Easterhegg 2019 - Generative Adversarial Networks für Anfänger
Einführung in Generative Adversarial Networks: Überblick, Geschichte und praktische Anwendungen für Entwickler. Erklärt Konzepte, Techniken und Herausforderungen anhand von Beispielen wie Pokémon-Generator und Gesichtsalterung.
Refurbish Your Training Data - Reusing Partially Augmented Samples for Faster Deep Neural Network Training
Innovative data refurbishing technique accelerates deep neural network training while maintaining model generalization by reusing partially augmented samples and optimizing CPU-GPU overlap.
Your Voice is My Passport
Explore machine learning's impact on voice authentication security, including synthetic audio generation and potential vulnerabilities in financial and home automation systems.
Audio Classification with Machine Learning
Learn practical techniques for audio classification using machine learning, including spectrograms, CNNs, and transfer learning. Explore applications in speech, music, medical diagnostics, and more using Keras and TensorFlow.
A Composer's Guide to Creating with Generative Neural Networks
Exploring the challenges and creative potential of using generative neural networks in music composition, from data collection to model selection and ethical considerations.
Best Practices for Real-time Intelligent Video Analytics
Explore best practices for real-time intelligent video analytics, including transfer learning, data augmentation, and NVIDIA tools for efficient AI applications and high-performance inference.
VulChecker - Graph-based Vulnerability Localization in Source Code
Explore VulChecker, a tool for precise vulnerability detection in source code using graph-based neural networks. Learn about its innovative approach to locating and classifying security flaws.
Data Science in Action
Explore real-world applications of data science, from label data to unsupervised machine learning, with practical examples in wind energy, object detection, and automotive industries.
How Multilingual Is Your NLP Model?
Explore multilingual NLP challenges, language identification techniques, and BERT's capabilities for handling diverse linguistic data in the evolving landscape of global communication.
Audio Classification with Machine Learning
Learn to build practical machine learning models for audio classification using spectrograms, CNNs, and techniques like transfer learning and data augmentation with Keras and TensorFlow.
Getting Started with Apple ML
Introduction to Apple ML: Learn to integrate machine learning into iOS apps using Create ML and Swift, with no math background required. Explore datasets, compare tools, and discover ML possibilities.
Semantic Segmentation Made Easy - Using Segmentation Models Library
Learn to implement semantic segmentation using the Segmentation Models library, covering model types, backbones, data augmentation, preprocessing, and training on electron microscopy datasets.
Data Augmentation Using Keras
Explore image augmentation techniques using Keras, including reshaping and multiclass augmentation, to enhance your deep learning models.
The Game Changing Evolution of Synthetic Data Generation - Magpie
Explore Magpie's revolutionary synthetic data generation for AI, automating high-quality training data creation without manual input, enhancing model performance and efficiency.
Synthetic Data Generation and Applications in Python
Discover how to generate synthetic data for machine learning and data analysis projects, with practical Python examples and implementation strategies.
Leveraging GANs for Building Synthetic Data
Explore GANs for synthetic data generation to overcome data scarcity, preserve privacy, and enhance machine learning model training across various domains.
Utterance Generator Fast Tracks Chatbot Training
Explore an utterance generator tool for efficient chatbot training, enhancing natural language understanding and streamlining development processes.
Three AI Use Cases for Analyzing Sales Data
Explore three practical AI applications for analyzing sales data, demonstrated with real-world examples from a warm outreach campaign. Learn data augmentation, structuring unstructured data, and lead scoring techniques.
How Ford Uses AI to Develop Better AI
Explore Ford's AI development lifecycle, focusing on data extraction, annotation, and augmentation for training. Learn about iteration's role in enhancing AI systems at Ford Motor Company.
Best Practices and Tips for Fine-Tuning Representation Models
Discover techniques to enhance generative applications through representation model fine-tuning. Learn data quality strategies, augmentation methods, and optimization tips for RAG-based pipelines.
Retrieve, Augment, Generate then Measure - How to Build Open Source AI
Explore open-source AI development through the Retrieve, Augment, Generate, and Measure framework. Learn practical techniques for building AI systems using freely available tools and resources.