Transformers: Unifying Machine Learning Communities

Related lectures (31)

Covers fundamental principles, opportunities, and challenges in machine learning.

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

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

Explores advancements in generative AI and reinforcement learning, focusing on their applications, safety, and future research directions.

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Explores the historical development of computer vision and inspires innovative project ideas.

Discusses Generative Adversarial Networks and their applications in synthesizing data and generating images.

Covers PyTorch tensor data structure and training a CNN to classify images.

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.

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