#5, First Floor, 4th Street , Dr. Subbarayan Nagar, Kodambakkam, Chennai-600 024 pro@slogix.in

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • pro@slogix.in
  • +91- 81240 01111

Social List

Deep Learning Models

Deep learning models are extremely beneficial in collecting, handling, interpreting, and analyzing a vast amount of data efficiently. Deep learning models are widely used for extracting abstract features for complex problems and provide superior performance over traditional methods. Deep Learning Model is a subset of the Machine Learning model that incorporates neural networks in successive layers to learn from data iteratively. The working of deep learning model based on the human brain for processing the datasets and making efficient decision making. Models can be trained by using a large set of labeled data and neural networks that contain many layers. Deep learning models are deeper alterations of artificial neural networks (ANNs) with multiple layers, whether linear or non-linear. Deep learning methods are categorized into supervised, semi-supervised, and unsupervised learning. Recurrent Neural Networks, Convolutional Neural Network, Deep Neural Network, Deep Belief Network, Generative Adversarial Network, Radial Basis Function Networks, Restricted Boltzmann Machine, Long short term memory networks, Autoencoder and Self Organizing Maps are the most commonly used deep learning algorithms. In recent years, Deep learning models have gained tremendous success in a wide range of applications, particularly object detection, health care, medical research, Natural language processing, speech, and audio processing, Virtual assistants, driver-less vehicles, Aerospace and defense, transportation prediction, disaster management, face recognition, fraud detection, and predictive forecasting. Future research directions of deep learning models are automation in data annotation, data preparation for ensuring data quality, black box perception, hybrid modeling, and uncertainty handling, and Lightweight Deep Learning Modeling for Next-Generation Smart Devices and Applications