TFlite Model Maker.

Cover image for Mat Kelcey's talk on JAX at the PyConAU event

GDE Matthew Kelcey from Australia gave a talk on JAX at PyConAU event. Mat gave an overview to fundamentals of JAX and an intro to some of the libraries being developed on top.

Image shows overview for the released PerceiverIO code

In Singapore, TFUG Singapore dived back into some of the latest papers, techniques, and fields of research that are delivering state-of-the-art results in a number of fields. GDE Martin Andrews included a brief code walkthrough for the released PerceiverIO code at perceiver- highlighting what JAX looks like, how Haiku relates to Sonnet, but also the data loading stuff which is done via tf.data.

Machine Learning Experimentation with TensorBoard book cover

GDE Imran us Salam Mian from Pakistan published a book "Machine Learning Experimentation with TensorBoard".

India

GDE Aakash Nain has published the TF-JAX tutorial series from Part 4 to Part 8. Part 4 gives a brief introduction about JAX (What/Why), and DeviceArray. Part 5 covers why pure functions are good and why JAX prefers them. Part 6 focuses on Pseudo Random Number Generation (PRNG) in Numpy and JAX. Part 7 focuses on Just In Time Compilation (JIT) in JAX. And Part 8 covers vmap and pmap.

Image of Bhavesh's Google Cloud certificate

GDE Bhavesh Bhatt published a video about his experience on the Google Cloud Professional Data Engineer certification exam.

Image shows phase 1 and 2 of the Climate Change project using Vertex AI

Climate Change project using Vertex AI by ML GDE Sayak Paul and Siddha Ganju (NVIDIA). They published a paper (Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning) and open-sourced the project with regard to NASA Impact's ETCI competition. This project made four NeurIPS workshops AI for Science: Mind the Gaps, Tackling Climate Change with Machine Learning, Women in ML, and Machine Learning and the Physical Sciences. And they finished as the first runners-up (see Test Phase 2).

Image shows example of handwriting recognition tutorial

Tutorial on handwriting recognition was contributed to Keras example by GDE Sayak Paul and Aakash Kumar Nain.

Graph regularization for image classification using synthesized graphs by GDE Sayak Pau was added to the official examples in the Neural Structured Learning in TensorFlow.

GDE Sayak Paul and Soumik Rakshit shared a new NLP dataset for multi-label text classification. The dataset consists of paper titles, abstracts, and term categories scraped from arXiv.

North America

Banner image shows students participating in Google Summer of Code

During the GSoC (Google Summer of Code), some GDEs mentored or co-mentored students. GDE Margaret Maynard-Reid (USA) mentored TF-GAN, Model Garden, TF Hub and TFLite products. You can get some of her experience and tips from the GDE Blog. And you can find GDE Sayak Paul (India) and Googler Morgan Roff’s GSoC experience in (co-)mentoring TensorFlow and TF Hub as well.

A beginner friendly workshop on TensorFlow with ML GDE Henry Ruiz (USA) was hosted by GDSC Texas A&M University (USA) for the students.

Screenshot from Youtube video on how transformers work

Youtube video Self-Attention Explained: How do Transformers work? by GDE Tanmay Bakshi from Canada explained how you can build a Transformer encoder-based neural network to classify code into 8 different programming languages using TPU, Colab with Keras.

Europe

GDG / GDSC Turkey hosted AI Summer Camp in cooperation with Global AI Hub. 7100 participants learned about ML, TensorFlow, CV and NLP.

Screenshot from slide presentation titled Why Jax?

TechTalk Speech Processing with Deep Learning and JAX/Trax by GDE Sergii Khomenko (Germany) and M. Yusuf Sarıgöz (Turkey). They reviewed technologies such as Jax, TensorFlow, Trax, and others that can help boost our research in speech processing.

South/Central America

Image shows Custom object detection in the browser using TensorFlow.js

On the other side of the world, in Brazil, GDE Hugo Zanini Gomes wrote an article about “Custom object detection in the browser using TensorFlow.js” using the TensorFlow 2 Object Detection API and Colab was posted on the TensorFlow blog.

Screenshot from a talk about Real-time semantic segmentation in the browser - Made with TensorFlow.js

And Hugo gave a talk about Real-time semantic segmentation in the browser - Made with TensorFlow.js covered using SavedModels in an efficient way in JavaScript directly enabling you to get the reach and scale of the web for your new research.

Data Pipelines for ML was talked about by GDE Nathaly Alarcon Torrico from Bolivia explained all the phases involved in the creation of ML and Data Science products, starting with the data collection, transformation, storage and Product creation of ML models.

Screensho from TechTalk “Machine Learning Competitivo: Top 1% en Kaggle (Video)

TechTalk “Machine Learning Competitivo: Top 1% en Kaggle (Video)“ was hosted by TFUG Santiago (Chile). In this talk the speaker gave a tour of the steps to follow to generate a model capable of being in the top 1% of the Kaggle Leaderboard. The focus was on showing the libraries and“ tricks ”that are used to be able to test many ideas quickly both in implementation and in execution and how to use them in productive environments.

MENA

Screenshot from workshop about Recurrent Neural Networks

GDE Ruqiya Bin Safi (Saudi Arabia) had a workshop about Recurrent Neural Networks : part 1 (Github / Slide) at the GDG Mena. And Ruqiya gave a talk about Recurrent Neural Networks: part 2 at the GDG Cloud Saudi (Saudi Arabia).

AI Training with Kaggle by GDSC Islamic University of Gaza from Palestine. It is a two month training covering Data Processing, Image Processing and NLP with Kaggle.

Sub-Saharan Africa

TFUG Ibadan had two TensorFlow events : Basic Sentiment analysis with Tensorflow and Introduction to Recommenders Systems with TensorFlow”.

Image of Yannick Serge Obam Akou's TensorFlow Certificate

Article covered some tips to study, prepare and pass the TensorFlow developer exam in French by ML GDE Yannick Serge Obam Akou (Cameroon).