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Explore Meta AI's LLaMA language models with Yannic Kilcher in under an hour. Learn about training data, hyperparameters, architecture modifications, and more.
Explore robust metrics for text generation with Yannic Kilcher in under an hour. Learn about BLEURT, a BERT-based evaluation metric, and its correlation with human judgments.
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.
Learn about Word2Vec, influential in modern NLP, from Yannic Kilcher. Understand distributed word representations, skip-gram model, hierarchical softmax, and negative sampling in under an hour.
Explore the fundamentals of ResNets and their impact on modern computer vision in this concise study by Yannic Kilcher, covering residual connections, network depth, and practical applications.
Explore the effectiveness of Deep Ensembles in improving neural networks' generalization capabilities in this less than 1-hour material by Yannic Kilcher.
Explore the engineering choices for training a deep hierarchical Variational Autoencoder (VAE) with Yannic Kilcher. Learn about VAEs, their challenges, and how to achieve sharp, high-resolution results.
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