Tag Archives: Keras

Machine Learning Communities: Q4 ‘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 last quarter of 2022. We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!


ML at DevFest 2022

A group of ML Developers attending DevFest 2022

A large number of members of ML GDE, TFUG, and 3P ML communities participated in DevFests 2022 worldwide covering various ML topics with Google products. Machine Learning with Jax: Zero to Hero (DevFest Conakry) by ML GDE Yannick Serge Obam Akou (Cameroon) and Easy ML on Google Cloud (DevFest Med) by ML GDE Nathaly Alarcon Torrico (Bolivia) hosted great sessions.

ML Community Summit 2022

A group of ML Developers attending ML Community Summit

ML Community Summit 2022 was hosted on Oct 22-23, 2022, in Bangkok, Thailand. Twenty-five most active community members (ML GDE or TFUG organizer) were invited and shared their past activities and thoughts on Google’s ML products. A video sketch from ML Developer Programs team and a blog posting by ML GDE Margaret Maynard-Reid (United States) help us revisit the moments.

TensorFlow

MAXIM in TensorFlow by ML GDE Sayak Paul (India) shows his implementation of the MAXIM family of models in TensorFlow.

Diagram of gMLP block

gMLP: What it is and how to use it in practice with Tensorflow and Keras? by ML GDE Radostin Cholakov (Bulgaria) demonstrates the state-of-the-art results on NLP and computer vision tasks using a lot less trainable parameters than corresponding Transformer models. He also wrote Differentiable discrete sampling in TensorFlow.

Building Computer Vision Model using TensorFlow: Part 2 by TFUG Pune for the developers who want to deep dive into training an object detection model on Google Colab, inspecting the TF Lite model, and deploying the model on an Android application. ML GDE Nitin Tiwari (India) covered detailed aspects for end-to-end training and deployment of object model detection.

Advent of Code 2022 in pure TensorFlow (days 1-5) by ML GDE Paolo Galeone (Italy) solving the Advent of Code (AoC) puzzles using only TensorFlow. The articles contain a description of the solutions of the Advent of Code puzzles 1-5, in pure TensorFlow.

tf.keras.metrics / tf.keras.optimizers by TFUG Taipei helped people learn the TF libraries. They shared basic concepts and how to use them using Colab.

Screen shot of TensorFlow Lite on Android Project Practical Course
A hands-on course on TensorFlow Lite projects on Android by ML GDE Xiaoxing Wang (China) is the book mainly introducing the application of TensorFlow Lite in Android development. The content focuses on applying three typical ML applications in Android development.

Build tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files with Colab by ML GDE George Soloupis (Greece) guides how you can shrink the final size of your Android application’s .apk by building tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files without the need of Docker or personal PC environment.

TensorFlow Lite and MediaPipe Application by ML GDE XuHua Hu (China) explains how to use TFLite to deploy an ML model into an application on devices. He shared experiences with developing a motion sensing game with MediaPipe, and how to solve problems that we may meet usually.

Train and Deploy TensorFlow models in Go by ML GDE Paolo Galeone (Italy) delivered the basics of the TensorFlow Go bindings, the limitations, and how the tfgo library simplifies their usage.

Keras

Diagram of feature maps concatenated together and flattened

Complete Guide on Deep Learning Architectures, Chapter 1 on ConvNets by ML GDE Merve Noyan (France) brings you into the theory of ConvNets and shows how it works with Keras.

Hazy Image Restoration Using Keras by ML GDE Soumik Rakshit (India) provides an introduction to building an image restoration model using TensorFlow, Keras, and Weights & Biases. He also shared an article Improving Generative Images with Instructions: Prompt-to-Prompt Image Editing with Cross Attention Control.

Mixed precision in Keras based Stable Diffusion
Let’s Generate Images with Keras based Stable Diffusion by ML GDE Chansung Park (Korea) delivered how to generate images with given text and what stable diffusion is. He also talked about Keras-based stable diffusion, basic building blocks, and the advantages of using Keras-based stable diffusion.

A Deep Dive into Transformers with TensorFlow and Keras: Part 1, Part 2, Part3 by ML GDE Aritra Roy Gosthipaty (India) covered the journey from the intuition of attention to formulating the multi-head self-attention. And TensorFlow port of GroupViT in 🤗 transformers library was his contribution to Hugging Face transformers library.

