(DS03) Deep Learning

This two day course on ‘Deep Learning’ aims to provide a complete overview of topics in deep learning. Deep Learning has revolutionized machine learning and artificial intelligence in just over five years. The advancements in vision and text processing as well as game playing has been remarkable. This course is a combination of both theory and practical component.


This comprehensive course provides you with a complete introduction to Deep Learning.

Deep Learning has revolutionized analytics in just over five years. The field itself is changing very quickly, with interesting developments every day. This workshop is aimed to provide a complete introduction to Deep Learning.

Main topics include, Artificial Neural Networks (ANN), Deep ANN, Auto-Encoders, Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, LSTM, Attention-based methods, Generative Adversarial Networks (GANs), Variational Auto-encoders, Deep Reinforcement Learning, etc.

Target Audience:

The course is ideal (but not limited) for someone with some technical background, e.g., people with Science, Technology, Engineering or Mathematics background.

Learning Outcomes:

  • Complete understanding of where Deep Learning fits into Machine Learning
  • Understanding/appreciation of deep learning applications and technologies
  • Better understanding of problem-domains that can be solved by DL
  • Build deep learning solution using TensorFlow

Learning Methods:

Face to Face with Online instructor.

Program Benefits:

  • Informs attendees about the foundations of deep learning and related problems
  • Providing expertise on the use of Tensorflow and Keras to solve challenging problems such as object recognition, game playing, etc.


  • Session D1A - Introduction: a) Introduction to Machine Learning, b) Linear Regression and Logistic Regression, c) Model Selection, d) Feature Engineering, e) Regularization
  • Session D1B - Deep Neural Networks: a) ANN 2.0, b) Non-saturating activations, c) Reguarlization, Bach Normalisation, Adam, Gradient Estimations
  • Session D1C - Representation Learning: a) Auto-Encoders, b) Word2vec, c) Network Embedding
  • session D1D - Convolutional Neural Networks: a) CNN building blocks, b) CNN architectures, c) Object detection, d) CNN on 1-d data
  • Session D2A - Recurrent Neural Networks: a) RNN fundamentals, architectures and applications, b) Language Models, c) Advance RNN architectures (GRU/Attention/Transformers)
  • Session D2B - Working with GPUs: a) TF Serving, b) Deploying TF models on GPUs, c) Configuring/Managing GPUs
  • Session D2C - Data Generation: a) Variational Auto-Encoders, b) GAN, c) Adversarial Learning
  • Session D2D - Reinforcement Learning: a) Introduction to RL, b) Q-Learning, c) Policy Gradients, d) Actor-critic Methods

The price listed is the total price including GST where GST is applicable.

Upcoming Workshops

Name Date Time Location Cost
(DS03) Deep Learning (AEST) 21 & 22 August 2021 08:30 am - 04:30 pm On Line - Virtual Classroom $2,200.00
(DS03) Deep Learning (AEST) 28 & 29 August 2021 08:30 am - 04:30 pm On Line - Virtual Classroom $2,200.00


NOTE: Courses are subject to cancellation if insufficient student numbers.

You will be notified a minimum of 4 weeks prior to the course commencement.

You will not be charged until the course is confirmed.