Generative AI and Large Language Models: From GPT-3 to ChatGPT with RLHF
Explore the latest advancements in generative AI, from CodeWhisperer to ChatGPT, while learning about prompt engineering and reinforcement learning techniques for improved human-AI interaction.
How to Train GPT-3 with Your Own Dataset
Discover practical techniques for customizing GPT-3 through dataset training, exploring real-world applications and methods to enhance model performance for specific use cases.
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.
Prompt Engineering: 7 Tricks to Solve Problems with AI - Part 4
Master prompt engineering techniques to enhance AI problem-solving. Learn 7 key tricks and explore practical applications using LangChain for building AI-powered solutions.
Generative AI for Product Builders
Explore generative AI's potential to enhance products, accelerate workflows, and unlock creativity. Learn about technical challenges and solutions in building AI-powered products.
Alpa: Simple Large Model Training and Inference on Ray
Explore Alpa, a Ray-native library for automated model-parallel training of large deep learning models, optimizing execution plans and scaling complex architectures efficiently.
The Unreasonable Effectiveness of Mathematics in Large Scale Deep Learning - From Theory to Practice
Explore the mathematical foundations of large-scale deep learning, focusing on infinite-width neural networks and their practical applications in tuning enormous models like GPT-3.
Automating Knowledge Work with Generative AI
Explore generative AI's impact on knowledge work, learn to apply models for business solutions, and gain hands-on experience in automation techniques.
A Brief History of AI and AI Agents - From Language Models to Multi-Agent Systems
Explore the evolution of AI from GPT-3 to modern multi-agent systems, covering key developments in language models, function calling, AI agents, and future implications for global artificial intelligence.