Training Vision Transformers for Real-Time Image Classification
Master Vision Transformer implementation for real-time emotion classification in video, including hands-on training techniques and comparison with CLIP for zero-shot classification tasks.
Parameter-Efficient Finetuning - QLoRA and LoRA Methods
Explore parameter-efficient fine-tuning techniques with LoRA and QLoRA, learning how to optimize large language models while maintaining performance and reducing computational costs.
Neural Fine-Tuning Search for Few-Shot Learning
Explore advanced neural architecture search techniques for optimizing few-shot learning adaptation strategies, focusing on meta-training and meta-testing trade-offs in multi-domain contexts.
How to Train GPT-3 with Your Own Dataset
Discover practical techniques for customizing GPT-3 through dataset training, exploring real-world applications and methods to enhance model performance for specific use cases.
Multi-Task Learning in Transformer-Based Architectures for Natural Language Processing
Explore multi-task learning in transformers, covering adapter techniques, hypernetworks, and efficient solutions for training multiple NLP tasks with shared architectures and reduced computational costs.
Introduction to Computer Vision with CNNs
Explore computer vision using CNNs and transfer learning. Learn practical steps for CNN training, including activation functions, and discover applications in image quality assessment and feature extraction.
Modeling Individuals Without Data via Secondary Task Transfer Learning
Discover a novel transfer learning method for modeling individual behavior using limited data from secondary tasks, outperforming standard approaches in financial health and game design.
Overcoming the Cold Start Problem: How to Make New Tasks Tractable
Explore strategies to overcome the cold start problem in AI, focusing on data aggregation and leveraging pre-trained models for efficient scaling of machine learning applications.
Towards Fast Autonomous Learners: Advances in Reinforcement Learning - 2015
Explore reinforcement learning for AI agents interacting with humans, focusing on sample efficiency and transfer learning to improve educational tools and decision-making systems.
Low-resource Morphological Generation with Neural Sequence-to-Sequence Models
Explore neural sequence-to-sequence approaches for morphological generation in low-resource languages, including multi-task learning, cross-lingual transfer, and semi-supervised techniques.
Foundation Models and the Transfer of Embodied Autonomy
Explore challenges in developing foundation models for sequential decision-making, including transfer learning, and discuss future research directions in embodied autonomy.
Fine-Tuning Vision Transformer Classifier for EyePacs Dataset Quality Model - Part 1
Fine-tune a Vision Transformer for image quality assessment using the EyeQ dataset, a subset of EyePacs, to enhance diabetic retinopathy detection capabilities.
Fine-Tuning Vision Transformer for Diabetic Retinopathy Detection - Part 2
Fine-tune a Vision Transformer for diabetic retinopathy detection using EyeQ dataset. Learn to adapt pretrained models for specialized medical image classification tasks.
Fine-Tuning ViT Classifier for EyePacs Dataset Quality Model - Tutorial en Español
Personaliza un Vision Transformer con tu propio dataset para analizar la calidad de imágenes oculares utilizando el conjunto de datos EyeQ, un subconjunto de EyePacs.
How to Finetune and Develop Your Own Large Language Model
Explore techniques for fine-tuning and developing custom large language models, enhancing your AI capabilities and understanding of advanced NLP concepts.
Tuning Foundation Models: A Comprehensive Guide
Explore techniques for customizing foundation models with your data, improving performance across tasks while understanding limitations and future prospects.
OWASP Top 10 for Machine Learning Security - A Comprehensive Walkthrough
Explore OWASP's top 10 ML security vulnerabilities, gain insights into real-world examples, and learn actionable strategies for securing machine learning models and systems.
Foundational Models in Enterprise AI - Challenges and Opportunities
Explore the future of AI with foundation models, their enterprise applications, and challenges. Learn about Snorkel AI's approach to optimizing these models for cost, quality, and control.
Ads Ranking Evolution at Pinterest - From Logistic Regression to Deep Learning
Explore Pinterest's ads ranking evolution, from logistic regression to deep learning transformers, incorporating sequential signals and multi-task learning. Gain insights on overcoming challenges and advancing ML platforms.
RecSys at Spotify - Building and Evaluating Large-Scale Recommender Systems
Explore Spotify's large-scale recommender systems, foundational embeddings for transfer learning, evaluation methods, and MLOps challenges with Senior ML Engineer Sanket Gupta.
Cooperative Data-driven Modeling: Addressing Catastrophic Forgetting in Neural Networks
Explore cooperative data-driven modeling techniques to address catastrophic forgetting in deep neural networks for class-incremental learning scenarios.