In recent years, deep learning has revolutionized the field of computer vision with algorithms that deliver super-human accuracy on the above tasks. Below is a list of popular deep neural network models used in computer vision and their open-source implementation....
Learn how deep learning works and how to use deep learning to design smart systems in a variety of applications. Resources include videos, examples, and documentation.
Deep learning is a kind of ML that entails training multiple-layer ANNs to recognize patterns in data. DL methods demand much larger datasets to perform better than typical ML applications. DL is especially helpful in fields with large and high-dimensional data [52]. Examples of deep learning ...
Despite the related algorithms have been scarcely applied to solve real situation tasks, the state of the art already presents some examples, such as Lillicrap et al. (2015), who introduce a deep learning reinforcement algorithm that solves more than 20 simulated physics tasks, including classic ...
Real-world deep learning applications are all around us, and so well integrated into products and services that users are unaware of the complex data processing that is taking place in the background. Some of these examples include: Customer service deep learning ...
Deep learning candetect advanced threatsbetter than traditional malware solutions by recognizing new, suspicious activities rather than responding to a database of known threats. Digital assistants Digital assistantsrepresent some of the most common examples of deep learning. With the help ofnatural langua...
Online security.Deep learning algorithms can protect against fraud by identifying security issues. For example, these algorithms can detect suspicious login attempts, send notifications and alert users if their chosen password isn't strong enough. ...
Input–output examples for program synthesis A large body of work addresses the problem of learning programs from input–output pairs. One type of approach learns a neural network for matching inputs to outputs directly11,13,67,68. This approach is difficult to integrate into existing libraries ...
Implementing a custom loss function in deep learning involves several steps, regardless of whether you’re using TensorFlow, PyTorch, or any other deep learning framework. Here’s a step-by-step guide with code examples using TensorFlow and PyTorch: Step 1: Choose a Suitable Loss Function: Deter...
We also employ data augmentation, where we randomly resize and add noise to the existing images, which in essence corresponds to highly increasing the number of training examples at our disposal30. The pre-trained network used in the present study is VGG 1630. We use the training samples (...