Jeremy Howard - answer.ai, what is wrong with the academia and industry
Become a Patreon: https://www.patreon.com/theaiepiphany
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Talked with Jeremy about what he's trying to accomplish with answer.ai, what is wrong with the current incentives in the industry & academia, how we're wasting the human potential, etc. :) enjoy!
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https://www.answer.ai/
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โ๏ธ Timetable:
00:00 - 00:49 Intro
00:49 - 02:29 Hyperstack GPUs sponsor
02:29 - 29:48 What is answer.ai? How to get hired?
29:48 - 37:42 Incentives in the industry and academia
37:42 - 41:47 Thoughts on Mojo an d Chris Lattner
41:47 - 45:10 fast.ai future?
45:10 - 54:49 unlocking human potential
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๐ฐ SPONSOR
The AI Epiphany - https://www.patreon.com/theaiepiphany
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Huge thank you to these AI Epiphany patreons:
Eli Mahler
Petar Veliฤkoviฤ
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๐ผ LinkedIn - https://www.linkedin.com/in/aleksagordic/
๐ฆ Twitter - https://twitter.com/gordic_aleksa
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#jeremyhoward #answerai #openai
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Intro | Neural Style Transfer #1
Aleksa Gordiฤ - The AI Epiphany
Basic Theory | Neural Style Transfer #2
Aleksa Gordiฤ - The AI Epiphany
Optimization method | Neural Style Transfer #3
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Advanced Theory | Neural Style Transfer #4
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Anyone can make deepfakes now!
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What is Computer Vision? | The Art of Creating Seeing Machines
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Feed-forward method | Neural Style Transfer #5
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Alan Turing | Computing Machinery and Intelligence
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Feed-forward method (training) | Neural Style Transfer #6
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What is Google Deep Dream? (Basic Theory) | Deep Dream Series #1
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Semantic Segmentation in PyTorch | Neural Style Transfer #7
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How to get started with Machine Learning
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How to learn PyTorch? (3 easy steps) | 2021
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PyTorch or TensorFlow?
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3 Machine Learning Projects For Beginners (Highly visual) | 2021
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Machine Learning Projects (Intermediate level) | 2021
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Cheapest (0$) Deep Learning Hardware Options | 2021
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How to learn deep learning? (Transformers Example)
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How do transformers work? (Attention is all you need)
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Developing a deep learning project (case study on transformer)
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Vision Transformer (ViT) - An image is worth 16x16 words | Paper Explained
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GPT-3 - Language Models are Few-Shot Learners | Paper Explained
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Google DeepMind's AlphaFold 2 explained! (Protein folding, AlphaFold 1, a glimpse into AlphaFold 2)
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Attention Is All You Need (Transformer) | Paper Explained
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Graph Attention Networks (GAT) | GNN Paper Explained
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Graph Convolutional Networks (GCN) | GNN Paper Explained
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Graph SAGE - Inductive Representation Learning on Large Graphs | GNN Paper Explained
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PinSage - Graph Convolutional Neural Networks for Web-Scale Recommender Systems | Paper Explained
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OpenAI CLIP - Connecting Text and Images | Paper Explained
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Temporal Graph Networks (TGN) | GNN Paper Explained
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Graph Neural Network Project Update! (I'm coding GAT from scratch)
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Graph Attention Network Project Walkthrough
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How to get started with Graph ML? (Blog walkthrough)
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DQN - Playing Atari with Deep Reinforcement Learning | RL Paper Explained
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AlphaGo - Mastering the game of Go with deep neural networks and tree search | RL Paper Explained
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DeepMind's AlphaGo Zero and AlphaZero | RL paper explained
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OpenAI - Solving Rubik's Cube with a Robot Hand | RL paper explained
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MuZero - Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model | RL Paper explained
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EfficientNetV2 - Smaller Models and Faster Training | Paper explained
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Implementing DeepMind's DQN from scratch! | Project Update
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MLP-Mixer: An all-MLP Architecture for Vision | Paper explained
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DeepMind's Android RL Environment - AndroidEnv
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When Vision Transformers Outperform ResNets without Pretraining | Paper Explained
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Non-Parametric Transformers | Paper explained
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Chip Placement with Deep Reinforcement Learning | Paper Explained
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Text Style Brush - Transfer of text aesthetics from a single example | Paper Explained
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Graphormer - Do Transformers Really Perform Bad for Graph Representation? | Paper Explained
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GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation | Paper Explained
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VQ-VAEs: Neural Discrete Representation Learning | Paper + PyTorch Code Explained
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VQ-GAN: Taming Transformers for High-Resolution Image Synthesis | Paper Explained
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Multimodal Few-Shot Learning with Frozen Language Models | Paper Explained
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Focal Transformer: Focal Self-attention for Local-Global Interactions in Vision Transformers
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AudioCLIP: Extending CLIP to Image, Text and Audio | Paper Explained
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RMA: Rapid Motor Adaptation for Legged Robots | Paper Explained
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DALL-E: Zero-Shot Text-to-Image Generation | Paper Explained
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DETR: End-to-End Object Detection with Transformers | Paper Explained
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DINO: Emerging Properties in Self-Supervised Vision Transformers | Paper Explained!
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DeepMind DetCon: Efficient Visual Pretraining with Contrastive Detection | Paper Explained
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Do Vision Transformers See Like Convolutional Neural Networks? | Paper Explained
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Fastformer: Additive Attention Can Be All You Need | Paper Explained
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Chapters (7)
00:49 Intro
0:49
02:29 Hyperstack GPUs sponsor
2:29
29:48 What is answer.ai? How to get hired?
29:48
37:42 Incentives in the industry and academia
37:42
41:47 Thoughts on Mojo an d Chris Lattner
41:47
45:10 fast.ai future?
45:10
54:49 unlocking human potential
๐
Tutor Explanation
DeepCamp AI