# Kaggle Published articles for Kaggle. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## IBM and Kaggle launch Java framework migration leaderboard DevFeed: [IBM and Kaggle launch Java framework migration leaderboard]() Original publisher: [Read original article]() Author: Bridget McGinn; Raju Pavuluri; Advait Pavuluri; Rahul Krishna; Srikanth Tamilselvam; Ashita Saxena; George Safta; Michele Merler Published: 2026-10-27T12:00:00Z Content type: news Language: en Sources: [IBM Research]() Topics: [Agent Harness](), [Java](), [Jakarta EE](), [Quarkus](), [Spring Framework](), [Kaggle](), [ibm]() Tags: [agent](), [agent-harness](), [ai](), [ai-for-code](), [ai-for-it](), [enterprise](), [framework](), [harness](), [ibm](), [java](), [java-migration](), [kaggle](), [launch](), [leaderboard](), [migration](), [model](), [technical-note](), [validation]() ### AI overview ScarfBench tests whether coding-agent configurations can migrate enterprise Java applications across Jakarta EE, Quarkus, and Spring while preserving behavior, checking builds, deployment, and behavioral tests. In 204 tasks, Gemini 4 Argon with Antigravity led the smoke-test leaderboard at 75.2%, while Claude Opus 5.5 with Claude Code had the highest all-tests-pass rate at 72.1%; results measure each model paired with its harness, not the model alone. Compilation was much less discriminating than behavioral fidelity: about 72% of 745 classified failures occurred at runtime, including assertion mismatches and lost routes. IBM and Kaggle say Kaggle independently reproduced the evaluation, sponsored compute, and will maintain the benchmark and add models over time. Public submissions were not available at launch but were on the roadmap. ### Source excerpt ScarfBench evaluates model-and-agent-harness combinations for enterprise Java migration, pairing migration validation with reproduced runs and a leaderboard. ## Announcing the verifiable quantum circuit compilation competition on Kaggle DevFeed: [Announcing the verifiable quantum circuit compilation competition on Kaggle]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-10-21T18:30:00Z Content type: release Language: en Sources: [Google Open Source Blog]() Topics: [Quantum Computing](), [Kaggle](), [Test automation]() Tags: [agent](), [agentic](), [agentic-ai](), [ai](), [ai-agent](), [algorithm](), [c-plus-plus-20](), [circuit](), [competition](), [compilation](), [error-correction](), [kaggle](), [open-source](), [python](), [qualtran](), [quantum](), [quantum-ai](), [quantum-circuits](), [quantum-computing](), [qubit](), [size]() ### AI overview The Kaggle competition asks participants to use AI to reduce quantum circuit qubit and gate costs across fusion energy, optimization, and chemistry benchmarks. Each provides an open-source reference circuit and standalone verifier; submissions must pass verification while minimizing a cost score based on qubit and gate counts. The approach focuses on kickmix circuits, a structured class built from reversible classical logic, diagonal phase gates, and measurement-based uncomputation. Because these circuits map each input bitstring to one output bitstring and phase without creating new superpositions, randomized classical fuzz testing can check approximate correctness efficiently. It does not prove exact equivalence, but the source says bounded errors are tolerable in many quantum algorithms and approximations can substantially reduce circuit resources. The excerpt identifies the benchmark tasks: approximate an inverse-square-root phase, build a Reed-Solomon decoder, and co-design a Hamiltonian factorization with compact parameter-loading circuits. Google's kickmix library in Qualtran supports circuit construction and testing; the excerpt does not give full competition rules or score ### Source excerpt by Tanuj Khattar and Ryan Babbush, Google Quantum AI While quantum computing holds the key to solving some of our most complex scientific challenges, current hardware is still limited by qubit counts and gate errors. Quantum error correction can overcome those errors, but it adds substantial overhead to every qubit and gate an algorithm uses. That makes circuit size one of the biggest factors in when a quantum algorithm becomes practical: every qubit and gate we remove lowers the bar that hardware must clear. To bring real-world quantum applications within reach sooner, it