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.

Topics to be covered in Workshop

Module 1: Convolution Neural Networks

  • Invariance, stability.
  • Variability models (deformation model, stochastic model). 
  • Scattering networks
  • 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.

Module 2: Deep Unsupervised Learning

  • Autoencoders (standard, denoising, contractive, etc etc)
  • Variational Autoencoders
  • Adversarial Generative Networks
  • Maximum Entropy Distributions

Module 3 : Miscellaneous Topics

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

Duration: The duration of this workshop will be two consecutive days, with eight hour session each day in a total of sixteen hours properly divided into theory and hands on sessions.

Certification Policy:

  • Certificate of Merit for all the workshop participants.
  •  At the end of this workshop, a small competition will be organized among the participating students and winners will be awarded with a 'Certificate of Excellence'.
  • Certificate of Coordination for the coordinators of the campus workshops.

Eligibility: It's a basic level workshop so there are no prerequisites. Any one interested, can join this workshop.

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