Self - Cross, Hard - Soft Attention and the Transformer
Explore attention mechanisms and Transformer architecture, covering self/cross and hard/soft attention, key-value stores, and practical implementation in PyTorch for advanced deep learning applications.
Attention and the Transformer
Explore self-attention, key-value stores, and transformer architecture in deep learning. Learn to implement attention mechanisms and understand encoder-decoder paradigms for advanced neural network design.
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.
The Future of Generative AI - Possibilities and Challenges
Explore the future of generative AI with Professor Michael Wooldridge, covering its capabilities, challenges, and potential impact on society. Gain insights into machine learning, neural networks, and the evolution of AI technology.
PixArt-α: Open Source Text-to-Image Model - Installation and Usage Tutorial
Explore PixArt-α, an open-source text-to-image model rivaling Midjourney. Learn installation, usage, and comparison with other models. Discover its superior capabilities in various art styles and prompt following.
First Principles of OCI Generative AI Service - Architecture and Implementation
Explore OCI Generative AI Service's mechanics, including flexible LLM fine-tuning and improved GPU cluster management for simplified, cost-effective AI application integration.
Inside GPT - Large Language Models Demystified
Demystifying GPT algorithms: architecture, inner workings, and practical applications. Explore NLP fundamentals, train models, and learn to leverage ChatGPT and GPT4 for innovative solutions.
What Is ChatGPT Doing and Why Does It Work?
Explore the inner workings and underlying principles of ChatGPT, delving into its functionality and effectiveness in natural language processing and generation.
Accelerating Simulations of Particle Systems Using Machine Learning
Explore machine learning techniques for accelerating particle system simulations using transformer-based architectures and Mathematica.
Unfolding the Paper Windmills
Demystify academic papers, understand Transformer architecture, and explore JAX ecosystem through hands-on implementation of "Attention is all you need" paper.
StreamingLLM: Deploying Language Models for Streaming Applications with Long Text Sequences
Explore innovative techniques for deploying LLMs in streaming applications, focusing on the "attention sink" phenomenon and efficient KV cache management.
Introduction to Generative AI
Explore the fundamentals of Generative AI, its applications, model types, and usage principles in this concise overview.
Generative AI Overview - Foundations, Models, and Applications
Explore foundational concepts of Generative AI, its evolution, and key models like DALL·E and GPT. Learn how these technologies reshape creativity and innovation across various industries.
Generative AI, ChatGPT, and LLMs - Buzzwords Explained
Clarifies key AI terms: genAI, ChatGPT, and LLMs. Demystifies buzzwords, explaining differences and relationships between these concepts in artificial intelligence.
Generative AI - Explained
Explore generative AI's unique features and how it differs from other AI buzzwords in this concise, informative video.
BERTology Meets Biology - Interpreting Attention in Protein Language Models
Explores how BERT models trained on protein sequences learn biological properties, revealing insights into protein structure, binding sites, and biophysical characteristics through attention mechanism analysis.
LoRA Explained - Efficient Fine-Tuning of Large Language Models
Explore LoRA, a technique for efficient fine-tuning of large language models. Learn about its low-rank matrix decompositions and reduced trainable parameters for task-specific adaptations.
The Narrated Transformer Language Model
Visual introduction to the Transformer architecture, explaining its components and applications in AI/ML. Accessible to various experience levels, covering tokenization, embedding, and output projection.
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.
Memory Optimization Techniques for On-Device Large Language Models
Discover advanced memory optimization techniques for deploying large language models on devices with limited resources, focusing on key-value caching and dynamic compression methods.
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.