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Hi, Im Mohammad Namvarpour. Today I want to explain the paper Pay Attention to MLPs published by google researchs brain team. This article claims that self-attention blocks used in transformer architecture arent necessary in many applications, and offers the gMLP architecture, which delivers outcomes comparable to transformers without using attention. Transformers have been one of the most important architectural advancements in deep learning in recent years, enabling many breakthroughs. They have revolutionized Natural language processing. And have also been employed in other fields including computer vision. This figure shows the main components of transformer model. If youre unfamiliar with them, I recommend watching my in-depth video on the paper attention is all you need, in which I go through the architecture in great detail.In any case, this architecture is primarily composed of two key components. Self-attention, and feed forward net