How should we define AI? In our very first section, we’ll become familiar with the concept of AI by looking into its definition and some examples. Application 1. Self-driving c...

Training the Transformer Model After completing this tutorial, you will know: How to prepare the training dataset How to apply a padding mask to the loss and accuracy computatio...

We have put together the complete Transformer model, and now we are ready to train it for neural machine translation. We shall use a training dataset for this purpose, which con...

How to Implement Scaled Dot-Product Attention from Scratch in TensorFlow and Keras After completing this tutorial, you will know: The operations that form part of the scaled dot...

This tutorial is divided into four parts; they are: Recap of the Transformer Architecture Masking Creating a Padding Mask Creating a Look-Ahead Mask Joining the Transformer Enco...

The queries, keys, and values: These are the inputs to each multi-head attention block. In the encoder stage, they each carry the same input sequence after this has been embedde...

How to Implement Scaled Dot-Product Attention from Scratch in TensorFlow and Keras The operations that form part of the scaled dot-product attention mechanism How to implement t...

The Import Section First, let’s write the section to import all the required libraries: import tensorflow as tf from tensorflow import convert_to_tensor, string from tensorflo...

How the Transformer architecture implements an encoder-decoder structure without recurrence and convolutions How the Transformer encoder and decoder work How the Transformer sel...

After completing this tutorial, you will know: How the Transformer attention differed from its predecessors How the Transformer computes a scaled-dot product attention How the T...

Attention Fundamentals What Is Attention? A Bird’s Eye View of Research on Attention A Tour of Attention-Based Architectures The Luong Attention Mechanism This tutorial is divi...

Tutorial Overview This tutorial is divided into five parts; they are: What Is the CycleGAN Architecture? How to Implement the CycleGAN Discriminator Model How to Implement the C...

How to Generate Images With LSGAN We can use the saved generator model to create new images on demand.......

The conceptual shift in the WGAN from discriminator predicting a probability to a critic predicting a score. The implementation details for the WGAN as minor changes to the stan...

from numpy import hstack from numpy import zeros from numpy import ones from numpy.random import rand from numpy.random import randn from keras.models import Sequential from ker...

Heuristics for Training Stable GANs Best Practices for Deep Convolutional GANs Downsample Using Strided Convolutions Upsample Using Strided Convolutions Use LeakyReLU Use Batch ...

This tutorial is divided into three parts; they are: Need for Upsampling in GANs How to Use the Upsampling Layer How to Use the Transpose Convolutional Layer How to Use the UpSa...

Step 1: Discover the promise of GANs for generative modeling. 18 Impressive Applications of Generative Adversarial Networks Step 2: Discover the GAN architecture and different G...