is critical to first compress the circuits that power them. To help bridge this gap, the Google Quantum AI team is launching a competition on Kaggle challenging you to use AI to build smaller, more efficient quantum circuits. The competition is powered by kickmix, our new open source C++20 and Python library in Qualtran for efficiently building and testing large quantum circuits. Our companion paper, Leveraging verifiable structure in quantum circuits to find more efficient compilations with agentic AI, describes the approach in detail. Turning compilation into an AI agent loop In the past, optimizing quantum algorithms meant domain experts meticulously tuning circuits by hand and publishing incremental wins one paper at a time. Today, agentic AI can search for more efficient circuits automatically, but such results can only be trusted when candidate circuits can be efficiently verified for correctness. For arbitrary quantum circuits, classical verification is intractable because quantum gates create superpositions over exponentially many classical bitstrings. Fortunately, many of the most expensive subroutines in fault-tolerant algorithms can be built within a structured subclass of circuits, called "kickmix" circuits, which we introduced in our earlier work. Built from reversible classical logic, diagonal phase gates, and measurement-based uncomputation, kickmix circuits map each input bitstring ## Automated Brick Schema Knowledge Graph Completion Across Buildings DevFeed: [Automated Brick Schema Knowledge Graph Completion Across Buildings]() Original publisher: [Read original article]() Author: Daniel Ma, Jalil Huseynov, Xinyi Pan, Shaked Shalev, Zhang Chi, Ai Yamamoto, Sohei Arisaka Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Graph Mining](), [Graphs](), [RDF (Resource Description Framework)](), [knowledge-engineering](), [machine learning overfitting]() Tags: [automated](), [building](), [completion](), [cs-lg](), [data](), [equipment](), [gradient-boosted-trees](), [graph](), [kaggle](), [knowledge-graph](), [machine-learning](), [relationships](), [schema](), [sensors](), [time-series-data]() ### AI overview The study frames missing Brick relationships as a knowledge graph completion problem to reduce the manual work of building Brick models from schematics and point lists. It compares gradient-boosted trees, Pairwise Brick Relation Ranking, and CompGCN for predicting feeds, hasPart, and hasPoint, using held-out MORTAR buildings plus out-of-distribution Kaggle and Building Time Series data. CompGCN performs best on held-out buildings by macro-F1 and AUPRC, but no method generalizes consistently across datasets or relations. ### Source excerpt arXiv:2610.27339v1 Announce Type: new Abstract: The Brick schema represents building equipment, sensors, spaces, and the relationships between them as typed, multi-relational graphs, providing machine-readable metadata that supports generalizable control applications. Current methods for producing complete Brick models require extensive manual examination of building schematics and point lists. We observe that as Brick models are knowledge graphs, inferring missing relations is naturally posed as a knowledge graph completion problem. To the best of our knowledge, this is the first application of knowledge graph completion to Brick model generation. We compare three methods, gradient-boosted trees, Pairwise Brick Relation Ranking, and CompGCN, on the relations feeds, hasPart, and hasPoint, which account for the majority of the schematic-reading work in building a Brick model by hand. Models are evaluated on held-out MORTAR buildings and out-of-distribution Kaggle and Building Time Series data. Although CompGCN is the strongest performer on held-out buildings under metrics resistant to class imbalance, leading macro-F1 and AUPRC, no method generalizes uniformly across datasets or relations. ## Google expands EmbeddingGemma beyond text to images, audio and video DevFeed: [Google expands EmbeddingGemma beyond text to images, audio and video]() Original publisher: [Read original article]() Author: Duncan Riley Published: 2026-10-06T22:18:04Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Google](), [multimodal-ai](), [generative-ai-tools](), [Generative AI](), [gemma]() Tags: [ai](), [ai-edge-foresight](), [ai-edge-gallery](), [apache-2-0](), [coding-agents](), [embedding-model](), [embeddinggemma](), [embeddinggemma-2](), [gemini-enterprise-agent-platform](), [gemma-4](), [google](), [google-pixel-11-pro](), [google-plus-deepmind](), [henrique-schechter-vera](), [hugging-face](), [images](), [kaggle](), [llama-cpp](), [massive-text-embedding-benchmark](), [matryoshka-representation-learning](), [model](), [model-garden](), [multimodal](), [news](), [ollama](), [open](), [open-source](), [quantization](), [release](), [retrieval-augmented-generation](), [sahil-dua](), [smartphone](), [text](), [the-latest](), [top-story-4](), [vector-database](), [video](), [video-moments-finder](), [vllm]() ### AI overview Google released EmbeddingGemma 2, an open multimodal embedding model that brings image, audio and video retrieval into the same embedding space as text and is small enough to run on a smartphone. The article describes its size, memory use, retrieval quality, benchmark results for code, and availability under an Apache 2.0 license. ### Source excerpt Google LLC today released EmbeddingGemma 2, an open multimodal embedding model small enough to run on a smartphone. The release takes the EmbeddingGemma line beyond text, which was all the first version handled when Google introduced it in September 2025. Images, audio and video now share one embedding space with text. An app built on [...] The post Google expands EmbeddingGemma beyond text to images, audio and video appeared first on SiliconANGLE. ## CoreWeave ARIA: The Coding Agent That Drives Continuous Improvement Across the AI Loop DevFeed: [CoreWeave ARIA: The Coding Agent That Drives Continuous Improvement Across the AI Loop]() Original publisher: [Read original article]() Author: CoreWeave Published: 2026-10-02T17:29:55Z Content type: release Language: en Sources: [Coreweave Blog Posts]() Topics: [GitHub Copilot Profiler Agent](), [ML model versioning](), [AI Context Management](), [dora metrics](), [scaling laws](), [Agent Harness](), [AI Agent]() Tags: [agent](), [ai](), [ai-research](), [coding-agent](), [content-type](), [coreweave-aria](), [coreweave-forge](), [evaluations](), [github](), [hugging-face](), [kaggle](), [model]() ### AI overview CoreWeave announces general availability of ARIA, an agent in CoreWeave Forge that analyzes model and agent experiments, proposes follow-up changes, and can launch experiments with user approval. It connects project data, code, and team memory, with configurable automation and visual reports. ### Source excerpt CoreWeave ARIA is generally available in Forge. Whether you're building an agent or training a model, ARIA analyzes your experiments, proposes what to try next, and launches it with your approval. ## Updates on HEIR, the homomorphic encryption compiler project DevFeed: [Updates on HEIR, the homomorphic encryption compiler project]() Original publisher: [Read original article]() Published: 2026-09-04T18:53:40Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [homomorphic encryption](), [Compiler](), [Encryption](), [Cryptography](), [Machine learning](), [Inference](), [GitHub](), [bazel](), [Kaggle]() Tags: [bazel](), [ckks](), [compiler](), [cryptography](), [encryption](), [github](), [homomorphic-encryption](), [inference](), [kaggle](), [mathematics](), [ml](), [programming]() ### AI overview This companion article explains HEIR, a homomorphic encryption compiler that converts programs to operate directly on encrypted data. It discusses compiling pre-trained machine-learning models for private inference, describes the repository and setup, and reports an example involving encrypted credit-card fraud detection. ### Source excerpt On 2026-08-14 I published an article on the Google Security blog with an update on HEIR, our homomorphic encryption (HE) compiler. This is a companion article, in which I have no limits on word count or jargon, and I can feel free to be honest. So strap in. Assuming you won't read the linked corporate blog post, HEIR is a compiler that converts an input program to a program that operates directly on encrypted data. ## Inside our 353,000-person vibe coding course DevFeed: [Inside our 353,000-person vibe coding course]() Original publisher: [Read original article]() Author: Brenda Flynn Published: 2026-08-03T15:00:00Z Content type: article Language: en Sources: [Developer tools](), [Google Cloud]() Topics: [Vibe coding](), [agentic-coding](), [ChatGPT](), [GitHub