PyTorch Vision Transformer - Guide to Fine-Tuning for Object Identification
Master fine-tuning Vision Transformers (ViT) in PyTorch through hands-on coding, focusing on object identification implementation with practical examples and real-time coding demonstrations in Google Colab.
PyTorch Vision Transformer: Applying Pre-trained and Fine-tuned ViT Models for Image Classification
Master Vision Transformer implementation in PyTorch using pre-trained and fine-tuned ViT models for image classification, with hands-on practice in Google Colab and Huggingface integration.
Vision Transformer-Based Transfer Learning for Medical Image Classification 2023
Explore cutting-edge Vision Transformer technology for medical image classification, focusing on mammogram analysis and early breast cancer detection through advanced deep learning approaches.
How to Fine-tune T5 and Flan-T5 Large Language Models - Theory Introduction
Discover the fundamentals of fine-tuning T5 and Flan-T5 language models, exploring key differences, implementation steps, and practical applications using Hugging Face transformers.
Deep Learning: BERT and GPT Models - Lecture 11
Dive into the evolution and architecture of transformer models, exploring BERT, GPT series, and T5 to understand their impact on modern natural language processing.
Transformer Types and Practical Considerations
Explore different transformer architectures and key implementation factors for building effective natural language processing models and applications.
Pretraining and Finetuning in Machine Learning
Dive into the fundamental concepts of pretraining and finetuning in machine learning, exploring key techniques and applications for model optimization.
Transformer Decoder - Pretraining and Finetuning
Master transformer decoder architecture fundamentals, exploring pretraining methods and effective finetuning techniques for advanced natural language processing applications.
The Unreasonable Effectiveness of Large Language-Vision Models for Video Domain Adaptation - Lecture 31
Explore the groundbreaking potential of large language-vision models in video domain adaptation through Prof. Ricci's expert analysis of their effectiveness and applications.
Transfer Learning and Transformers - Full Stack Deep Learning - Spring 2021
Explore transfer learning's evolution from computer vision to NLP, diving into embeddings, language models, and the revolutionary Transformer architecture. Gain insights into various models and their applications.
Xavier Amatriain on Practical Deep Learning Systems - November 2019
Practical insights on deep learning systems, covering data handling, model selection, architecture engineering, and real-world implementation challenges in various domains.
Computer Vision Dog Breed Classification with Convolutional Neural Networks, TensorFlow and Kubeflow
Develop a dog breed classification system using TensorFlow, Kubeflow, and convolutional neural networks. Compare AI predictions with DNA results for accuracy.
Modular and Composable Transfer Learning
Explore parameter-efficient transfer learning with adapters, enabling modular and composable fine-tuning for improved task performance and cross-lingual generalization in transformer-based models.
Deep Learning for ESG: Advances and Limitations
Explore recent NLP advances in ESG analysis, including transformer models and transfer learning. Understand the advantages and limitations of current technology in ESG domain.
Measures of Relatedness in Domain Adaptation and Related Problems
Explore unstructured distribution shifts, discrepancy measures, and adaptation techniques in domain adaptation and transfer learning settings.
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
Discover a methodology for efficiently selecting and fine-tuning pretrained models through meta-learning, optimizing model selection and hyperparameter configurations for new datasets.
Transfer Learning and Transformer Models
Explore natural language processing's evolution, focusing on Transformer architecture, language modeling, and BERT's impact on transfer learning in this comprehensive tech talk.
Towards Extreme Multi-Task Scaling for Transfer Learning
Explores ExT5, an advanced NLP model pre-trained on 107 diverse tasks. Analyzes multi-task scaling benefits, task co-training effects, and demonstrates ExT5's superior performance across various NLP benchmarks.
Pretrained Transformers as Universal Computation Engines
Explore how pretrained language transformers can be fine-tuned for diverse tasks like vision and protein folding, with minimal parameter adjustments, demonstrating their potential as universal computation engines.
Transfer Learning for Image Classification with PyTorch & Python Tutorial - Traffic Sign Recognition
Learn to classify traffic signs using transfer learning with PyTorch. Explore data, build datasets, train a pre-trained ResNet model, evaluate performance, and make predictions on new images.
Getting Started with AI Powered Q&A Using Hugging Face Transformers - HuggingFace Tutorial
Learn to implement AI-powered Q&A using Hugging Face Transformers. Explore BERT, transfer learning, and practical coding with pre-trained models for your own data and solutions.