NUS ISS Deep Learning Masterclass on Computer Vision

This Masterclass will give participants an update on the latest advances in Deep Learning from the industry perspective and more significantly provides a practical jumpstart into Deep Learning using Deep Neural Networks in computer vision applications.

Key Takeaways

  • Gain a practical understanding about Deep Learning, Convolutional Neural Network and Network Architectures.
  • Learn & Apply Convolutional Neural Network to image classification problems
  • Acquire competencies in using TensorFlow framework and building image classifier together with pre-processing pipeline


  • Basic Python programming (Python3.5)
  • Understanding of Machine Learning & Neural Network concepts
  • Bring your own laptop (optional)
Introduction to Computer Visions

Introducing Computer Vision, different applications of CV, approach with hand-crafted feature extractors.

History of Deep Learning

Inspired by biological neurons, brief on neural network and define deep neural network. The first paper that toys around the idea and the experiment will be mentioned.

Introduction to Convolutional Neural Networks (CNN)

Overview on multi-layer perceptron and origins of convolutional neural network. Concepts such as Local Connectivity, Spatial arrangement, constraints on strides and use of zero padding are introduced.

Network Architectures

Sharing network architectures of CNN, namely LeNet, AlexNet, ResNet, etc. Building a ConvNet, e.g.: Input, Conv, ReLU, Pool, Fully-connected layers.

ImageNet Benchmarks

Illustrate the accuracy of network architectures on Imagenet dataset.

Visualizing the black box

Making deep learning more transparent by visualizing the convolutional units. Sneak peek into a neuron to better understand workings of neural networks.

Recent advancements (Generative Adversarial Networks-GANs)

Latest architecture, applications and how well it fare against previous approaches.

Practical tips on training Deep Neural Networks (DNN) models

Sharing tips on model training and general rule of thumb on setting various parameters.

Deep Learning Frameworks & Libraries

Big tech companies, e.g. Google, Facebook, Amazon, etc have their own deep learning framework. Learn the pros and cons as a starter.

Environment set-up

Setup Python environment for workshop.
Motivations of TensorFlow

What is TensorFlow and why do we use it?

A brief introduction to TensorFlow

Building a simple one-hidden-layer neural network using TensorFlow

Build deep image classifier using Convolutional Neural Networks

Convolutional, pooling and Fully-Connected layers, lose functions, training and evaluation

Techniques to improve model accuracy

Batch normalisation Dropout L2 regularisation

Image augmentations

Using pre-trained model for transfer learning (Optional)


Code walkthrough and how TensorFlow API works. Participants are expected to apply what they learn in the walkthrough session and produce an accurate model on the other dataset provided.
NUS ISS Deep Learning Masterclass on Computer Vision
NUS ISS Deep Learning Masterclass on Computer Vision Registration



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