370 skills found · Page 10 of 13
ceylon-ai-projects / Data Stream GymAny Stream to Reinforcement Learning Environment (Time Series Data, Stock Market )
briancline / Crm114 PythonA Python module for The CRM-114 Discriminator, which handles learning and classification of text streams.
VinayTeki / Semantic SegmentationKERAS: Multimodal Deep Learning for Semantic Segmentation (RGB, NIR Streams) - multiple architectures
jaumpedro214 / Ml Streming Kafka CdcDeploying a Machine Learning model streaming application with Apache Kafka
Keycatowo / Streamlit 30days Learning學習Streamlit
danielegrattarola / Cdt Ccm AaeChange Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds (2018, https://arxiv.org/abs/1805.06299)
Full-Stack-Data-Science / Real Time Ml Inference With Spark Streaming And KafkaFSDS Webinar 1: Real-Time Machine Learning Inference with Spark Streaming and Kafka
officialarijit / Fed ReMECS MqttA Federated Learning Method for Real-time Emotion State Classification from Multi-modal Streaming
deepankarvarma / Age And Gender Detection OpenCV Keras TensorFlowThis repository contains Python code for an age and gender detection project using the video stream from the camera. The model is trained on the UTKFace dataset and utilizes deep learning techniques for accurate age and gender estimation. The code includes scripts for data preprocessing, model training, and real-time detection from the camera.
dlp3d-ai / Audio2faceAudio2Face is a real-time audio-to-face animation service that converts streaming audio input into synchronized facial animation data. The system uses advanced machine learning models to extract audio features and generate corresponding facial expressions.
JoshWrites / KidsVMKids Ubuntu VM Setup A complete Ubuntu virtual machine configuration designed for children, featuring dedicated GPU passthrough for gaming, educational software, and streamlined access to streaming services. Perfect for providing kids with a safe, controlled computing environment for schoolwork, entertainment, and learning.
JasonChennn / Face Expression Recognition For Remote Classroom Concentration Detection一個基於Python的人臉表情影像辨識深度學習，原理則是將原本的七個表情，轉化為上課程度的表現，並可以實現即時串流。 A deep learning of facial expression image recognition based on python, the principle is to convert the original seven expressions into a class-level performance, and real-time streaming can be realized.
Mamtapriya / Linear Regression Of Data Driven BatteryMachine-learning approach In this work, author has developed data-driven models that accurately predict the cycle life of commercial lithium iron phosphate (LFP)/ graphite cells using early-cycle data, with no prior knowledge of degradation mechanisms. To build an early-prediction model, a feature-based approach is used. Features, such as initial discharge capacity, charge time, and cell can temperature, are generated and used in a regularized linear framework and proposed from domain knowledge of lithium-ion batteries. Several features are calculated based on the discharge voltage curve to capture the electrochemical evolution of individual cells during cycling. For the Q(V), the discharge voltage curves of each cell, summary statistics such as minimum, mean, and variance are determined. The change in voltage curves between two cycles is captured by each summary statistic. Three alternative models have been studied due to the great predictive potential of features based on Q100-10(V). (1) variance of ΔQ100-10(V), (2) additional candidate features obtained during discharge and (3) features from additional data streams such as temperature and internal resistance. Data is collected from the first 100 cycles in every case. These three models are proposed to examine the cost–benefit of collecting more data streams as well as the accuracy limits of prediction. The training data (41 cells) are used to choose model features and coefficient values, while the primary testing data (43 cells) are used to evaluate model performance. The model is then tested using a secondary testing dataset (40 cells) that has been generated after the development of the model. The prediction performance is measured using two metrics: root-mean-square error (RMSE), which is measured in cycles, and average percentage error, which is explained in the ‘Machine-Learning model creation’ selection. In short, the data is first separated into training and test sets. The elastic net is then used to train the model on the training set, resulting in a linear model with downselected features and coefficients. The model is then applied to both the primary and secondary test sets. The elastic net prediction and data processing are done in MATLAB, while the classification is done in Python with the NumPy, pandas, and sklearn tools.
zuhairmhtb / AudioClassificationThis software is a demonstration of Audio Signal Processing and Machine Learning using Python and Tensorflow. The software contains a GUI that can stream audio via webcams or external audio devices connected to the computer and process the audio in real time using a Convolutional and/or a Recurrent Neural Network in order to perform audio classification like speech recognition, music classification, etc. (Depending on how the network was trained). The data set can be arranged in directories where the name of a parent directory represents a classification class. In this way a single network can be trained for multiple types of binary independent audio data eventually building a complex neural network.
SAWassermann / RALRAL - Reinforced stream-based Active Learning
soniahorchidan / Crayfish23Benchmarking Machine Learning Model Inference in Data Streaming Solutions
canoalberto / ROSEROSE: Robust Online Self-Adjusting Ensemble for Continual Learning from Imbalanced Drifting Data Streams
fabriziocarcillo / StreamingActiveLearningStrategiescollection of active learning strategies for streaming scenarios
anshumansinha3301 / Spotify System ArchitectureSpotify’s architecture integrates microservices, machine learning, and distributed systems for a scalable, robust music streaming platform. Key features include personalized recommendations, adaptive audio streaming, global CDN support, and fault tolerance.
atelili / Bitrate Ladder BenchmarkThis repository contains the code for our paper on Benchmarking Learning-based Bitrate Ladder Prediction Methods for Adaptive Video Streaming
