Keras

We have already familiarised ourselves with the theory behind the Transformer model and its attention mechanism, and 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 attention mechanism, of which it is a core component. Our end goal remains the application of the complete model to Natural Language Processing (NLP). In…

Having seen how to implement the scaled dot-product attention, and integrate it within the multi-head attention of the Transformer model, we may progress one step further towards implementing a complete Transformer model by implementing its encoder. Our end goal remains the application of the complete model to Natural Language Processing (NLP). In this tutorial, you will discover how to implement the Transformer encoder from scratch in TensorFlow and Keras.  After completing this tutorial, you will know: The layers that…

Deep learning networks have gained immense popularity in the past few years. The ‘attention mechanism’ is integrated with the deep learning networks to improve their performance. Adding attention component to the network has shown significant improvement in tasks such as machine translation, image recognition, text summarization and similar applications. This tutorial shows how to add a custom attention layer to a network built using a recurrent neural network. We’ll illustrate an end to end application…

This tutorial is designed for anyone looking for an understanding of how recurrent neural networks (RNN) work and how to use them via the Keras deep learning library. While all the methods required for solving problems and building applications are provided by the Keras library, it is also important to gain an insight on how everything works. In this article, the computations taking place in the RNN model are shown step by step. Next, a complete end to end system for time series prediction is developed. After completing…

In part 1: A gentle introduction to positional encoding in transformer models, we discussed the positional encoding layer of the transformer model. We also showed how you can implement this layer and its functions yourself in Python. In this tutorial, we’ll implement the positional encoding layer in Keras and Tensorflow. You can then use this layer in a complete transformer model. After completing this tutorial, you will know: Text vectorization in Keras Embedding layer in Keras How to subclass the embedding layer…

Deep CNNs used for segmentation often suffer from vanishing gradients. Can we combat this by calculating loss at different output levels?Image segmentation entails partitioning image pixels into different classes. Some applications include identifying tumour regions in medical images, separating land and water areas in drone images, etc. Unlike classification, where CNNs output a class probability score vector, segmentation requires CNNs to output an image.Image segmentation of a tennis player (Source-Creative Commons…

A step-by-step tutorialView from Monte San Vigilio (Vigiljoch), Trentino-Alto Adige, Italy. Image by author.IntroductionEnvironment SetupDatasetPreprocessingBuilding the neural network5.1 Define the layers5.2 Compile5.3 FitTest set performancesConclusionsReferencesBoth R and Python are useful and popular tools for Data Science. However, when it comes to Deep Learning, it is most common to find tutorials and guides for Python rather than R.This post provides a simple Deep Learning example in the R language. It aims at…

Learn how to build a model capable of performing multiple image classifications concurrently with Multiple-Task LearningPhoto by Markus Winkler on UnsplashMulti-task learning (MLT) is a subfield of Machine Learning in which multiple tasks are simultaneously learned by a shared model. This type of learning helps to improve data efficiency and training speed, because the shared model will learn several tasks from the same data set, and will be able to learn faster thanks to the auxiliary information of the different tasks.…

Using the Functional API — Neural Networks and Deep Learning Course: Part 29Original photo by refargotohp on Unsplash, edited by authorAlgorithms are useless if we ignore their practical applications.We’ve already discussed the principles behind autoencoders. It is time to discuss their practical applications. Before that, you should know how autoencoders are implemented in Keras.So, in this article, I will discuss the Keras implementation of autoencoders by building two autoencoder models on the MNIST data (see dataset…

Last Updated on July 20, 2022 When we work on a machine learning problem related to images, not only we need to collect some images as training data, but also need to employ augmentation to create variations in the image. It is especially true for more complex object recognition problems. There are many ways for image augmentation. You may use some external libraries or write your own functions for that. There are some modules in TensorFlow and Keras for augmentation, too. In this post you will discover how we can use…