TFX

Digits + TFX banner

How startups can benefit from TFX by ML GDE Hannes Hapke (United States) explains how the San Francisco-based FinTech startup Digits has benefitted from applying TFX early, how TFX helps Digits grow, and how other startups can benefit from TFX too.

Usha Rengaraju (India) shared TensorFlow Extended (TFX) Tutorials (Part 1, Part 2, Part 3) and the following TF projects: TensorFlow Decision Forests Tutorial and FT Transformer TensorFlow Implementation.

Hyperparameter Tuning and ML Pipeline by ML GDE Chansung Park (Korea) explained hyperparam tuning, why it is important; Introduction to KerasTuner, basic usage; how to visualize hyperparam tuning results with TensorBoard; and integration within ML pipeline with TFX.

JAX/Flax

JAX High-performance ML Research by TFUG Taipei and ML GDE Jerry Wu (Taiwan) introduced JAX and how to start using JAX to solve machine learning problems.

[TensorFlow + TPU] GatedTabTransformer[W&B] and its JAX/Flax counterpart GatedTabTransformer-FLAX[W&B] by Usha Rengaraju (India) are tutorial series containing the implementation of GatedTabTransformer paper in both TensorFlow (TPU) and FLAX.

Putting NeRF on a diet: Semantically consistent Few-Shot View Synthesis Implementation
JAX implementation of Diet NeRf by ML GDE Wan Hong Lau (Singapore) implemented the paper “Putting NeRF on a Diet (DietNeRF)” in JAX/Flax. And he also implemented a JAX-and-Flax training pipeline with the ResNet model in his Kaggle notebook, 🐳HappyWhale🔥Flax/JAX⚡TPU&GPU - ResNet Baseline.

Introduction to JAX with Flax (slides) by ML GDE Phillip Lippe (Netherlands) reviewed from the basics of the requirements we have on a DL framework to what JAX has to offer. Further, he focused on the powerful function-oriented view JAX offers and how Flax allows you to use them in training neural networks.

Screen grab of ML GDE David Cardozo and Cristian Garcia during a live coding session of a review of new features, specifically Shared Arrays, in the recent release of JAX
JAX Streams: Exploring JAX 0.4 by ML GDE David Cardozo (Canada) and Cristian Garcia (Colombia) showed a review of new features (specifically Shared Arrays) in the recent release of JAX and demonstrated live coding.

[LiveCoding] Train ResNet/MNIST with JAX/Flax by ML GDE Qinghua Duan (China) demonstrated how to train ResNet using JAX by writing code online.

Kaggle

Low-light Image Enhancement using MirNetv2 by ML GDE Soumik Rakshit (India) demonstrated the task of Low-light Image Enhancement.

Heart disease Prediction and Diabetes Prediction Competition hosted by TFUG Chandigarh were to familiarize participants with ML problems and find solutions using classification techniques.

TensorFlow User Group Bangalore Sentiment Analysis Kaggle Competition 1
TFUG Bangalore Kaggle Competition - Sentiment Analysis hosted by TFUG Bangalore was to find the best sentiment analysis algorithm. Participants were given a set of training data and asked to submit an ML/DL algorithm that could predict the sentiment of a text. The group also hosted Kaggle Challenge Finale + Vertex AI Session to support the participants and guide them in learning how to use Vertex AI in a workflow.

Cloud AI

Better Hardware Provisioning for ML Experiments on GCP by ML GDE Sayak Paul (India) discussed the pain points of provisioning hardware (especially for ML experiments) and how we can get better provision hardware with code using Vertex AI Workbench instances and Terraform.

Jayesh Sharma, Platform Engineer, Zen ML; MLOps workshop with TensorFlow and Vertex AI November 12, 2022|TensorFlow User Group Chennai
MLOps workshop with TensorFlow and Vertex AI by TFUG Chennai targeted beginners and intermediate-level practitioners to give hands-on experience on the E2E MLOps pipeline with GCP. In the workshop, they shared the various stages of an ML pipeline, the top tools to build a solution, and how to design a workflow using an open-source framework like ZenML.

