Deep Learning for Vision Training Course

Description

• Type

  Course

• Methodology

  Online

Description

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.

About this course

Requirements

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

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Course programme

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...

Additional information

21 hours (usually 3 days including breaks)

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