Training Strategies for Transformers

Related lectures (23)

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Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Covers the impact of transformers in computer vision, discussing their architecture, applications, and advancements in various tasks.

Offers a historical overview of computer vision, exploring key developments and influential figures in the field.

Covers the scaling of language models, focusing on training efficiency and deployment considerations.

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Explores deep learning for question answering, analyzing neural networks and model robustness to noise.