[ad_1]
Posted by Nari Yoon, Bitnoori Keum, Hee Jung, DevRel Group Supervisor / Soonson Kwon, DevRel Program Supervisor
Let’s discover highlights and accomplishments of huge Google Machine Studying communities over the second quarter of 2023. We’re enthusiastic and grateful about all of the actions by the worldwide community of ML communities. Listed below are the highlights!
ML Coaching Campaigns Abstract
Greater than 35 communities around the globe have hosted ML Campaigns distributed by the ML Developer Packages crew through the first half of the 12 months. Thanks all in your coaching efforts for the complete ML neighborhood!
- ML Research Jams: TFUG Bauchi, GDSC Uninter, TFUG Abidjan, MLAct, Universitas Pendidikan Indonesia, Nationwide Institute of Know-how (Kosen), Kumamoto Faculty, GDG Assiut, GDG Bassam, GDG Cloud Abidjan, GDG Antananarivo, Madan Mohan Malaviya College of Know-how – Gorakhpur, Université d’Abomey-Calavi (UAC), ABES Engineering Faculty – Ghaziabad, ABV-IIITM, Vishwakarma College – Pune, Pimpri Chinchwad Faculty of Engineering and Analysis – Pune, GDG Cloud Edmonton, GDG Cocody, GDG Cloud Wilmington, College of Lay Adventist of Kigali
- ML Paper Studying Golf equipment: GalsenAI, TFUG Dhaka, Pseudo Lab, TFUG Durg, TFUG Ibadan, Universidad Nacional de Ingeniería, GDG Karaganda, Western College, GDG Raipur, College Faculty Dublin
- ML Math Golf equipment: TFUG Dhaka, TFUG Hajipur, GDG Yangon, GalsenAI
Group Highlights
Keras
 |
Picture Segmentation utilizing Composable Absolutely-Convolutional Networks by ML GDE Suvaditya Mukherjee (India) is a Kears.io instance explaining easy methods to implement a fully-convolutional community with a VGG-16 backend and easy methods to use it for performing picture segmentation. His presentation, KerasCV for the Younger and Stressed (slides | video) at TFUG Malaysia and TFUG Kolkata was an introduction to KerasCV. He mentioned how fundamental pc imaginative and prescient elements work, why Keras is a crucial software, and the way KerasCV builds on high of the established TFX and Keras ecosystem.
[ML Story] My Keras Chronicles by ML GDE Aritra Roy Gosthipaty (India) summarized his story of stepping into deep studying with Keras. He included pointers as to how one may get into the open supply neighborhood. Plus, his Kaggle pocket book, [0.11] keras starter: unet + tf knowledge pipeline is a starter information for Vesuvius Problem. He and Subvaditya additionally shared Keras implementation of Temporal Latent Bottleneck Networks, proposed in the paper.
KerasFuse by ML GDE Ayse Ayyuce Demirbas (Portugal) is a Python library that mixes the ability of TensorFlow and Keras with numerous pc imaginative and prescient methods for medical picture evaluation duties. It gives a set of modules and features to facilitate the event of deep studying fashions in TensorFlow & Keras for duties corresponding to picture segmentation, classification, and extra.
TensorFlow at Google I/O 23: A Preview of the New Options and Instruments by TFUG Ibadan explored the preview of the newest options and instruments in TensorFlow. They lined a variety of subjects together with Dtensor, KerasCV & KerasNLP, TF quantization API, and JAX2TF.
 |
StableDiffusion – Textual-Inversion implementation app by ML GDE Dimitre Oliveira (Brazil) is an instance of easy methods to implement code from analysis and fine-tunes it utilizing the Textual Inversion course of. It additionally gives related use circumstances for worthwhile instruments and frameworks corresponding to HuggingFace, Gradio, TensorFlow serving, and KerasCV.
In Understanding Gradient Descent and Constructing an Picture Classifier in TF From Scratch, ML GDE Tanmay Bakshi (Canada) talked about easy methods to develop a strong instinct for the basics backing ML tech, and truly constructed an actual picture classification system for canine and cats, from scratch in TF.Keras.
TensorFlow and Keras Implementation of the CVPR 2023 paper by Usha Rengaraju (India) is a analysis paper implementation of BiFormer: Imaginative and prescient Transformer with Bi-Degree Routing Consideration.
Smile Detection with Python, OpenCV, and Deep Studying by Rouizi Yacine is a tutorial explaining easy methods to use deep studying to construct a extra strong smile detector utilizing TensorFlow, Keras, and OpenCV.