10 Predictions on the Future of Cloud Computing by 2025: Insights from Google Next Conference by ML GDE Victor Dibia (United States) includes a recap of his notes reflecting on the top 10 cloud technology predictions discussed at the Google Cloud Next 2022 keynote.
Workflow of Google Virtual Career Center
O uso do Vertex AI Matching Engine no Virtual Career Center (VCC) do Google Cloud by ML GDE Rubens Zimbres (Brazil) approaches the use of Vertex AI Matching Engine as part of the Google Cloud Virtual Career Center solution.

More practical time-series model with BQML by ML GDE JeongMin Kwon (Korea) introduced BQML and time-series modeling and showed some practical applications with BQML ARIMA+ and Python implementations.

Vertex AI Forecast - Demand Forecasting with AutoML by ML GDE Rio Kurihara (Japan) presented a time series forecast overview, time series fusion transformers, and the benefits and desired features of AutoML.

Research & Ecosystem

AI in Healthcare by ML GDE Sara EL-ATEIF (Morocco) introduced AI applications in healthcare and the challenges facing AI in its adoption into the health system.

Women in AI APAC finished their journey at ML Paper Reading Club. During 10 weeks, participants gained knowledge on outstanding machine learning research, learned the latest techniques, and understood the notion of “ML research” among ML engineers. See their session here.

A Natural Language Understanding Model LaMDA for Dialogue Applications by ML GDE Jerry Wu (Taiwan) introduced the natural language understanding (NLU) concept and shared the operation mode of LaMDA, model fine-tuning, and measurement indicators.

Python library for Arabic NLP preprocessing (Ruqia) by ML GDE Ruqiya Bin (Saudi Arabia) is her first python library to serve Arabic NLP.

Screengrab of ML GDEs Margaret Maynard-Reid and Akash Nain during Chat with ML GDE Akash
Chat with ML GDE Vikram & Chat with ML GDE Aakash by ML GDE Margaret Maynard-Reid (United States) shared the stories of ML GDEs’ including how they became ML GDE and how they proceeded with their ML projects.

Anatomy of Capstone ML Projects 🫀by ML GDE Sayak Paul (India) discussed working on capstone ML projects that will stay with you throughout your career. He covered various topics ranging from problem selection to tightening up the technical gotchas to presentation. And in Improving as an ML Practitioner he shared his learning from experience in the field working on several aspects.

Screen grab of statement of objectives in MLOps Development Environment by ML GDE Vinicius Carida
MLOps Development Environment by ML GDE Vinicius Caridá (Brazil) aims to build a full development environment where you can write your own pipelines connecting MLFLow, Airflow, GCP and Streamlit, and build amazing MLOps pipelines to practice your skills.

Transcending Scaling Laws with 0.1% Extra Compute by ML GDE Grigory Sapunov (UK) reviewed a recent Google article on UL2R. And his posting Discovering faster matrix multiplication algorithms with reinforcement learning explained how AlphaTensor works and why it is important.

Back in Person - Prompting, Instructions and the Future of Large Language Models by TFUG Singapore and ML GDE Sam Witteveen (Singapore) and Martin Andrews (Singapore). This event covered recent advances in the field of large language models (LLMs).

ML for Production: The art of MLOps in TensorFlow Ecosystem with GDG Casablanca by TFUG Agadir discussed the motivation behind using MLOps and how it can help organizations automate a lot of pain points in the ML production process. It also covered the tools used in the TensorFlow ecosystem.

Machine Learning Communities: Q3 ‘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 third quarter of the year! We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!


TensorFlow/Keras

Load-testing TensorFlow Serving’s REST Interface

Load-testing TensorFlow Serving’s REST Interface by ML GDE Sayak Paul (India) and Chansung Park (Korea) shares the lessons and findings they learned from conducting load tests for an image classification model across numerous deployment configurations.

TFUG Taipei hosted events (Python + Hugging Face-Translation+ tf.keras.losses, Python + Object detection, Python+Hugging Face-Token Classification+tf.keras.initializers) in September and helped community members learn how to use TF and Hugging face to implement machine learning model to solve problems.

Neural Machine Translation with Bahdanau’s Attention Using TensorFlow and Keras and the related video by ML GDE Aritra Roy Gosthipaty (India) explains the mathematical intuition behind neural machine translation.

Serving a TensorFlow image classification model as RESTful and gRPC based services with TFServing, Docker, and Kubernetes

Automated Deployment of TensorFlow Models with TensorFlow Serving and GitHub Actions by ML GDE Chansung Park (Korea) and Sayak Paul (India) explains how to automate TensorFlow model serving on Kubernetes with TensorFlow Serving and GitHub Action.