Copilot]() Tags: [ai](), [ai-developer](), [coding](), [developer-community](), [developer-tools](), [gemini-models](), [google-cloud](), [kaggle](), [none](), [vibe-coding]() ### AI overview Google and Kaggle report that more than 353,000 people registered for their five-day AI Agents intensive, which taught natural-language programming and covered designing, securing, and deploying production-grade AI agents. The recap highlights community collaboration, more than 6,000 capstone submissions, and projects spanning digital humanities and space-weather research. ### Source excerpt Illustrations of a laptop, an AI spark, messages, code, and a 3-D cube ## Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning DevFeed: [Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-14T18:20:32Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Benchmark](), [Chain-of-thought](), [dataset](), [Cloud](), [Google](), [NCCL]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-inference](), [benchmark](), [blackwell](), [chain-of-thought](), [cost](), [deep-learning](), [engineering](), [evaluation](), [featured](), [google](), [kaggle](), [lora](), [nemotron](), [nvidia](), [performance](), [pre-trained-foundation-models](), [synthetic](), [techniques]() ### AI overview The article distills lessons from NVIDIA's Nemotron Model Reasoning Challenge, where more than 5,000 Kaggle participants tested ways to improve AI reasoning under shared model, infrastructure, and evaluation constraints. It highlights synthetic chain-of-thought data, trace quality, targeted solvers, validation beyond public leaderboards, and careful training and context-budget management. ### Source excerpt The NVIDIA Nemotron Model Reasoning Challenge invited the Kaggle community to explore a focused question: What techniques can improve reasoning accuracy when... ## Fine-tuning a DistilBERT classifier with numerical and text inputs DevFeed: [Fine-tuning a DistilBERT classifier with numerical and text inputs]() Original publisher: [Read original article]() Author: Paloma Jol Published: 2026-04-10T10:04:33Z Content type: tutorial Language: en Sources: [FreeAgent]() Topics: [Fine-tuning](), [Transformer](), [AI Foundation Models](), [Training AI Models](), [dataset](), [Kaggle](), [scikit-learn]() Tags: [ai](), [aws](), [data](), [data-ml](), [data-science](), [dataset](), [encoding](), [fine-tuning](), [hugging-face](), [kaggle](), [llms](), [machine-learning](), [model](), [models](), [nlp](), [performance](), [text-classification](), [training-data](), [validation]() ### AI overview This tutorial explains how to fine-tune a DistilBERT text classifier while incorporating a numerical feature into the same network. It uses the Kaggle wine reviews dataset, combining review descriptions with price and preprocessing the data for training, validation, and testing. ### Source excerpt Text classification is often done through fine-tuning of a pretrained foundation model with domain-specific data. In FreeAgent we use transformer based models to automatically classify incoming bank transactions. Specifically we use a DistilBERT model that is fine-tuned on hundreds of millions of bank transactions with customer-labelled accounting categories. The model inputs are currently text-based, built from a combination of bank transaction descriptions and amounts. In this post we describe an approach to fine-tuning the DistilBERT model and training the classifier including the numerical amount feature as a single network. ## Measuring progress toward AGI: A cognitive framework DevFeed: [Measuring progress toward AGI: A cognitive framework]() Original publisher: [Read original article]() Author: Ryan Burnell Published: 2026-03-17T16:03:47Z Content type: news Language: en Sources: [Google DeepMind News]() Topics: [LLM evaluation / benchmarking](), [Machine Intelligence](), [Hackathon](), [Kaggle]() Tags: [agi](), [ai](), [ai-systems](), [artificial-general-intelligence](), [attention](), [benchmarks](), [cognitive-science](), [community](), [design](), [environment](), [evaluations](), [executive](), [framework](), [frontier-models](), [general](), [generation](), [hackathon](), [kaggle](), [measuring](), [none](), [progress]() ### AI overview The framework proposes measuring AI's progress toward AGI across 10 cognitive abilities, from perception and memory to reasoning and social cognition. Its