Getting Started With Deep Learning
Do you want to jump into the Deep Learning bandwagon? Are you still stuck on grasping how backpropagation works? Then this workshop is for you! The goal of this workshop is to get you up to speed with the advancements in Deep Learning with a practical perspective.
Along with this, you will also get to know about keras, a tool which has been getting heavy attention from the community as it provide simple higher level interface to build Deep Learning models. In this workshop, our objective is to make you comfortable to build image and text classification deep learning models using keras. And, you will also learn the building blocks of deep learning: MLP (Multi Layer Perceptron), CNN(Convolutional Neural Network) and RNN (Recurrent Neural Network).
Prerequisites for the workshop:
- Python programming experience
- Basic knowledge of machine learning
STRUCTURE OF THE WORKSHOP
This is an 8-hour workshop and includes the following modules:
- Introduction to Deep Learning
- Getting started with Keras
- Working with Image dataset
- Understanding Building Block of a Neural Network
- Building a simple neural network using Keras
- Parameter tuning in Deep Learning
- Solving an image classification challenge – Fashion MNIST
- Image Processing using Convolutional Neural Network
- Natural Language Processing using Recurrent Neural Network and LSTM
- Solving a text classification challenge using RNN/ LSTM
- Where to go from here?
INSTRUCTORS
Xander Steenbrugge
Xander is a computer-science engineer from Belgium, Europe, fascinated by Machine Learning and Artificial Intelligence and YouTube vlogger at ‘Arxiv Insights’. Through his master thesis on EEG-based brain-computer interfaces, Xander first came into contact with the vast opportunities provided by data driven computer algorithms. Working as a Machine Learning consultant for 4 years, Xander has worked on many different projects including computer vision (object tracking, optical character recognition, image classification, ..) , natural language processing (chatbots, text classification, …) and many others using open source libraries like TensorFlow and Pytorch in combination with powerful compute resources on the Google cloud platform.Recently, Xander started focussing on the interface between academic research and the real world through a PhD in Deep Reinforcement Learning focussed on applying these novel algorithms to industrial process optimization. Xander is now head of applied ML-research at Belgian AI scale-up ML6.