Deploying ? ViT on Kubernetes with TF Serving by ML GDE Sayak Paul (India) and Chansung Park (Korea) shows how to scale the deployment of a ViT model from ? Transformers using Docker and Kubernetes.

Screenshot of the TensorFlow Forum in the Chinese Language run by the tf.wiki team

Long-term TensorFlow Guidance on tf.wiki Forum by ML GDE Xihan Li (China) provides TensorFlow guidance by answering the questions from Chinese developers on the forum.

photo of a phone with the Hindi letter 'Ohm' drawn on the top half of the screen. Hinidi Character recognition shows the letter Ohm as the Predicted Result below.

Hindi Character Recognition on Android using TensorFlow Lite by ML GDE Nitin Tiwari (India) shares an end-to-end tutorial on training a custom computer vision model to recognize Hindi characters. In TFUG Pune event, he also gave a presentation titled Building Computer Vision Model using TensorFlow: Part 1.

Using TFlite Model Maker to Complete a Custom Audio Classification App by ML GDE Xiaoxing Wang (China) shows how to use TFLite Model Maker to build a custom audio classification model based on YAMNet and how to import and use the YAMNet-based custom models in Android projects.

SoTA semantic segmentation in TF with ? by ML GDE Sayak Paul (India) and Chansung Park (Korea). The SegFormer model was not available on TensorFlow.

Text Augmentation in Keras NLP by ML GDE Xiaoquan Kong (China) explains what text augmentation is and how the text augmentation feature in Keras NLP is designed.

The largest vision model checkpoint (public) in TF (10 Billion params) through ? transformers by ML GDE Sayak Paul (India) and Aritra Roy Gosthipaty (India). The underlying model is RegNet, known for its ability to scale.

A simple TensorFlow implementation of a DCGAN to generate CryptoPunks

CryptoGANs open-source repository by ML GDE Dimitre Oliveira (Brazil) shows simple model implementations following TensorFlow best practices that can be extended to more complex use-cases. It connects the usage of TensorFlow with other relevant frameworks, like HuggingFace, Gradio, and Streamlit, building an end-to-end solution.


TFX

TFX Machine Learning Pipeline from data injection in TFRecord to pushing out Vertex AI

MLOps for Vision Models from ? with TFX by ML GDE Chansung Park (Korea) and Sayak Paul (India) shows how to build a machine learning pipeline for a vision model (TensorFlow) from ? Transformers using the TF ecosystem.

First release of TFX Addons Package by ML GDE Hannes Hapke (United States). The package has been downloaded a few thousand times (source). Google and other developers maintain it through bi-weekly meetings. Google’s Open Source Peer Award has recognized the work.

TFUG São Paulo hosted TFX T1 | E4 & TFX T1 | E5. And ML GDE Vinicius Caridá (Brazil) shared how to train a model in a TFX pipeline. The fifth episode talks about Pusher: publishing your models with TFX.

Semantic Segmentation model within ML pipeline by ML GDE Chansung Park (Korea) and Sayak Paul (India) shows how to build a machine learning pipeline for semantic segmentation task with TFX and various GCP products such as Vertex Pipeline, Training, and Endpoints.


JAX/Flax

Screen shot of Tutorial 2 (JAX): Introduction to JAX+Flax with GitHub Repo and Codelab via university of Amseterdam

JAX Tutorial by ML GDE Phillip Lippe (Netherlands) is meant to briefly introduce JAX, including writing and training neural networks with Flax.


TFUG Malaysia hosted Introduction to JAX for Machine Learning (video) and Leong Lai Fong gave a talk. The attendees learned what JAX is and its fundamental yet unique features, which make it efficient to use when executing deep learning workloads. After that, they started training their first JAX-powered deep learning model.

TFUG Taipei hosted Python+ JAX + Image classification and helped people learn JAX and how to use it in Colab. They shared knowledge about the difference between JAX and Numpy, the advantages of JAX, and how to use it in Colab.

Introduction to JAX by ML GDE João Araújo (Brazil) shared the basics of JAX in Deep Learning Indaba 2022.