three-stage protocol evaluates systems on a broad set of tasks with held-out tests, collects results from a demographically representative adult sample on the same tasks, and compares AI performance with the human performance distribution for each ability. To address evaluation gaps in learning, metacognition, attention, executive functions, and social cognition, the article announces a Kaggle hackathon using Community Benchmarks to test evaluations against frontier models. The excerpt gives a $200,000 prize pool and submission dates of March 17-April 16, with results due June 1; it does not establish that the proposed framework alone can determine whether a system has achieved AGI. ### Source excerpt We're introducing a framework to measure progress toward AGI, and launching a Kaggle hackathon to build the relevant evaluations. ## DSGym: A holistic framework for evaluating and training data science agents DevFeed: [DSGym: A holistic framework for evaluating and training data science agents]() Original publisher: [Read original article]() Author: Fan Nie, Junlin Wang, Harper Hua, Federico Bianchi, Yongchan Kwon, Zhenting Qi, Owen Queen, Shang Zhu, James Zou Published: 2026-01-26T00:00:00Z Content type: article Language: en Sources: [Together.ai]() Topics: [Data science and engineering](), [Large Language Model](), [Data Science](), [Kaggle](), [AI-assisted coding]() Tags: [agents](), [benchmark](), [code-execution](), [containers](), [data](), [data-science](), [datasets](), [development](), [generation](), [kaggle](), [llm](), [synthetic]() ### AI overview DSGym is a unified framework for evaluating and training LLM based data science agents. It brings together existing benchmarks, adds bioinformatics and Kaggle competition tasks, and uses standardized interfaces and containerized execution. The authors report that synthetic query and trajectory data helped train a 4B open source model that performed competitively among open source models. Their evaluations also find that agents often produce valid machine learning pipelines but struggle to achieve competitive results on harder tasks. ### Source excerpt Introducing DSGym--a holisti evaluation and training framework for LLM-based data science agents. Features 90+ bioinformatics tasks, 92 Kaggle competitions, and synthetic trajectory generation. Our 4B model achieves state-of-the-art performance among open-source models through exe ## Next generation medical image interpretation with MedGemma 1.5 and medical speech to text with MedASR DevFeed: [Next generation medical image interpretation with MedGemma 1.5 and medical speech to text with MedASR]() Original publisher: [Read original article]() Published: 2026-01-13T20:57:16Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Medical imaging](), [AI Models](), [asr](), [Generative AI](), [Google](), [hugging face](), [Cloud](), [Kaggle]() Tags: [ai-models](), [asr](), [generative-ai](), [google](), [google-cloud](), [health](), [health-bioscience](), [healthcare](), [hugging-face](), [kaggle](), [machine-intelligence](), [medical-imaging]() ### AI overview Google Research describes MedGemma 1.5 4B, an updated open medical generative AI model with improved support for medical imaging, text, medical records, and 2D images. The article also presents MedASR, an open medical speech-to-text model for dictation that can pair with MedGemma for advanced reasoning. The models are available for research and commercial use through Hugging Face and Vertex AI, with a related medical AI hackathon on Kaggle. ### Source excerpt Generative AI ## Google Research 2025: Bolder breakthroughs, bigger impact DevFeed: [Google Research 2025: Bolder breakthroughs, bigger impact]() Original publisher: [Read original article]() Published: 2025-12-18T23:29:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Artificial Intelligence](), [Large Language Model](), [Quantum Computing](), [Algorithms](), [data centers](), [Cloud](), [Bioinformatics](), [Benchmark](), [Kaggle]() Tags: [2025](), [agentic](), [ai](), [benchmarks](), [cloud-data](), [data-centers](), [efficiency](), [energy](), [generative](), [google](), [health](), [infrastructure](), [kaggle](), [llm](), [quantum](), [quantum-computing](), [research](), [resilience](), [science](), [year-in-review]() ### AI overview Google Research's 2025 review describes accelerated progress across AI, generative