AlexNet

No Regrets - What Happens to AI Beyond Generative? - Computerphile

Computerphile

A deep convolutional neural network that was one of the early successes that demonstrated the power of deep learning on GPUs.

Production AI Engineering starts with Evals

Latent Space

A groundbreaking convolutional neural network, whose release made the speaker realize new possibilities for data processing, inspiring Impira.

Jim Keller: The Future of Computing, AI, Life, and Consciousness | Lex Fridman Podcast #162

Lex Fridman

A pioneering convolutional neural network that won the ImageNet LSVRC-2012 competition, mentioned as a point in the steady improvement of compute resolution for neural networks.

The End of Finetuning — with Jeremy Howard of Fast.ai

Latent Space

A landmark convolutional neural network in computer vision, mentioned as part of the early development phase similar to current LLM understanding.

Dmitry Korkin: Evolution of Proteins, Viruses, Life, and AI | Lex Fridman Podcast #153

Lex Fridman

A convolutional neural network that made a significant leap in image recognition performance, marking a deep learning milestone.

Chris Lattner: The Future of Computing and Programming Languages | Lex Fridman Podcast #131

Lex Fridman

A groundbreaking convolutional neural network that won the ImageNet Large Scale Visual Recognition Challenge in 2012, marking a significant breakthrough and catalyst for the deep learning revolution.

Efficient Computing for Deep Learning, Robotics, and AI (Vivienne Sze) | MIT Deep Learning Series

Lex Fridman

A popular neural network mentioned as an example of increasing channel count and computational demand.

MIT 6.S093: Introduction to Human-Centered Artificial Intelligence (AI)

Lex Fridman

Likely referring to a seminal deep learning model for image recognition, implied in the discussion of breakthroughs in computer vision.

Deep Learning State of the Art (2019)

Lex Fridman

A convolutional neural network that marked a significant jump in performance for computer vision tasks, inspiring the deep learning field.

Deep Learning Basics: Introduction and Overview

Lex Fridman

An early and influential convolutional neural network that achieved high performance on the ImageNet challenge, showcasing deep learning capabilities.

Anthropic Co-founder: Building Claude Code, Lessons From GPT-3 & LLM System Design

Y Combinator

Information Theory for Language Models: Jack Morris

Latent Space

A deep neural network that marked a paradigm shift in AI around 2010-2012, trained on ImageNet.

MIT 6.S094: Computer Vision

Lex Fridman

One of the first highly successful GPU-trained neural networks on ImageNet, marking a significant boost in performance.

MIT 6.S094: Deep Learning

Lex Fridman

A pioneering deep learning network that achieved a significant performance leap on the ImageNet challenge in 2012, trained on GPUs.

MIT 6.S094: Recurrent Neural Networks for Steering Through Time

Lex Fridman

A famous convolutional neural network architecture, mentioned as a pre-trained network useful for transfer learning.

MIT 6.S094: Introduction to Deep Learning and Self-Driving Cars

Lex Fridman

A pioneering convolutional neural network that achieved record-breaking performance in the ImageNet classification competition in 2012.

Deep Learning for Computer Vision (Andrej Karpathy, OpenAI)

Lex Fridman

The winning architecture of the 2012 ImageNet challenge, which significantly advanced computer vision by successfully applying a deep convolutional neural network trained on GPUs.

TensorFlow Tutorial (Sherry Moore, Google Brain)

Lex Fridman

Referenced as an influential development that worked closely with the TensorFlow team during its creation.

Stephen Wolfram: Fundamental Theory of Physics, Life, and the Universe | Lex Fridman Podcast #124

Lex Fridman

A convolutional neural network that won the ImageNet Large Scale Visual Recognition Challenge in 2012, significantly boosting the deep learning revolution.

Pieter Abbeel: Deep Reinforcement Learning | Lex Fridman Podcast #10

Lex Fridman

An early deep learning model from 2012 that represented a breakthrough in image recognition and demonstrated the power of transfer learning through fine-tuning for new tasks.