A comparison of the performance and overview of issues resulting from changing from NumPy to JAX

Should I change from NumPy to JAX? by ML GDE Gad Benram (Portugal) compares the performance and overview of the issues that may result from changing from NumPy to JAX.

Introduction to JAX: efficient and reproducible ML framework by ML GDE Seunghyun Lee (Korea) introduced JAX/Flax and their key features using practical examples. He explained the pure function and PRNG, which make JAX explicit and reproducible, and XLA and mapping functions which make JAX fast and easily parallelized.

Data2Vec Style pre-training in JAX by ML GDE Vasudev Gupta (India) shares a tutorial for demonstrating how to pre-train Data2Vec using the Jax/Flax version of HuggingFace Transformers.

Distributed Machine Learning with JAX by ML GDE David Cardozo (Canada) delivered what makes JAX different from TensorFlow.

Image classification with JAX & Flax by ML GDE Derrick Mwiti (Kenya) explains how to build convolutional neural networks with JAX/Flax. And he wrote several articles about JAX/Flax: What is JAX?, How to load datasets in JAX with TensorFlow, Optimizers in JAX and Flax, Flax vs. TensorFlow, etc..


Kaggle

DDPMs - Part 1 by ML GDE Aakash Nain (India) and cait-tf by ML GDE Sayak Paul (India) were announced as Kaggle ML Research Spotlight Winners.

Forward process in DDPMs from Timestep 0 to 100

Fresher on Random Variables, All you need to know about Gaussian distribution, and A deep dive into DDPMs by ML GDE Aakash Nain (India) explain the fundamentals of diffusion models.

In Grandmasters Journey on Kaggle + The Kaggle Book, ML GDE Luca Massaron (Italy) explained how Kaggle helps people in the data science industry and which skills you must focus on apart from the core technical skills.


Cloud AI

How Cohere is accelerating language model training with Google Cloud TPUs by ML GDE Joanna Yoo (Canada) explains what Cohere engineers have done to solve scaling challenges in large language models (LLMs).

ML GDE Hannes Hapke (United States) chats with Fillipo Mandella, Customer Engineering Manager at Google

In Using machine learning to transform finance with Google Cloud and Digits, ML GDE Hannes Hapke (United States) chats with Fillipo Mandella, Customer Engineering Manager at Google, about how Digits leverages Google Cloud’s machine learning tools to empower accountants and business owners with near-zero latency.

A tour of Vertex AI by TFUG Chennai for ML, cloud, and DevOps engineers who are working in MLOps. This session was about the introduction of Vertex AI, handling datasets and models in Vertex AI, deployment & prediction, and MLOps.

TFUG Abidjan hosted two events with GDG Cloud Abidjan for students and professional developers who want to prepare for a Google Cloud certification: Introduction session to certifications and Q&A, Certification Study Group.

Flow chart showing shows how to deploy a ViT B/16 model on Vertex AI

Deploying ? ViT on Vertex AI by ML GDE Sayak Paul (India) and Chansung Park (Korea) shows how to deploy a ViT B/16 model on Vertex AI. They cover some critical aspects of a deployment such as auto-scaling, authentication, endpoint consumption, and load-testing.

Photo collage of AI generated images

TFUG Singapore hosted The World of Diffusion - DALL-E 2, IMAGEN & Stable Diffusion. ML GDE Martin Andrews (Singapore) and Sam Witteveen (Singapore) gave talks named “How Diffusion Works” and “Investigating Prompt Engineering on Diffusion Models” to bring people up-to-date with what has been going on in the world of image generation.

ML GDE Martin Andrews (Singapore) have done three projects: GCP VM with Nvidia set-up and Convenience Scripts, Containers within a GCP host server, with Nvidia pass-through, Installing MineRL using Containers - with linked code.

Jupyter Services on Google Cloud by ML GDE Gad Benram (Portugal) explains the differences between Vertex AI Workbench, Colab, and Deep Learning VMs.

Google Cloud's Two Towers Recommender and TensorFlow

Train and Deploy Google Cloud's Two Towers Recommender by ML GDE Rubens de Almeida Zimbres (Brazil) explains how to implement the model and deploy it in Vertex AI.


