Course Code: dlv
Duration: 21 hours
Prerequisites:

Any programming language knowledge is required. Familiarity with Machine Learning is not required but beneficial.

Overview:

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images

This course provide working examples.

Course Outline:

Deep Learning vs Machine Learning vs Other Methods

  • When Deep Learning is suitable
  • Limits of Deep Learning
  • Comparing accuracy and cost of different methods

Methods Overview

  • Nets and  Layers
  • Forward / Backward: the essential computations of layered compositional models.
  • Loss: the task to be learned is defined by the loss.
  • Solver: the solver coordinates model optimization.
  • Layer Catalogue: the layer is the fundamental unit of modeling and computation
  • Convolution​

Methods and models

  • Backprop, modular models
  • Logsum module
  • RBF Net
  • MAP/MLE loss
  • Parameter Space Transforms
  • Convolutional Module
  • Gradient-Based Learning 
  • Energy for inference,
  • Objective for learning
  • PCA; NLL: 
  • Latent Variable Models
  • Probabilistic LVM
  • Loss Function
  • Detection with Fast R-CNN
  • Sequences with LSTMs and Vision + Language with LRCN
  • Pixelwise prediction with FCNs
  • Framework design and future

Tools

  • Caffe
  • Tensorflow
  • R
  • Matlab
  • Others...
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