Deep Learning Certification Test

51 Ratings

Topics covered - Syllabus

  • Autoencoders (stochastic encoders and decoders, undercomplete, regularized, etc)
  • Convolutional networks (pooling, data types,structured outputs, variants etc.)
  • Deep forwarded networks (architecture,hidden units,back propagation,gradient based learning Etc)
  • Deep generative models (deep belief networks, deep boltzmann machines etc)
  • Informatin theory and Probability(Random variables,probability distributions,bayes rule etc)
  • Linear Algebra (Norms, Eigen decomposition,trace operator,PCA,tensors,matrices,vectors etc)
  • Linear factor models (Independent component,PCA and factor,sparse coding, slow feature) etc
  • Machine Learning Basics (supervised and unsupervised learning, maximum likelihood estimation etc)
  • Monte carlo methods(gibbs sampling,markov chain monte carlo methods etc )
  • Numerical Computation(Poor conditioning,gradient based optimization,overflow and underflow etc)
  • Optimization for training deep models
  • Partition function (log-likelihood gradient,denoising score matching,ratio matching etc)
  • Practical Methodology (default baseline models,debugging strategies etc)
  • Representational Layering(transfer layering and domain adaptation,distributed representation etc.)
  • Structured Probabilistic models for Deep learning

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Total Tested


Total Passed - 10072

Total Failed - 15822

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