Results for “machine-learning”

Provides an adaptive methodology for adding machine learning models to existing codebases, covering problem framing, data readiness, architectural decoupling, and baseline model integration.

Integrates on-device and cloud machine learning into Flutter apps with TensorFlow Lite and Firebase ML Kit, covering image classification, object detection, OCR, face detection, and barcode scanning.

Evaluates machine learning classifiers on their ability to distinguish signal from background in particle physics simulations, measuring how well algorithms rank signal events above background ones using the AUC metric.

Predict continuous values from temporal sequences using aeon's time series regressors, covering convolutional, deep learning, distance-based, feature-based, hybrid, interval-based, and shapelet-based approaches.

Build and deploy machine learning models with TensorFlow, covering Keras, data pipelines, and production serving.

High-performance numerical computing with JAX, covering functional transformations, Flax NNX, and best practices for ML research.

Enforces rigorous ML modeling, feature engineering, training, and evaluation standards at principal-engineer level.

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow.

Explains machine learning model predictions using SHAP values, covering feature importance, visualizations, debugging, bias analysis, and production deployment.

Manages the machine learning lifecycle with experiment tracking, model versioning, reproducible runs, and deployment through the MLflow platform.

Computes the InfoLM metric from torchmetrics for evaluating text generation against ground truth, with configurable information measures and sentence-level scoring.

Train, evaluate, export, and run inference for Masked Auto-Encoder (MAE) models for self-supervised pretraining and fine-tuning of visual representations.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.

Performs time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using the aeon toolkit.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Provides scikit-learn compatible algorithms for time series machine learning, including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

Train or fine-tune sentence-transformers models for retrieval, similarity, clustering, classification, and reranking, with support for bi-encoders, cross-encoders, and sparse encoders.

Trains and fine-tunes vision models for object detection, image classification, and segmentation using Hugging Face Transformers on cloud GPUs, with automatic dataset validation and Hub persistence.

Train, evaluate, distill, quantize, export, and run inference for PyTorch-based TAO image classification models with support for multiple backbones.

Train, evaluate, export, and run inference for pose classification models using ST-GCN on skeleton keypoint sequences.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks, including model serving, feature engineering, A/B testing, and monitoring.

Track ML experiments, manage the model registry with versioning, deploy models, and reproduce experiments using MLflow's framework-agnostic platform.

Perform time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using a scikit-learn compatible Python toolkit.

Provides guidance on using PyTorch for deep learning, covering tensors, autograd, nn.Module, DataLoaders, and best practices.

Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery, for dataframe and ML workflows.

Access AI-ready drug discovery datasets, benchmarks, and molecular oracles from Therapeutics Data Commons for therapeutic machine learning and pharmacological prediction.

Train quantum circuits like neural networks with automatic differentiation, device-independent programming, and integration with PyTorch or JAX.

Runs time series machine learning tasks—classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search—using the scikit-learn compatible aeon toolkit.