A User Guide to Transformers
Explore practical applications of transformers with minimal examples for various scenarios, enhancing your understanding of this innovative data analysis tool.
Building Intelligent Systems with MoE, Multimodality, and Crowd of Agents
Explore advanced AI systems using MoE, multimodality, and agent crowds. Learn techniques for building composite intelligent systems beyond single algorithms or models.
Practical Applications of Generative AI: Building a Chatbot for Document Q&A
Explore Retrieval Augmented Generation to build chatbots that answer questions about unseen documents, leveraging the power of Generative AI and Large Language Models.
Accelerating Materials Design with Crystal Graph Neural Networks
Explore Crystal Graph Convolutional Neural Networks for predicting material properties and their impact on graph neural networks and transformers in materials design.
Transforming the Retail Industry with Transformers
Explore how transformers and language models revolutionize retail data processing, overcoming challenges in deployment and automation for improved business outcomes.
Deploying Transformers at Scale - Addressing Challenges and Increasing Performance
Explore challenges and solutions for deploying large-scale Transformer networks, focusing on improving latency, throughput, and cost-effectiveness for processing massive datasets.
Beyond Standard Deep Learning Models for Time Series and Sequences
Explore advanced techniques for temporal data analysis, including confident predictions and adaptive computational time models for improved sequence-to-sequence processing.
State-of-the-Art Deep Learning-Based Object Detection in 2D
Explore state-of-the-art object detection systems, including two-stage, one-stage, and transformer-based approaches. Learn to upgrade detection pipelines and tackle key challenges in 2D and 3D object detection.
Fast Language Generation by Finetuning Pretrained Transformers
Explore efficient language generation by converting pretrained transformers to RNNs, improving speed without sacrificing accuracy through a swap-then-finetune approach.
Streaming Sequence Transduction through Dynamic Compression
Explore efficient sequence-to-sequence transduction with STAR, a novel Transformer-based model for streaming ASR and simultaneous speech-to-text tasks.
GenAI for Observability in the Serverless World
Harness GenAI to enhance serverless observability, leveraging vector databases and ML for improved data ingestion, log analysis, and problem resolution.
Faraday's Law and Electromagnetic Induction - Chapter 22 Summary
Explore Faraday's Law, Lenz's Law, and Maxwell's Equations. Understand inductors, transformers, and magnetic field energy density. Gain insights into electromagnetic principles and applications.
Physics Summary: Electromagnetic Induction - Chapter 23
Explore electromagnetic induction, Faraday's Law, and related concepts. Learn about electric generators, power production, and applications like transformers and RL circuits.
Reinforcement Learning with Human Feedback
Explore how human feedback enhances AI models, improving text generation and responsiveness. Learn RLHF techniques for fine-tuning large language models with Luis Serrano, PhD.
Attention Is All You Need - A Landmark Paper in AI
Explore the groundbreaking "Attention Is All You Need" paper, its impact on modern AI, and the potential of transformer architecture in various applications beyond machine translation.
Want High Performing LLMs? It's All About Your Data - Importance of Quality Data in AI Development
Explore data-centric AI development for high-performing LLMs. Learn pre-training techniques, avoid common pitfalls, and understand the critical role of quality data in model performance.
Accelerating Transformers via Kernel Density Estimation - Google TechTalk
Explore efficient Transformer acceleration using kernel density estimation. Learn innovative techniques for handling long sequences and improving performance in deep learning models.
Representational Strengths and Limitations of Transformers
Explore the mathematical analysis of attention layers in transformers, comparing their representation power to other architectures and examining complexity parameters in various tasks.
History of Language Models: Evolution, Frameworks, and Deployment
Explore language models' evolution from early systems to neural networks, transformers, and large-scale pretraining. Gain insights on key frameworks and real-world deployment strategies.
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.
Fine-tuning Optimizations - DoRA, NEFT, LoRA+, and Unsloth
Explore advanced fine-tuning techniques like DoRA, NEFT, LoRA+, and Unsloth to optimize language models. Learn implementation, comparisons, and practical applications through code examples and demonstrations.