Advanced Deep Learning

Course Code

dladv

Duration

28 hours (usually 4 days including breaks)

Requirements

Good understanding of Machine Learning. At least theoretical knowledge of Deep Learning.

Overview

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

Course Outline

  • Machine Learning Limitations
  • Machine Learning, Non-linear mappings
  • Neural Networks
  • Non-Linear Optimization, Stochastic/MiniBatch Gradient Decent
  • Back Propagation
  • Deep Sparse Coding
  • Sparse Autoencoders (SAE)
  • Convolutional Neural Networks (CNNs)
  • Successes: Descriptor Matching
  • Stereo-based Obstacle
  • Avoidance for Robotics
  • Pooling and invariance
  • Visualization/Deconvolutional Networks
  • Recurrent Neural Networks (RNNs) and their optimizaiton
  • Applications to NLP
  • RNNs continued,
  • Hessian-Free Optimization
  • Language analysis: word/sentence vectors, parsing, sentiment analysis, etc.
  • Probabilistic Graphical Models
  • Hopfield Nets, Boltzmann machines, Restricted Boltzmann Machines
  • Hopfield Networks, (Restricted) Bolzmann Machines
  • Deep Belief Nets, Stacked RBMs
  • Applications to NLP , Pose and Activity Recognition in Videos
  • Recent Advances
  • Large-Scale Learning
  • Neural Turing Machines

 

Client Testimonials

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Bookings, Prices and Enquiries

Guaranteed to run even with a single delegate!

Private Classroom

From £5800

Private Remote

From £5200 (98)

Public Classroom

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