Kaggle
 |
ML Olympiad for College students by GDSC UNINTER was for college students and aspiring ML practitioners who need to enhance their ML abilities. It consisted of a problem of predicting US working visa purposes. 320+ attendees registered for the opening occasion, 700+ views on YouTube, 66 groups competed, and the winner acquired a 71% F1-score.
ICR | EDA & Baseline by ML GDE Ertuğrul Demir (Turkey) is a starter pocket book for newcomers within the newest featured code competitors on Kaggle. It acquired 200+ Upvotes and 490+ forks.
 |
Compete Extra Successfully on Kaggle utilizing Weights and Biases by TFUG Hajipur was a meetup to discover methods utilizing Weights and Biases to enhance mannequin efficiency in Kaggle competitions. Usha Rengaraju (India) joined as a speaker and delivered her insights on Kaggle and techniques to win competitions. She shared ideas and tips and demonstrated easy methods to arrange a W&B account and easy methods to combine with Google Colab and Kaggle.
Skeleton Primarily based Motion Recognition: A failed try by ML GDE Ayush Thakur (India) is a dialogue publish about documenting his learnings from competing within the Kaggle competitors, Google – Remoted Signal Language Recognition. He shared his repository, coaching logs, and concepts he approached within the competitors. Plus, his article Keras Dense Layer: Tips on how to Use It Appropriately) explored what the dense layer in Keras is and the way it works in observe.
On-device ML
 |
Add Machine Studying to your Android App by ML GDE Pankaj Rai (India) at Tech Talks for Educators was a session on on-device ML and easy methods to add ML capabilities to Android apps corresponding to object detection and gesture detection. He defined capabilities of ML Equipment, MediaPipe, TF Lite and easy methods to use these instruments. 700+ individuals registered for his speak.
In MediaPipe with a little bit of Bard at I/O Prolonged Singapore 2023, ML GDE Martin Andrews (Singapore) shared how MediaPipe suits into the ecosystem, and confirmed 4 totally different demonstrations of MediaPipe performance: audio classification, facial landmarks, interactive segmentation, and textual content classification.
Including ML to our apps with Google ML Equipment and MediaPipe by ML GDE Juan Guillermo Gomez Torres (Bolivia) launched ML Equipment & MediaPipe, and the advantages of on-device ML. In Startup Academy México (Google for Startups), he shared easy methods to enhance the worth for purchasers with ML and MediaPipe.
LLM
Introduction to Google’s PaLM 2 API by ML GDE Hannes Hapke (United States) launched easy methods to use PaLM2 and summarized main benefits of it. His one other article The function of ML Engineering within the time of GPT-4 & PaLM 2 explains the function of ML specialists find the fitting stability and alignment amongst stakeholders to optimally navigate the alternatives and challenges posed by this rising expertise. He did shows underneath the identical title at North America Join 2023 and the GDG Portland occasion.
 |
ChatBard : An Clever Buyer Service Middle App by ML GDE Ruqiya Bin Safi (Saudi Arabia) is an clever customer support heart app powered by generative AI and LLMs utilizing PaLM2 APIs.
Bard can now code and put that code in Colab for you by ML GDE Sam Witteveen (Singapore) confirmed how Bard makes code. He runs a Youtube channel exploring ML and AI, with playlists corresponding to Generative AI, Paper Evaluations, LLMs, and LangChain.
Google’s Bard Can Write Code by ML GDE Bhavesh Bhatt (India) reveals the coding capabilities of Bard, easy methods to create a 2048 sport with it, and easy methods to add some fundamental options to the sport. He additionally uploaded movies about LangChain in a playlist and launched Google Cloud’s new course on Generative AI in this video.
 |
Consideration Mechanisms and Transformers by GDG Cloud Saudi talked about Consideration and Transformer in NLP and ML GDE Ruqiya Bin Safi (Saudi Arabia) participated as a speaker. One other occasion, Arms-on with the PaLM2 API to create good apps(Jeddah) explored what LLMs, PaLM2, and Bard are, easy methods to use PaLM2 API, and easy methods to create good apps utilizing PaLM2 API.
Arms-on with Generative AI: Google I/O Prolonged [Virtual] by ML GDE Henry Ruiz (United States) and Net GDE Rabimba Karanjai (United States) was a workshop on generative AI displaying hands-on demons of easy methods to get began utilizing instruments corresponding to PaLM API, Hugging Face Transformers, and LangChain framework.
