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Explore Yannic Kilcher's efficient approach to Neural Architecture Search using a Synthetic Petri Dish model. Learn to predict model performance in less than an hour.
Explore object detection through CornerNet's unique approach of detecting objects as paired keypoints, with Yannic Kilcher. Less than 1 hour.
Explore the Synthesizer model and its impact on Transformers in this concise study by Yannic Kilcher. Learn about attention mechanisms, synthetic attention, and their applications in language modeling and translation.
Explore GPT-3's capabilities in NLP tasks with Yannic Kilcher's 1-2 hour material, covering language models, transformer models, fine-tuning, and experimental results.
Explore object detection in images using a single Transformer stacked on a CNN in this concise study by Yannic Kilcher. Learn about architecture, problem formulation, and results in under an hour.
Explore BERT model pruning and the Lottery Ticket Hypothesis with Yannic Kilcher. Learn how even "bad" subnetworks can be fine-tuned for good accuracy in under an hour.
Explore meta-learning with implicit gradients in under an hour with Yannic Kilcher. Understand the computational benefits of iMAML and its application in few-shot image recognition.
Learn to build a sentiment classifier using PyTorch and Huggingface's NLP library in a 1-2 hour tutorial by Yannic Kilcher.
Explore the world of self-supervised learning with Yannic Kilcher's under 1-hour material. Dive into intrinsic rewards, planning in latent space, and maximizing information gain.
Explore FNet architecture and Fourier Transforms in this brief, insightful study by Yannic Kilcher. Learn how to speed up Transformer encoders and reduce compute requirements.
Explore how DDPMs can outperform GANs in image generation with Yannic Kilcher's under 1-hour study of a machine learning research paper.
Explore the principles of convolution and involution in computer vision with Yannic Kilcher. Learn about spatial-specific computations, the Involution Operator, and RedNet architecture in under an hour.
Explore MLP-Mixer architecture, its experimental results, and effects of scale in less than an hour with Yannic Kilcher. Dive into the future of computer vision beyond CNNs and Transformers.
Explore Facebook AI's DINO system, combining Self-Supervised Learning and Vision Transformer architecture for impressive results in computer vision. Offered by Yannic Kilcher, under 1 hour.
Explore the concept of Neural Radiance Fields (NeRF) and view synthesis with Yannic Kilcher in less than an hour. Learn about training NeRF from sparse views, volume rendering, and more.
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