UI, algorithmic research, agentic models, quantum computing, Earth sciences, genomics, biology, neuroscience, climate resilience, health, and education. It also highlights work improving efficiency, reliability, factuality, and cost and energy use in Google products and cloud infrastructure. ### Source excerpt Year in Review ## FACTS Benchmark Suite: Systematically evaluating the factuality of large language models DevFeed: [FACTS Benchmark Suite: Systematically evaluating the factuality of large language models]() Original publisher: [Read original article]() Author: FACTS team Published: 2025-12-09T11:29:03Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [LLM evaluation / benchmarking](), [Benchmark](), [Large Language Model](), [Kaggle](), [multimodal-ai]() Tags: [benchmark](), [benchmarks](), [evaluation](), [kaggle](), [language-models](), [large-language-models](), [leaderboard](), [multimodal](), [responsibility-safety](), [search](), [web]() ### AI overview Google DeepMind and Kaggle introduce the FACTS Benchmark Suite, which evaluates the factuality of large language models across internal-knowledge, search, multimodal, and grounded-answering tasks. The suite contains 3,513 publicly available examples, with held-out private sets used to calculate an overall FACTS Score and publish results on a leaderboard. ### Source excerpt Systematically evaluating the factuality of large language models with the FACTS Benchmark Suite. ## Improving Hugging Face Model Access for Kaggle Users DevFeed: [Improving Hugging Face Model Access for Kaggle Users]() Original publisher: [Read original article]() Author: Vincent Roseberry; Meg Risdal; Julien Chaumond; Pedro Cuenca; Vaibhav Srivastav Published: 2025-05-14T00:00:00Z Content type: article Language: en Sources: [Hugging Face]() Topics: [hugging face](), [Kaggle](), [qwen](), [spaces]() Tags: [add-ons](), [announcement](), [code](), [community](), [evaluation](), [hub](), [hugging-face](), [kaggle](), [model](), [qwen](), [qwen3](), [secrets](), [spaces](), [token]() ### AI overview Kaggle is integrating Hugging Face models into its platform, improving model discovery, navigation, and reuse in Kaggle Notebooks. Public notebooks using Hugging Face models can contribute code examples to Kaggle model pages, while private and gated models continue to require Hugging Face authentication. Support for offline Kaggle competition submissions is still in development. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The TensorFlow Lite Plugin for Flutter is Officially Available DevFeed: [The TensorFlow Lite Plugin for Flutter is Officially Available]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-08-18T16:00:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [Flutter](), [LiteRT](), [Tensorflow](), [cross-platform](), [Mobile](), [object-detection](), [GitHub](), [Google](), [Kaggle]() Tags: [announcement](), [cross-platform](), [explore](), [flutter](), [github](), [google](), [inference](), [kaggle](), [mobile](), [object-detection](), [plugin](), [tensorflow](), [tensorflow-lite](), [tensorflowlite](), [tutorial]() ### AI overview The TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released. The updated plugin adds features and example apps, including live-camera object detection, and enables local TensorFlow model inference in Flutter apps across supported platforms. ### Source excerpt Posted by Paul Ruiz, Developer Relations Engineer We're excited to announce that the TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released! Three years ago, Amish Garg, one of our talented Google Summer of Code contributors, wrote a widely used TensorFlow Lite plugin for Flutter. The plugin was so popular that we decided to migrate it to our official repo, making it easier to maintain directly by the Google team. We are grateful to Amish for his contributions to the TensorFlow Lite Flutter plugin. Through the efforts of developers in the community, the plugin has been updated to the latest version of TensorFlow Lite, and a collection of new features and example apps have been added, such as object detection through a live camera feed. So what is TensorFlow Lite? TensorFlow Lite is a way to run TensorFlow models on devices locally, supporting mobile, embedded, web, and edge devices. TensorFlow Lite's cross-platform support and on-device performance optimizations make it a