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Deep Learning


Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep-learning software attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs.

the software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data.


Acclivis has dedicated R&D team that works in the area of deep learning and artificial intelligence. The team has worked on multiple projects with major automobile company in developing vehicles based on ADAS.


  • Training on the desired platform for the given set of applications and         requirements
  • Designing consultation in terms of choosing a neural network platform and        architecture for client’s target hardware and applications for specific training         purposes.
  • Platform Optimization neural network path for a number of platforms and        architectures to improve performance and power efficiency while retaining its         accuracy and predictive capability
  • New Model development, deployment and long-term maintenance with         respect to client’s requirements for application and hardware platform.
  • Incremental Updates and Maintenance of neural network as more data        available to continuously improve its accuracy. As the target hardware for the        network advances, the network’s predictive capabilities can also be upgraded       by using additional computer power, expanded memory, and changing power         profiles.


  • Head pose estimation
  • Object identification and tracking
  • Object recognition
  • Decision making
  • Object classification
  • Prediction of lighting conditions

  • Deep Learning
  • OCR
  • Object detection and classification
  • Time series predictions using LSTM net         architecture

  • Image Enhancement
  • Water Level Estimation
  • Shape analysis

  • Caffe
  • Tensorflow
  • C++ development of SVM