Research & Ecosystem

WOMEN DATA SCIENCE, LA PAZ Club de lectura de papers de Machine Learning Read, Learn and Share the knowledge #MLPaperReadingClubs, Nathaly Alarcón, @WIDS_LaPaz #MLPaperReadingClubs

The first session of #MLPaperReadingClubs (video) by ML GDE Nathaly Alarcon Torrico (Bolivia) and Women in Data Science La Paz. Nathaly led the session, and the community members participated in reading the ML paper “Zero-shot learning through cross-modal transfer.”

In #MLPaperReadingClubs (video) by TFUG Lesotho, Arnold Raphael volunteered to lead the first session “Zero-shot learning through cross-modal transfer.”

Screenshot of a screenshare of Zero-shot learning through cross-modal transfer to 7 participants in a virtual call

ML Paper Reading Clubs #1: Zero Shot Learning Paper (video) by TFUG Agadir introduced a model that can recognize objects in images even if no training data is available for the objects. TFUG Agadir prepared this event to make people interested in machine learning research and provide them with a broader vision of differentiating good contributions from great ones.

Opening of the Machine Learning Paper Reading Club (video) by TFUG Dhaka introduced ML Paper Reading Club and the group’s plan.

EDA on SpaceX Falcon 9 launches dataset (Kaggle) (video) by TFUG Mysuru & TFUG Chandigarh organizer Aashi Dutt (presenter) walked through exploratory data analysis on SpaceX Falcon 9 launches dataset from Kaggle.

Screenshot of ML GDE Qinghua Duan (China) showing how to apply the MRC paradigm and BERT to solve the dialogue summarization problem.

Introduction to MRC-style dialogue summaries based on BERT by ML GDE Qinghua Duan (China) shows how to apply the MRC paradigm and BERT to solve the dialogue summarization problem.

Plant disease classification using Deep learning model by ML GDE Yannick Serge Obam Akou (Cameroon) talked on plant disease classification using deep learning model : an end to end Android app (open source project) that diagnoses plant diseases.

TensorFlow/Keras implementation of Nystromformer

Nystromformer Github repository by Rishit Dagli provides TensorFlow/Keras implementation of Nystromformer, a transformer variant that uses the Nyström method to approximate standard self-attention with O(n) complexity which allows for better scalability.

Machine Learning Communities: Q2 ‘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 second quarter of the year! We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!

TensorFlow/Keras

TFUG Agadir hosted #MLReady phase as a part of #30DaysOfML. #MLReady aimed to prepare the attendees with the knowledge required to understand the different types of problems which deep learning can solve, and helped attendees be prepared for the TensorFlow Certificate.

TFUG Taipei hosted the basic Python and TensorFlow courses named From Python to TensorFlow. The aim of these events is to help everyone learn about the basics of Python and TensorFlow, including TensorFlow Hub, TensorFlow API. The event videos are shared every week via Youtube playlist.

TFUG New York hosted Introduction to Neural Radiance Fields for TensorFlow users. The talk included Volume Rendering, 3D view synthesis, and links to a minimal implementation of NeRF using Keras and TensorFlow. In the event, ML GDE Aritra Roy Gosthipaty (India) had a talk focusing on breaking the concepts of the academic paper, NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis into simpler and more ingestible snippets.

TFUG Turkey, GDG Edirne and GDG Mersin organized a TensorFlow Bootcamp 22 and ML GDE M. Yusuf Sarıgöz (Turkey) participated as a speaker, TensorFlow Ecosystem: Get most out of auxiliary packages. Yusuf demonstrated the inner workings of TensorFlow, how variables, tensors and operations interact with each other, and how auxiliary packages are built upon this skeleton.

TFUG Mumbai hosted the June Meetup and 110 folks gathered. ML GDE Sayak Paul (India) and TFUG mentor Darshan Despande shared knowledge through sessions. And ML workshops for beginners went on and participants built up machine learning models without writing a single line of code.

ML GDE Hugo Zanini (Brazil) wrote Realtime SKU detection in the browser using TensorFlow.js. He shared a solution for a well-known problem in the consumer packaged goods (CPG) industry: real-time and offline SKU detection using TensorFlow.js.

ML GDE Gad Benram (Portugal) wrote Can a couple TensorFlow lines reduce overfitting? He explained how just a few lines of code can generate data augmentations and boost a model’s performance on the validation set.