great addition to the Flutter development toolbox. Our goal with this plugin is to make it easy to integrate TensorFlow Lite models into Flutter apps across mobile platforms, with desktop support currently in development through the efforts of our developer community. Find pre-trained TensorFlow Lite models on model repos like Kaggle Models or create your own custom TensorFlow Lite models. Let's take a look at how you could use the Flutter TensorFlow Lite plugin for image classification: TensorFlow Lite Image Classification with Flutter First you will need to install the plugin from pub.dev. Once the plugin is installed, you can load a TensorFlow Lite model into your Flutter app and define the input and output tensor shapes. If you're using the MobileNet model, then the input tensor will be a 224 by 224 RGB image, and the output will be a list of confidence scores for the trained labels. // Load model Future _loadModel() async { final opt ## Hand Gesture Recognition on ESP32-S3 with ESP-Deep Learning DevFeed: [Hand Gesture Recognition on ESP32-S3 with ESP-Deep Learning]() Original publisher: [Read original article]() Author: John Lee Published: 2022-12-02T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32-S3](), [Deep learning](), [Artificial Intelligence](), [ESP-IDF](), [dataset](), [Model Development](), [quantization]() Tags: [ai](), [blog](), [classification](), [deep-learning](), [esp-dl](), [esp-id](), [esp32](), [esp32-s3](), [espressif](), [kaggle](), [quantization]() ### AI overview This tutorial explains how to deploy a deep-learning model for classifying six hand gestures on the ESP32-S3 using Espressif's ESP-DL framework. It covers prerequisites, model development, dataset preparation, train/test/calibration splits, and model quantization. ### Source excerpt Artificial intelligence transforms the way computers interact with the real world. Decisions are carried by getting data from Tiny low-powered devices and sensors into the cloud. Connectivity, high cost and data privacy are some of the demerits of this method. Edge artificial intelligence is another way to process the data right on the physical device without sending data back and forth improving the latency and security and reducing the bandwidth and power. ## Bootstrapping Training Labels with Semi-Supervised, Active, and Weakly Supervised Learning DevFeed: [Bootstrapping Training Labels with Semi-Supervised, Active, and Weakly Supervised Learning]() Original publisher: [Read original article]() Author: Eugene Yan Published: 2021-08-01T00:00:00Z Content type: tutorial Language: en Sources: [Eugene Yan]() Topics: [Artificial Intelligence](), [data-engineering](), [Machine learning](), [dataset](), [data](), [Kaggle]() Tags: [dataset](), [human-in-the-loop](), [kaggle](), [labeling](), [machine-learning](), [machinelearning](), [supervised-learning](), [teardown]() ### AI overview This tutorial explains how to create training labels when labeled datasets are unavailable. It covers semi-supervised, active, and weakly supervised learning, including human-in-the-loop labeling, uncertainty sampling, query-by-committee, and information density. ### Source excerpt How to generate labels from scratch with semi, active, and weakly supervised learning. ## How to Win a Data Hackathon (Hacklytics 2021) DevFeed: [How to Win a Data Hackathon (Hacklytics 2021)]() Original publisher: [Read original article]() Author: Eugene Yan Published: 2021-02-14T00:00:00Z Content type: tutorial Language: en Sources: [Eugene Yan]() Topics: [Hackathon](), [Data Science](), [dataset](), [Kaggle](), [Demo]() Tags: [data-science](), [datascience](), [datasets](), [demo](), [engineering](), [hackathon](), [kaggle](), [misc](), [visualization]() ### AI overview A mentor and judge at Hacklytics 2021 explains patterns among successful teams in a 36-hour data hackathon. The article discusses judging criteria, project presentation, deployment readiness, visualization quality, and the value of using clean, publicly available datasets to save time. ### Source excerpt What the top teams did to win the 36-hour data hackathon. No, not machine learning. ## DataTalksClub - The Importance Of Writing In A Tech Career DevFeed: [DataTalksClub - The Importance Of Writing In A Tech Career]() Original publisher: [Read original article]() Author: Eugene Yan Published: 2021-01-17T00:00:00Z Content type: article