Shaping the Future of AI from the History of Transformer Architectures - Stanford CS25
Explore the evolution of Transformer architectures and AI development with OpenAI researcher Hyung Won Chung, focusing on historical motivations and future projections in the field.
Neural Nets for NLP 2019 - Attention
Explore attention mechanisms in neural networks for NLP, covering basic concepts, improvements, specialized varieties, and a case study on the Transformer model.
Neural Nets for NLP 2017 - Conditioned Generation
Explore encoder-decoder models, conditional generation, ensembling, and evaluation techniques for neural networks in natural language processing. Gain insights into advanced NLP concepts and practical applications.
CMU Advanced NLP 2021 - Conditional Generation
Learn advanced NLP techniques for conditional generation, including encoder-decoder models, search algorithms, ensembling, and evaluation methods. Explore applications in translation, summarization, and dialogue systems.
Neural Nets for NLP 2020: Conditioned Generation
Learn about encoder-decoder models, conditional generation, search techniques, and evaluation methods for neural networks in natural language processing.
Transformers - From Zero to Hero
Comprehensive deep dive into transformer architecture, covering key concepts from self-attention to tokenization. Includes hands-on coding for both encoder and decoder implementations.
Neural Nets for NLP 2021 - Conditioned Generation
Explore encoder-decoder models, conditional generation, search techniques, ensembling, evaluation methods, and various data types for conditioning in neural network-based natural language processing.
Introduction to T5 for Sentiment Span Extraction
Explore T5's text-to-text approach for NLP tasks, focusing on sentiment span extraction in tweets. Learn about multi-task learning and its potential impact on industrial NLP applications.
MIT 6.S191 - Automatic Speech Recognition
Explore automatic speech recognition with Rev.com experts, covering data selection, speech input processing, and advanced ASR techniques like attention-based models and language modeling.
Lost and Found in Translation - An Intro to ML Assisted Decompilation and Deobfuscation
Explore ML-assisted decompilation and deobfuscation, covering large language models, neural machine translation, and their applications in reverse engineering and security auditing.