(Deep) Learn Neural Networks with PyTorch

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Location: Cyril Magnin II

Duration: 9:00am - 4:00pm

Day of week: Monday

Level: Beginner

Key Takeaways

Build a neural network, learn PyTorch, and use your GPU easily to compute faster.

Prerequisites

Attendees will be provided with an AWS Sagemaker instance.

In case you want to use your own laptop (you should have an NVIDIA GPU) you need to install:
- Python 3.7
- Cuda https://developer.nvidia.com/cuda-downloads
- Cudnn https://developer.nvidia.com/cudnn
We will send out a detailed e-mail with instructions to run install all required python packages two weeks before the conference.

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.

Speaker: 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

Speaker page

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

2019 Tracks

Hands-on Codelabs & Speakers Office Hours

Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.
Solving Software Engineering Problems with Machine Learning

Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
Predictive Architectures in the Real World

Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.

Predictive Data Pipelines & Architectures

Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix
Sequential Data: Natural Language, Time Series, and Sound

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

Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

Deep Learning in Practice

Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.
Handling Sequential Data Like an Expert / ML Applied to Operations

Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.

Half-day tracks
AI Meets the Physical World

Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.

2019 Schedule

Workshop: [SOLD OUT] (Deep) Learn Neural Networks with PyTorch