Teckzite || Deep learning

Teckzite - Deep learning

About the workshop

            Preventing disease, Building smart cities, Revolutionizing analytics. These are just a few things happening today with Artificial Intelligence and, specifically, Deep Learning. Already, organizations are using Deep Learning to transform moonshots into real results.Deep Learning is a sub-field of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

             Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a class of machine learning algorithms. Deep learning algorithms transform their inputs through more layers than shallow learning algorithms. At each layer, the signal is transformed by a processing unit, like an artificial neuron, whose parameters are 'learned' through training.

            It’s a hands-on class; you’ll learn to implement and understand both deep neural networks as well as unsupervised techniques.    

Note :

  • Top performers will be provided internship opportunity AMZ Automotive.


Convolution Neural Networks

  • Invariance , stability.
  • Variability models (deformation model, stochastic model).
  • Scattering networks and Group Formalism
  • Supervised Learning: classification.
  • Properties of CNN representations: inevitability, stability, invariance.
  • Covariance/invariance : capsules and related models.
  • Connections with other models: dictionary learning, LISTA.
  • Other tasks: localization, regression.
  • Embeddings (DrLim), inverse problems
  • Extensions to non-euclidean domains
  • Dynamical systems: RNNs.

 Deep Unsupervised Learning

  • Auto encoders (standard, denoising, contractive, etc )
  • Variational Auto encoders.
  • Adversarial Generative Networks
  • Maximum Entropy Distributions

Miscellaneous Topics

  • Non-convex optimization for deep networks
  • Stochastic Optimization
  • Attention and Memory Models
  • Open Problems

Rules & Regulations


  • Each participant should bring one laptop.
  • Participants having a valid ID card of their respective educational institutions are eligible for the workshop.
  • Participants should be present in all the sessions. Failing this, no certificate will be awarded to the participant.


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Contact Details

Name - Ch. Sunil Kumar
Mobile No - 7013228098
Email -