Generative AI with Google PaLM and MakerSuite by ML GDE Kuan Hoong (Malaysia) at Google I/O Prolonged George City 2023 was a discuss LLMs with Google PaLM and MakerSuite. The occasion hosted by GDG George City and in addition included ML subjects corresponding to LLMs, accountable AI, and MLOps.
 |
Intro to Gen AI with PaLM API and MakerSuite by TFUG São Paulo was for individuals who need to be taught generative AI and the way Google instruments will help with adoption and worth creation. They lined easy methods to begin prototyping Gen AI concepts with MakerSuite and easy methods to entry superior options of PaLM2 and PaLM API. The group additionally hosted Opening Pandora’s field: Understanding the paper that revolutionized the sector of NLP (video) and ML GDE Pedro Gengo (Brazil) and ML GDE Vinicius Caridá (Brazil) shared the key behind the well-known LLM and different Gen AI fashions.The group members studied Consideration Is All You Want paper collectively and discovered the total potential that the expertise can supply.
Language fashions which PaLM can converse, see, transfer, and perceive by GDG Cloud Taipei was for individuals who need to perceive the idea and utility of PaLM. ML GED Jerry Wu (Taiwan) shared the PaLM’s foremost traits, features, and and so forth.
 |
Serving With TF and GKE: Steady Diffusion by ML GDE Chansung Park (Korea) and ML GDE Sayak Paul (India) discusses how TF Serving and Kubernetes Engine can serve a system with on-line deployment. They broke down Steady Diffusion into foremost elements and the way they affect the following consideration for deployment. Then additionally they lined the deployment-specific bits corresponding to TF Serving deployment and k8s cluster configuration.
TFX + W&B Integration by ML GDE Chansung Park (Korea) reveals how KerasTuner can be utilized with W&B’s experiment monitoring characteristic throughout the TFX Tuner part. He developed a customized TFX part to push a full-trained mannequin to the W&B Artifact retailer and publish a working utility on Hugging Face House with the present model of the mannequin. Additionally, his speak titled, ML Infra and Excessive Degree Framework in Google Cloud Platform, delivered what MLOps is, why it’s arduous, why cloud + TFX is an effective starter, and the way TFX is seamlessly built-in with Vertex AI and Dataflow. He shared use circumstances from the previous tasks that he and ML GDE Sayak Paul (India) have finished within the final 2 years.
Open and Collaborative MLOps by ML GDE Sayak Paul (India) was a discuss why openness and collaboration are two essential facets of MLOps. He gave an outline of Hugging Face Hub and the way it integrates effectively with TFX to advertise openness and collaboration in MLOps workflows.
ML Analysis
Paper evaluation: PaLM 2 Technical Report by ML GDE Grigory Sapunov (UK) seemed into the main points of PaLM2 and the paper. He shares evaluations of papers associated to Google and DeepMind by his social channels and listed below are a few of them: Mannequin analysis for excessive dangers (paper), Quicker sorting algorithms found utilizing deep reinforcement studying (paper), Energy-seeking might be possible and predictive for skilled brokers (paper).
Studying JAX in 2023: Half 3 — A Step-by-Step Information to Coaching Your First Machine Studying Mannequin with JAX by ML GDE Aritra Roy Gosthipaty (India) and ML GDE Ritwik Raha (India) reveals how JAX can practice linear and nonlinear regression fashions and the utilization of PyTrees library to coach a multilayer perceptron mannequin. As well as, at Might 2023 Meetup hosted by TFUG Mumbai, they gave a chat titled Decoding Finish to Finish Object Detection with Transformers and lined the structure of the mode and the varied elements that led to DETR’s inception.
20 steps to coach a deployed model of the GPT mannequin on TPU by ML GDE Jerry Wu (Taiwan) shared easy methods to use JAX and TPU to coach and infer Chinese language question-answering knowledge.
 |
Multimodal Transformers – Customized LLMs, ViTs & BLIPs by TFUG Singapore checked out what fashions, techniques, and methods have come out lately associated to multimodal duties. ML GDE Sam Witteveen (Singapore) seemed into numerous multimodal fashions and techniques and how one can construct your personal with the PaLM2 Mannequin. In June, this group invited Blaise Agüera y Arcas (VP and Fellow at Google Analysis) and shared the Cerebra venture and the analysis happening at Google DeepMind together with the present and future developments in generative AI and rising traits.
