| Course Name: An Intuitive Approach To Deep Learning for Time-Series Forecasting
Course Code: TW-IADL
Duration: 5 days, 9:00am to 5.00pm
Location: 80 Jurong East Street 21 #04- 04, Devan Nair Institute, Singapore 609607
Course Fees: S$4,000 (excl of G.S.T)
|2018 Course Dates
12 – 16 Nov 2018
3 – 7 Dec 2018
|None of the published dates will work for you? Speak to our training consultants for a private tuition arrangement or a closed door training.|
|Do note that this course listed uses digital courseware. You are required to bring your own device to access the digital manual.|
Neural Network (NN) techniques for timeseries forecasting. Using the Caffe  deep learning framework, we will teach you how to create NN models for timeseries forecasting, which can be applied to any area that requires you to predict future events given time-varying data sources. Planning & Operations: supply chain forecasting, logistics planning, process optimization.
Understand how NNs work. To use them effectively, you have to really understand how NNs work. What they can and can’t do. We will take a bottom-up approach to teaching you how to translate the complex math into practical knowledge you can use to design and train NNs. This is reinforced with quizzes and 10 hands-on lab sessions.
Best practices for NN based forecasting. How to determine optimum network configurations? How to train complex networks? How to evaluate and track performance? When to apply a technique and when not to? We teach you how to develop an intuition of what will work and what won’t so you can reason for yourself.
Module 1: Introduction to Caffe & Linux
Module 2: Running Caffe
Module 3: Neural Network Basics
Module 4: The Backpropagation Algorithm
Module 5: Training and Evaluating Neural Networks
Module 6: Timeseries Data
Module 7: The Loss Function
Module 8: Deep Learning & Stacked Autoencoders
Module 9: Data Transformations
Module 10: Putting it All Together
Click Here for full course outline
Samuel Wang holds a masters’ degree in Physics from the National University of Singapore (NUS). He is a Data Scientist at AI@TerraWx and will be the lead trainer for this AI Workshop. Samuel has contributed to the development of TW Caffe (see http://ai.terrawx.com), our open-source fork of Caffe specifically aimed at timeseries forecasting. He also works on Autocaffe, a productivity tool to simplify deep learning on Caffe.
Arnold Doray holds a degree in Physics and masters in Knowledge Engineering from NUS. He leads product development at Terra Weather and is the lead developer of Autocaffe and the Mini scripting language we use for data processing. Arnold is the alternative trainer for this AI Workshop.