Language: en Sources: [Eugene Yan]() Topics: [Data Science](), [Machine learning](), [Learning](), [ibm](), [Kaggle](), [amazon]() Tags: [amazon](), [career](), [data-science](), [data-science-machine-learning](), [ibm](), [kaggle](), [tech](), [writing]() ### AI overview A DataTalksClub podcast transcript about the role of writing in a technology career. The discussion covers the guest's reasons for writing, writing process, how to begin, writing culture at Amazon, and a career path through data science, machine learning, and product work. ### Source excerpt Why did I start writing? What's my writing process? What's the writing culture at Amazon like? ## Mailbag: How to Get Experienced DS Resume Noticed by Recruiters? DevFeed: [Mailbag: How to Get Experienced DS Resume Noticed by Recruiters?]() Original publisher: [Read original article]() Author: Eugene Yan Published: 2021-01-16T00:00:00Z Content type: opinion Language: en Sources: [Eugene Yan]() Topics: [Job](), [Kaggle](), [Learning](), [Reinforcement learning]() Tags: [career](), [datascience](), [education](), [interviews](), [kaggle](), [project](), [reinforcement-learning](), [resume](), [side-projects]() ### AI overview A data scientist asks what experienced data scientists should include on their resumes to improve their chances of attracting recruiters. The discussion considers Kaggle competitions, personal projects with real business applications, school projects from an OMSCS program, volunteer work, and relying on achievements from current employment. ### Source excerpt How to increase the chances of getting called up by recruiters? ## Announcing Our Market Prediction Kaggle Competition DevFeed: [Announcing Our Market Prediction Kaggle Competition]() Original publisher: [Read original article]() Author: Craig Falls Published: 2020-11-24T00:00:00Z Content type: release Language: en Sources: [Jane Street]() Topics: [Kaggle](), [Machine learning](), [data](), [Data Science]() Tags: [competition](), [data](), [data-science](), [financial](), [hiring](), [kaggle](), [london](), [machine-learning](), [ml]() ### AI overview Jane Street announces a Kaggle competition based on a real problem and real financial data, with $100,000 in total prizes. The company says the contest aims to introduce more people to its data-science work and encourage applications. ### Source excerpt Jane Street is running a Kaggle contest based on a real problem with real financial data. If you like ML projects, or think you might, head over and check it out. We think it's a pretty fun one. The prizes are pretty good too, with a total $100K being paid out. ## Datacast Podcast - Effective Data Science with Eugene Yan DevFeed: [Datacast Podcast - Effective Data Science with Eugene Yan]() Original publisher: [Read original article]() Author: Eugene Yan Published: 2020-09-03T00:00:00Z Content type: article Language: en Sources: [Eugene Yan]() Topics: [Data Science](), [Artificial Intelligence](), [Machine learning](), [Self-organizing Team](), [Kaggle]() Tags: [agile](), [career](), [data-science](), [datascience](), [ibm](), [kaggle](), [leadership](), [machine-learning](), [ml](), [podcast]() ### AI overview A podcast conversation about Eugene Yan's transition from psychology into data science, his work at IBM and Lazada, leadership, agile practices, machine learning in production, experimentation, and writing. ### Source excerpt My chat with James Le about my experience, leadership, agile, ML in production, writing, and more. ## Machine Learning Datasets for NLP DevFeed: [Machine Learning Datasets for NLP]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-03-09T00:00:00Z Content type: article Language: en Sources: [Scott Johnson]() Topics: [dataset](), [Dataset development](), [Machine learning]() Tags: [datasets](), [kaggle](), [keep](), [learning](), [machine](), [machine-learning](), [netlabeler](), [pointers](), [review](), [sample](), [stream](), [twitter]() ### AI overview The excerpt lists Kaggle, LionBridge.ai, the Million Song Dataset, Synced Review, and Twitter Sample Stream as dataset pointers for machine learning. It provides no descriptions or comparison of these sources. ### Source excerpt Today is a short one, just pointers to some datasets for Machine Learning data sets that I keep losing. Kaggle LionBridge.ai Million Song Dataset Synced Review Twitter Sample Stream [Next page]()