TensorFlow
Coaching a suggestion mannequin with dynamic embeddings by ML GDE Thushan Ganegedara (Australia) explains easy methods to construct a film recommender mannequin by leveraging TensorFlow Recommenders (TFRS) and TensorFlow Recommenders Addons (TFRA). The first focus was to indicate how the dynamic embeddings offered within the TFRA library can be utilized to dynamically develop and shrink the dimensions of the embedding tables within the suggestion setting.
 |
How I constructed essentially the most environment friendly deepfake detector on the planet for $100 by ML GDE Mathis Hammel (France) was a chat exploring a technique to detect photos generated through ThisPersonDoesNotExist.com and even a strategy to know the precise time the picture was produced. Plus, his Twitter thread, OSINT Investigation on LinkedIn, investigated a community of faux corporations on LinkedIn. He used a selfmade software primarily based on a TensorFlow mannequin and hosted it on Google Cloud. Technical explanations of generative neural networks had been additionally included. Greater than 701K individuals considered this thread and it acquired 1200+ RTs and 3100+ Likes.
 |
Few-shot studying: Making a real-time object detection utilizing TensorFlow and Python by ML GDE Hugo Zanini (Brazil) reveals easy methods to take footage of an object utilizing a webcam, label the photographs, and practice a few-shot studying mannequin to run in real-time. Additionally, his article, Customized YOLOv7 Object Detection with TensorFlow.js explains how he skilled a customized YOLOv7 mannequin to run it straight within the browser in actual time and offline with TensorFlow.js.
 |
The Lord of the Phrases : The Return of the experiments with DVC (slides) by ML GDE Gema Parreno Piqueras (Spain) was a chat explaining Transformers within the neural machine studying state of affairs, and easy methods to use Tensorflow and DVC. Within the venture, she used Tensorflow Datasets translation catalog to load knowledge from numerous languages, and TensorFlow Transformers library to coach a number of fashions.
Speed up your TensorFlow fashions with XLA (slides) and Ship quicker TensorFlow fashions with XLA by ML GDE Sayak Paul (India) shared easy methods to speed up TensorFlow fashions with XLA in Cloud Group Days Kolkata 2023 and Cloud Group Days Pune 2023.
Setup of NVIDIA Merlin and Tensorflow for Advice Fashions by ML GDE Rubens Zimbres (Brazil) offered a evaluation of advice algorithms in addition to the Two Towers algorithm, and setup of NVIDIA Merlin on premises and on Vertex AI.
Cloud
AutoML pipeline for tabular knowledge on VertexAI in Go by ML GDE Paolo Galeone (Italy) delved into the event and deployment of tabular fashions utilizing VertexAI and AutoML with Go, showcasing the precise Go code and sharing insights gained by trial & error and in depth Google analysis to beat documentation limitations.
 |
Past photos: looking data in movies utilizing AI (slides) by ML GDE Pedro Gengo (Brazil) and ML GDE Vinicius Caridá (Brazil) confirmed easy methods to create a search engine the place you possibly can seek for data in movies. They offered an structure the place they transcribe the audio and caption the frames, convert this textual content into embeddings, and save them in a vector DB to have the ability to search given a consumer question.
The key sauce to creating wonderful ML experiences for builders by ML GDE Gant Laborde (United States) was a podcast sharing his “aha” second, 20 years of expertise in ML, and the key to creating satisfying and significant experiences for builders.
What’s inside Google’s Generative AI Studio? by ML GDE Gad Benram (Portugal) shared the preview of the brand new options and what you possibly can anticipate from it. Moreover, in Tips on how to pitch Vertex AI in 2023, he shared the six easy and sincere gross sales pitch factors for Google Cloud representatives on easy methods to persuade prospects that Vertex AI is the fitting platform.
In Tips on how to construct a conversational AI Augmented Actuality Expertise with Sachin Kumar, ML GDE Sachin Kumar (Qatar) talked about easy methods to construct an AR app combining a number of applied sciences like Google Cloud AI, Unity, and and so forth. The session walked by the step-by-step strategy of constructing the app from scratch.
 |
Machine Studying on Google Cloud Platform by ML GDE Nitin Tiwari (India) was a mentoring aiming to supply college students with an in-depth understanding of the processes concerned in coaching an ML mannequin and deploying it utilizing GCP. In Constructing strong ML options with TensorFlow and GCP, he shared easy methods to leverage the capabilities of GCP and TensorFlow for ML options and deploy customized ML fashions.
Information to AI on Google cloud: Auto ML, Gen AI, and extra by TFUG Prayagraj educated college students on easy methods to leverage Google Cloud’s superior AI applied sciences, together with AutoML and generative AI.
[ad_2]