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Related lectures (28)

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Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Discusses how robots manipulate objects using natural language instructions and integrates vision-language models for enhanced performance.

Discusses challenges and advancements in Transformers, pretraining models, and subword tokenization in NLP.

Outlines the project for building educational chatbots for EPFL courses, detailing its structure and requirements.

Covers the second assignment for the CS-552: Modern NLP course, focusing on transfer learning and data augmentation.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Delves into the use of custom GPTs for academic writing, highlighting the balance between AI assistance and traditional learning methods.

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.