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The Vision Transformer Model

With the Transformer architecture revolutionizing the implementation of attention, and achieving very promising results in the natural language processing domain, it was only a matter of time before we could see its application in the computer vision domain too. This was eventually achieved with the implementation of the Vision Transformer (ViT).  In this tutorial, you […]

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How to Implement Multi-Head Attention from Scratch in TensorFlow and Keras

We have already familiarized ourselves with the theory behind the Transformer model and its attention mechanism. We have already started our journey of implementing a complete model by seeing how to implement the scaled-dot product attention. We shall now progress one step further into our journey by encapsulating the scaled-dot product attention into a multi-head […]

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How to Implement Scaled Dot-Product Attention from Scratch in TensorFlow and Keras

Having familiarized ourselves with the theory behind the Transformer model and its attention mechanism, we’ll start our journey of implementing a complete Transformer model by first seeing how to implement the scaled-dot product attention. The scaled dot-product attention is an integral part of the multi-head attention, which, in turn, is an important component of both […]

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TransformX by Scale AI is Oct 19-21: Register for free!

Sponsored Post     📣 The AI event of the year is quickly approaching… We’re talking about TransformX, a FREE virtual conference where you’ll hear from 120+ technology leaders from companies like Google, Meta, OpenAI, DeepMind, Amazon, and more. Explore how AI will power ecommerce, AI applications for healthcare, NFT marketplaces and more. 🎙 Speakers […]

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The Transformer Model

We have already familiarized ourselves with the concept of self-attention as implemented by the Transformer attention mechanism for neural machine translation. We will now be shifting our focus to the details of the Transformer architecture itself to discover how self-attention can be implemented without relying on the use of recurrence and convolutions. In this tutorial, […]

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The Transformer Attention Mechanism

Before the introduction of the Transformer model, the use of attention for neural machine translation was implemented by RNN-based encoder-decoder architectures. The Transformer model revolutionized the implementation of attention by dispensing with recurrence and convolutions and, alternatively, relying solely on a self-attention mechanism.  We will first focus on the Transformer attention mechanism in this tutorial […]

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An Introduction to Recurrent Neural Networks and the Math That Powers Them

When it comes to sequential or time series data, traditional feedforward networks cannot be used for learning and prediction. A mechanism is required to retain past or historical information to forecast future values. Recurrent neural networks, or RNNs for short, are a variant of the conventional feedforward artificial neural networks that can deal with sequential […]

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