ML GDE Victor Dibia (USA) wrote How to Build An Android App and Integrate Tensorflow ML Models sharing how to run machine learning models locally on Android mobile devices, How to Implement Gradient Explanations for a HuggingFace Text Classification Model (Tensorflow 2.0) explaining in 5 steps about how to verify the model is focusing on the right tokens to classify text. He also wrote how to finetune a HuggingFace model for text classification, using Tensorflow 2.0.

ML GDE Karthic Rao (India) released a new series ML for JS developers with TFJS. This series is a combination of short portrait and long landscape videos. You can learn how to build a toxic word detector using TensorFlow.js.

ML GDE Sayak Paul (India) implemented the DeiT family of ViT models, ported the pre-trained params into the implementation, and provided code for off-the-shelf inference, fine-tuning, visualizing attention rollout plots, distilling ViT models through attention. (code | pretrained model | tutorial)

ML GDE Sayak Paul (India) and ML GDE Aritra Roy Gosthipaty (India) inspected various phenomena of a Vision Transformer, shared insights from various relevant works done in the area, and provided concise implementations that are compatible with Keras models. They provide tools to probe into the representations learned by different families of Vision Transformers. (tutorial | code)

JAX/Flax

ML GDE Aakash Nain (India) had a special talk, Introduction to JAX for ML GDEs, TFUG organizers and ML community network organizers. He covered the fundamentals of JAX/Flax so that more and more people try out JAX in the near future.

ML GDE Seunghyun Lee (Korea) started a project, Training and Lightweighting Cookbook in JAX/FLAX. This project attempts to build a neural network training and lightweighting cookbook including three kinds of lightweighting solutions, i.e., knowledge distillation, filter pruning, and quantization.

ML GDE Yucheng Wang (China) wrote History and features of JAX and explained the difference between JAX and Tensorflow.

ML GDE Martin Andrews (Singapore) shared a video, Practical JAX : Using Hugging Face BERT on TPUs. He reviewed the Hugging Face BERT code, written in JAX/Flax, being fine-tuned on Google’s Colab using Google TPUs. (Notebook for the video)

ML GDE Soumik Rakshit (India) wrote Implementing NeRF in JAX. He attempts to create a minimal implementation of 3D volumetric rendering of scenes represented by Neural Radiance Fields.

Kaggle

ML GDEs’ Kaggle notebooks were announced as the winner of Google OSS Expert Prize on Kaggle: Sayak Paul and Aritra Roy Gosthipaty’s Masked Image Modeling with Autoencoders in March; Sayak Paul’s Distilling Vision Transformers in April; Sayak Paul & Aritra Roy Gosthipaty’s Investigating Vision Transformer Representations; Soumik Rakshit’s Tensorflow Implementation of Zero-Reference Deep Curve Estimation in May and Aakash Nain’s The Definitive Guide to Augmentation in TensorFlow and JAX in June.

ML GDE Luca Massaron (Italy) published The Kaggle Book with Konrad Banachewicz. This book details competition analysis, sample code, end-to-end pipelines, best practices, and tips & tricks. And in the online event, Luca and the co-author talked about how to compete on Kaggle.















ML GDE Ertuğrul Demir (Turkey) wrote Kaggle Handbook: Fundamentals to Survive a Kaggle Shake-up covering bias-variance tradeoff, validation set, and cross validation approach. In the second post of the series, he showed more techniques using analogies and case studies.













TFUG Chennai hosted ML Study Jam with Kaggle and created study groups for the interested participants. More than 60% of members were active during the whole program and many of them shared their completion certificates.

TFUG Mysuru organizer Usha Rengaraju shared a Kaggle notebook which contains the implementation of the research paper: UNETR - Transformers for 3D Biomedical Image Segmentation. The model automatically segments the stomach and intestines on MRI scans.

TFX

ML GDE Sayak Paul (India) and ML GDE Chansung Park (Korea) shared how to deploy a deep learning model with Docker, Kubernetes, and Github actions, with two promising ways - FastAPI (for REST) and TF Serving (for gRPC).

ML GDE Ukjae Jeong (Korea) and ML Engineers at Karrot Market, a mobile commerce unicorn with 23M users, wrote Why Karrot Uses TFX, and How to Improve Productivity on ML Pipeline Development.

ML GDE Jun Jiang (China) had a talk introducing the concept of MLOps, the production-level end-to-end solutions of Google & TensorFlow, and how to use TFX to build the search and recommendation system & scientific research platform for large-scale machine learning training.

