Author: Nathalie Rauschmayr

Research over the past few years has shown that machine learning (ML) models are vulnerable to adversarial inputs, where an adversary can craft inputs to strategically alter the model’s output (in image classification, speech recognition, or fraud detection). For example, imagine you have deployed a model that identifies your employees based on images of their […]

Rapid development of deep learning technology has produced an abundance of open-sourced, pre-trained models in computer vision and natural language processing. As a result, transfer learning has become a popular approach in deep learning. Transfer learning is a machine learning technique where a model pre-trained on one task is fine-tuned on a new task. Given […]

Convolutional neural networks (CNNs) achieve state-of-the-art results in tasks such as image classification and object detection. They are used in many diverse applications, such as in autonomous driving to detect traffic signs and objects on the street, in healthcare to more accurately classify anomalies in image-based data, and in retail for inventory management. However, CNNs […]

In the past decade, deep learning has advanced many different areas, such as computer vision and natural language processing. State-of-the-art models now achieve near-human performance in tasks such as image classification. Deep neural networks can achieve this because they consist of millions of parameters that you train on large training datasets. For instance, the BERT […]