Model Explanation and Prediction Exploration Using Spark ML
Explore techniques for explaining non-linear models, showing feature contributions, and performing what-if analysis using Spark ML. Implement these methods to enhance model interpretability and enable interactive prediction exploration.
Deep Learning - Joan Bruna NYU
Explore deep learning concepts, including lazy regime, relative scale, and practical applications in this comprehensive lecture by Joan Bruna from NYU.
Deep Learning II - Joan Bruna NYU
Explore advanced deep learning concepts including symmetry, transformations, and geometric stability in neural networks with NYU professor Joan Bruna.
Parallel Approximate Undirected Shortest Paths Via Low Hop Emulators
Explore parallel algorithms for approximating undirected shortest paths using low hop emulators. Learn about innovative techniques and their applications in graph theory.
Cyber & Data - Classification Metrics and ML
Explore classification metrics, ROC curves, SVM, and machine learning examples to enhance your understanding of data analysis and cybersecurity concepts.
End to End ML Project With Deployment - Project Problem Statement, EDA and Model Training
Comprehensive guide to building and deploying a machine learning project, covering problem definition, exploratory data analysis, feature engineering, and model training.
Arduino with Python - Graphing and Plotting Live Data from Arduino with Matplotlib
Learn to graph real-time Arduino sensor data using Python and Matplotlib. Master data streaming, processing, and visualization techniques for temperature and pressure readings from a BMP180 sensor.
Wio Terminal TinyML Weather Prediction with TensorFlow Lite and LVGL GUI - Course 4
Create an intelligent meteostation using Wio Terminal and TensorFlow Lite for Microcontrollers to predict weather and precipitation based on local environmental sensor data, with LVGL GUI integration.
Sensing Applications as a Driver for TinyML Solutions
Explore intelligent MEMS applications in motion learning, sports analytics, and environmental sensing. Learn how domain knowledge optimizes TinyML for ultra-low power contexts and shapes emerging ecosystems.
Practical Application of TinyML in Battery Powered Anomaly Sensors for Predictive Maintenance of Industrial Assets
Explore tinyML for industrial anomaly detection, enabling predictive maintenance through low-power sensors, wireless tech, and IoT cloud integration. Includes live demo and practical insights.
How to Train and Deploy TinyML Models for Three Common Sensor Types
Learn to train and deploy tinyML models for sensor, audio, and vision data using Edge Impulse. Explore workflows, best practices, and optimization techniques for embedded machine learning applications.
TinyML Talks - Dr. Dominic Binks- TinyML Doesn’t Need Big Data, It Needs Great Data
Explore challenges and solutions in obtaining quality audio data for tinyML sound recognition, focusing on data sources, collection methods, labeling strategies, and performance evaluation.
DALI 2018 - Andreas Geiger: How to Satisfy the Thirst for Data
Explore innovative approaches to address data scarcity in computer vision, including specialized architectures, soft constraints, self-supervision, and label transfer techniques.
Resource Efficient Machine Learning in a Few KBs of RAM - tinyML Summit 2020
Explore resource-efficient machine learning techniques for IoT devices with limited RAM, focusing on Microsoft's EdgeML library and algorithms like ProtoNN and FastRNN for edge computing.
Avoiding Loss of Quality in Tiny Models - Neuton.ai Partner Session
Explore compact AI models without sacrificing quality. Learn to assess model quality, understand decision-making logic, evaluate training data, and interpret model outputs for tinyML applications.
Build Industrial-Grade TinyML Applications with Edge Impulse - Tutorial
Learn to build industrial-grade tinyML applications using Edge Impulse. Covers dataset collection, model design, training, optimization, and integration into embedded systems running on MCUs.
SQL Server Reporting Services - Data Analysis Master Tutorial
Master SQL Server Reporting Services: Learn to extend functionality, create mobile reports, and design KPIs. Gain hands-on skills in data analysis, report customization, and mobile-friendly visualizations.
Analyzing Wi-Fi Wardriving Data with Google Colab
Learn to analyze Wi-Fi wardriving data using Google Colab. Explore captured data, detect access points, and gain insights into wireless device mapping with this hands-on tutorial led by security researcher Alex Lynd.
Machine Learning at the Extreme Edge of Always-on Intelligent Sensor Networks
Explore ML optimization techniques for resource-constrained IoT sensor nodes, focusing on system, algorithm, architecture, and circuit levels to achieve significant area-power improvements.
Stable Diffusion DreamBooth Guide - Optimal Classification Images Count Comparison Test
Comprehensive guide on optimizing DreamBooth training in Automatic1111, comparing classification image counts for best results in AI image generation and fine-tuning.
Unsupervised Machine Learning for Scaling Data Quality Monitoring in Databricks
Explore unsupervised ML algorithms for scalable data quality monitoring in Databricks, covering implementation, strengths, weaknesses, and practical applications for effective data management.