Neural Nets for NLP 2020 - Document Level Models
Explore document-level NLP techniques including language modeling, coreference resolution, and discourse parsing. Learn advanced approaches for handling long-range dependencies and entity relationships in text.
Neural Nets for NLP 2021 - Document-Level Models
Explores advanced NLP techniques for document-level tasks, including long-document modeling, entity coreference, and discourse parsing, with a focus on neural network approaches and their applications.
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.
Transformer Neural Networks, ChatGPT's Foundation, Clearly Explained
Clear explanation of Transformer Neural Networks, the foundation of ChatGPT and modern AI. Covers key concepts like word embedding, self-attention, and encoder-decoder architecture.
Deep Dive into the Transformer Encoder Architecture
Detailed exploration of transformer encoder architecture, covering embeddings, positional encodings, self-attention mechanisms, and key components for effective natural language processing.
On-Device Speech Models Optimization and Deployment for Mobile Hardware
Explore on-device speech model optimization and deployment, covering streaming-aware design, quantization techniques, and benchmarks for popular speech processing model topologies on mobile platforms.
Accelerating Simulations of Particle Systems Using Machine Learning
Explore machine learning techniques for accelerating particle system simulations using transformer-based architectures and Mathematica.
Deep Learning Applied to the Analysis of Dissolved Carbon Dioxide in Coastal Areas of the Balearic Islands
Explore deep learning techniques for analyzing dissolved CO2 in Balearic coastal areas, covering self-attention, permutation convolution, and symbolic regression.
Informer Encoder Architecture Explained
Explore the Informer Encoder architecture, its key components, and implementation details for advanced time series forecasting and sequence modeling tasks.
New Approaches to Image and Video Reconstruction Using Deep Learning
Explore cutting-edge deep learning techniques for image and video enhancement, including colorization and resolution improvement, using efficient PyTorch-based tools trainable on a single GPU.
Large Language Models for Computational Biology - A Primer
Explore large language models in computational biology, covering transformers, pretraining, and applications in genomics and single-cell analysis.
LLMs and Transformers Demystified: Introduction to AI Engineering - Lecture 1
Demystify transformers and LLMs through intuitive weather analogies. Explore the architecture's impact on AI, from 'Attention is All You Need' to modern applications like ChatGPT.
Informer Architecture Explained - Complete Guide
Comprehensive exploration of Informer encoder architecture, detailing its components and implementation for advanced time series forecasting and sequence modeling tasks.
Informer: Complete Code from Scratch - Time Series Transformer Analysis
Line-by-line exploration of the time series transformer, focusing on implementation details and code structure for deep learning enthusiasts and practitioners.
Vision Transformers (ViTs) - A Beginner's Guide to Image Processing with Transformers
Dive into Vision Transformers (ViTs), exploring core concepts like linear projection, multihead attention layers, and patch embedding in a beginner-friendly, comprehensive walkthrough.
Transformer and Large Language Models - Part I - Lecture 12
Explore Transformer architecture and Large Language Models in this comprehensive MIT lecture, covering key concepts and recent advancements in AI.
Transformer and Large Language Models - Part I - Lecture 12
Explore Transformer architecture and Large Language Models in this comprehensive MIT lecture, covering key concepts and recent advancements in AI.
Transformer and Large Language Models - Part II - Lecture 13
Explore advanced concepts of Transformers and Large Language Models in this in-depth lecture from MIT's EfficientML course.
Transformer and Large Language Models - Part II - Lecture 13
Explore advanced concepts of Transformers and Large Language Models, focusing on efficiency and optimization techniques in AI and machine learning.
Vision Transformer - Lecture 14
Explore Vision Transformers in deep learning, covering architecture, training, and applications. Gain insights into this powerful model for computer vision tasks.
Vision Transformer - Lecture 14
Explore Vision Transformers in deep learning, covering architecture, training, and applications. Gain insights into this powerful model for computer vision tasks.