What we're about

Welcome to the IBM Code London Meetup!

Latest News: our new office is now based in wework Moorgate :)

We're running a series of hands-on workshops (BYOD*) on a variety of technologies:

- Cloud Native Development and Containers
- Internet of Things
- Artificial Intelligence and Machine Learning
- Data Science
- