A Gentle Introduction to the tensorflow.data API

When you build and train a Keras deep learning model, you can provide the training data in several different ways. Presenting the data as a NumPy array or a TensorFlow tensor is common. Another way is to make a Python generator function and let the training loop read data from it. Yet another way of providing data is to use tf.data dataset. In this tutorial, you will see how you can use the tf.data dataset for a Keras model. After finishing […]

January 19, 2024 Machine Learning

Understanding the Design of a Convolutional Neural Network

Convolutional neural networks have been found successful in computer vision applications. Various network architectures are proposed, and they are neither magical nor hard to understand. In this tutorial, you will make sense of the operation of convolutional layers and their role in a larger convolutional neural network. After finishing this tutorial, you will learn: How convolutional layers extract features from an image How different convolutional layers can stack up to build a neural network Let’s get started. Understanding the design […]

January 19, 2024 Machine Learning

High-Fidelity Synthetic Data for Data Engineers and Data Scientists Alike

Sponsored Post If you’re a data engineer or data scientist, you know how hard it is to generate and maintain realistic data at scale. And to guarantee data privacy protection, in addition to all your day-to-day responsibilities? OOF. Talk about a heavy lift. But in today’s world, efficient data de-identification is no longer optional for teams that need to build, test, solve, and analyze in fast-paced environments. The rise in ever-stronger data privacy regulations make de-identification a requirement, and the […]

January 19, 2024 Machine Learning

Loss Functions in TensorFlow

The loss metric is very important for neural networks. As all machine learning models are one optimization problem or another, the loss is the objective function to minimize. In neural networks, the optimization is done with gradient descent and backpropagation. But what are loss functions, and how are they affecting your neural networks? In this post, you will learn what loss functions are and delve into some commonly used loss functions and how you can apply them to your neural […]

January 19, 2024 Machine Learning

Image Augmentation with Keras Preprocessing Layers and tf.image

When you work on a machine learning problem related to images, not only do you need to collect some images as training data, but you 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, […]

January 19, 2024 Machine Learning

Using Depthwise Separable Convolutions in Tensorflow

Looking at all of the very large convolutional neural networks such as ResNets, VGGs, and the like, it begs the question on how we can make all of these networks smaller with less parameters while still maintaining the same level of accuracy or even improving generalization of the model using a smaller amount of parameters. One approach is depthwise separable convolutions, also known by separable convolutions in TensorFlow and Pytorch (not to be confused with spatially separable convolutions which are […]

January 19, 2024 Machine Learning

A Bird’s Eye View of Research on Attention

Attention is a concept that is scientifically studied across multiple disciplines, including psychology, neuroscience, and, more recently, machine learning. While all disciplines may have produced their own definitions for attention, one core quality they can all agree on is that attention is a mechanism for making both biological and artificial neural systems more flexible. In this tutorial, you will discover an overview of the research advances on attention. After completing this tutorial, you will know: The concept of attention that […]

January 19, 2024 Machine Learning

What Is Attention?

Attention is becoming increasingly popular in machine learning, but what makes it such an attractive concept? What is the relationship between attention applied in artificial neural networks and its biological counterpart? What components would one expect to form an attention-based system in machine learning? In this tutorial, you will discover an overview of attention and its application in machine learning. After completing this tutorial, you will know: A brief overview of how attention can manifest itself in the human brain […]

January 19, 2024 Machine Learning

The Attention Mechanism from Scratch

The attention mechanism was introduced to improve the performance of the encoder-decoder model for machine translation. The idea behind the attention mechanism was to permit the decoder to utilize the most relevant parts of the input sequence in a flexible manner, by a weighted combination of all the encoded input vectors, with the most relevant vectors being attributed the highest weights. In this tutorial, you will discover the attention mechanism and its implementation. After completing this tutorial, you will know: […]

January 19, 2024 Machine Learning

A Tour of Attention-Based Architectures

As the popularity of attention in machine learning grows, so does the list of neural architectures that incorporate an attention mechanism. In this tutorial, you will discover the salient neural architectures that have been used in conjunction with attention. After completing this tutorial, you will better understand how the attention mechanism is incorporated into different neural architectures and for which purpose. Kick-start your project with my book Building Transformer Models with Attention. It provides self-study tutorials with working code to […]

« 1 … 61 62 63 64 65 … 908 »