Host: Jendrik Jördening

Data Scientist @Nooxit

Jendrik is Head of Data Science at a stealth startup. He formerly worked at Aurubis and Akka Germany on Data Science and Deep Learning in the field of industry 4.0 and autonomous machines.
At the same time he took part in the Udacity Self-Driving Car Nanodegree, participating with a group of other Udacity student in the Self-Racing Cars event at the Thunderhill race-track in California.

Find Jendrik Jördening at

http://www.linkedin.com/in/jendrik

Workshop : (Deep) Learn Neural Networks with PyTorch

Location : Embarcadero
Day of week : Monday
Duration : 9:00am - 4:00pm
This workshop will teach you PyTorch and building neural networks with it. 
It will provide you with all the necessary knowledge to get started with crunching vast amounts of data and generating rules from them.
You will learn the following:
  • Build and train a Perceptron in numpy
  • Move the code to the GPU using PyTorch
  • Extend the neural network for more complex time-series forecasting
  • Introduction to NLP
  • TCN vs. RNN
This workshop aims at programmers comfortable with Python who want to expand their skills towards neural networks. It is recommended to be familiar with the basics of machine learning (but not mandatory). E.g. being familiar with train, validation, test splits and scaling would be perfect. 
It is not required that you have dealt with neural networks before.
You won’t need to setup your computer for this workshop either. 
We will take care of providing you an adequately setup cloud instance.
For those of you who prefer programming locally and having a computer with an NVIDIA GPU and Linux, instructions for preparing the computer will be sent out, too.

2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

Schedule