AI on the Edge - Advancing Edge Computing and Next-Generation Networks
Explore AI's role in advancing edge computing and next-gen networks. Discover opportunities for AI on the edge in DOD Microelectronics Commons Program.
Functional Tokens for On-device Multimodal Models - Nexa AI
Explore innovative functional tokens for on-device multimodal AI models, enhancing accuracy and efficiency in function calling tasks while achieving remarkable performance across languages and modalities.
Building an End-to-End Web Application with Microsoft's Semantic Kernel
Develop a web app using Microsoft's Semantic Kernel to integrate LLMs, enhancing their capabilities with structured responses and external data sources.
Fine-Tuning Language Models with Declarative ML Orchestration
Gain hands-on experience using Flyte to declaratively specify infrastructure for fine-tuning language models on your own data, overcoming compute resource challenges.
An Introduction to Drift Detection
Explore drift detection in machine learning, covering types, detection methods, and practical applications using Alibi Detect. Gain hands-on experience with real-world examples and state-of-the-art techniques.
Leveraging ML Recommendation Models to Improve Customer Engagement
Explore personalization in retail using Amazon Personalize. Learn about ML-powered recommendation systems, real-time personalized experiences, and their impact on customer engagement.
Responsible AI Toolbox: Practical Approaches and Tools for Ethical Machine Learning
Explore practical approaches to responsible AI and learn how open-source and cloud-integrated capabilities empower data scientists to understand and improve ML models ethically.
Introduction to Data Analysis Using Pandas
Learn to effectively analyze data using pandas in Python. Master data manipulation, aggregation, and visualization techniques for extracting valuable insights from tabular datasets.
How You Can Start Designing AI with Purpose
Explore ethical AI design principles and learn to create purposeful, responsible machine learning systems for real-world applications.
Deep AutoViML for TensorFlow Models and MLOps Workflows
Explore Deep AutoViML for building powerful TensorFlow models and MLOps workflows. Learn to fast prototype data pipelines and models using TF 2.4+ and Keras preprocessing layers.
Modeling, Querying and Seeing Time Series Data within a Self-Organizing Mesh Network
Explore graph database modeling for dynamic power grid networks. Learn to query and analyze sensor data, triage at-risk components, and understand network healing processes.
TinyEngine - Efficient Training and Inference on Microcontrollers - Lecture 17
Explore TinyEngine library for efficient neural network training and inference on microcontrollers. Learn techniques to optimize deep learning for resource-constrained devices.
MCUNet: TinyML on Microcontrollers - Lecture 10
Explore MCUNet for TinyML on microcontrollers, covering efficient neural architecture design and deployment strategies for resource-constrained devices.
TinyEngine and Parallel Processing - Lecture 11
Explore TinyEngine and parallel processing techniques for efficient machine learning on resource-constrained devices, enhancing performance and reducing energy consumption.
On-Device Training and Transfer Learning - Lecture 19
Explore on-device training and transfer learning techniques for efficient machine learning, focusing on practical applications and cutting-edge methodologies.
On-Device Training and Transfer Learning - Lecture 19
Explore on-device training and transfer learning techniques for efficient machine learning, focusing on practical applications and cutting-edge methodologies.
EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1
Explore efficient machine learning techniques and their applications in this introductory lecture from MIT's EfficientML course.
EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1
Explore efficient machine learning techniques and their applications in this comprehensive introduction to EfficientML.ai from MIT's Prof. Song Han.
Brain Disorders and Quantum Computing for Neuroradiologists
Explore quantum computing's potential in neuroradiology, focusing on AI applications for brain MRI analysis and the benefits of hybrid quantum systems for processing large datasets in medical imaging.
Developing New Quantum Neural Networks for Neuroradiology
Explore quantum neural networks for neuroradiology, focusing on modifications to Qiskit/Python circuits, updated fit times, fidelity values, and applications in MRI/PET image classification.
Quantum Gate Calculations to Brain Algorithms Design - Live Demo
Explore quantum gates and circuits in IBM Qiskit, focusing on applications in neuroradiology and brain MR image classification using hybrid classical-quantum networks.