ML GDE Piero Esposito (Brazil) wrote Building Deep Learning Pipelines with Tensorflow Extended. He showed how to get started with TFX locally and how to move a TFX pipeline from local environment to Vertex AI; and provided code samples to adapt and get started with TFX.

TFUG São Paulo (Brazil) had a series of online webinars on TensorFlow and TFX. In the TFX session, they focused on how to put the models into production. They talked about the data structures in TFX and implementation of the first pipeline in TFX: ingesting and validating data.

TFUG Stockholm hosted MLOps, TensorFlow in Production, and TFX covering why, what and how you can effectively leverage MLOps best practices to scale ML efforts and had a look at how TFX can be used for designing and deploying ML pipelines.

Cloud AI

ML GDE Chansung Park (Korea) wrote MLOps System with AutoML and Pipeline in Vertex AI on GCP official blog. He showed how Google Cloud Storage and Google Cloud Functions can help manage data and handle events in the MLOps system.

He also shared the Github repository, Continuous Adaptation with VertexAI's AutoML and Pipeline. This contains two notebooks to demonstrate how to automate to produce a new AutoML model when the new dataset comes in.

TFUG Northwest (Portland) hosted The State and Future of AI + ML/MLOps/VertexAI lab walkthrough. In this event, ML GDE Al Kari (USA) outlined the technology landscape of AI, ML, MLOps and frameworks. Googler Andrew Ferlitsch had a talk about Google Cloud AI’s definition of the 8 stages of MLOps for enterprise scale production and how Vertex AI fits into each stage. And MLOps engineer Chris Thompson covered how easy it is to deploy a model using the Vertex AI tools.

Research

ML GDE Qinghua Duan (China) released a video which introduces Google’s latest 540 billion parameter model. He introduced the paper PaLM, and described the basic training process and innovations.

ML GDE Rumei LI (China) wrote blog postings reviewing papers, DeepMind's Flamingo and Google's PaLM.

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: 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).

Announcing TensorFlow r1.4

Posted by the TensorFlow Team

TensorFlow release 1.4 is now public - and this is a big one! So we're happy to announce a number of new and exciting features we hope everyone will enjoy.

Keras

In 1.4, Keras has graduated from tf.contrib.keras to core package tf.keras. Keras is a hugely popular machine learning framework, consisting of high-level APIs to minimize the time between your ideas and working implementations. Keras integrates smoothly with other core TensorFlow functionality, including the Estimator API. In fact, you may construct an Estimator directly from any Keras model by calling the tf.keras.estimator.model_to_estimatorfunction. With Keras now in TensorFlow core, you can rely on it for your production workflows.

To get started with Keras, please read:

To get started with Estimators, please read:

Datasets

We're pleased to announce that the Dataset API has graduated to core package tf.data(from tf.contrib.data). The 1.4 version of the Dataset API also adds support for Python generators. We strongly recommend using the Dataset API to create input pipelines for TensorFlow models because:

  • The Dataset API provides more functionality than the older APIs (feed_dict or the queue-based pipelines).
  • The Dataset API performs better.
  • The Dataset API is cleaner and easier to use.

We're going to focus future development on the Dataset API rather than the older APIs.

To get started with Datasets, please read:

Distributed Training & Evaluation for Estimators

Release 1.4 also introduces the utility function tf.estimator.train_and_evaluate, which simplifies training, evaluation, and exporting Estimator models. This function enables distributed execution for training and evaluation, while still supporting local execution.

Other Enhancements

Beyond the features called out in this announcement, 1.4 also introduces a number of additional enhancements, which are described in the Release Notes.

Installing TensorFlow 1.4

TensorFlow release 1.4 is now available using standard pipinstallation. 

# Note: the following command will overwrite any existing TensorFlow
# installation.
$ pip install --ignore-installed --upgrade tensorflow
# Use pip for Python 2.7
# Use pip3 instead of pip for Python 3.x

We've updated the documentation on tensorflow.org to 1.4.

TensorFlow depends on contributors for enhancements. A big thank you to everyonehelping out developing TensorFlow! Don't hesitate to join the community and become a contributor by developing the source code on GitHub or helping out answering questions on Stack Overflow.

We hope you enjoy all the features in this release.

Happy TensorFlow Coding!