Deep Learning Algorithms and Applications

EasyChair Preprint no. 12551

Abstract

Deep learning algorithms have emerged as powerful tools for solving complex problems across various domains. This abstract provides an overview of deep learning algorithms and highlights their wide-ranging applications. Deep learning refers to a subset of machine learning techniques that leverage artificial neural networks with multiple layers to extract high-level representations from raw data. These algorithms have demonstrated remarkable success in areas such as computer vision, natural language processing, speech recognition, and recommendation systems.

The abstract begins by introducing the concept of deep learning and its fundamental principles. It explains how deep neural networks are constructed by stacking multiple layers of interconnected neurons, allowing for hierarchical feature extraction and nonlinear transformations. The training process, typically based on backpropagation and gradient descent, is briefly described.

Moving forward, the abstract delves into the applications of deep learning algorithms. In computer vision, deep learning has revolutionized object detection, image classification, and segmentation tasks. Convolutional neural networks (CNNs) have played a pivotal role in achieving state-of-the-art performance in image-related problems. Furthermore, deep learning techniques have been applied to medical imaging, enabling accurate diagnosis and detection of diseases.

In natural language processing, deep learning models such as recurrent neural networks (RNNs) and transformer-based architectures have demonstrated exceptional capabilities in tasks like machine translation, sentiment analysis, and text generation. These models have also been employed in speech recognition systems, yielding significant improvements in accuracy and robustness.

Keyphrases: AI, computing, science

@Booklet{EasyChair:12551,
  author = {Favour Olaoye and Kaledio Potter},
  title = {Deep Learning Algorithms and Applications},
  howpublished = {EasyChair Preprint no. 12551},

  year = {EasyChair, 2024}}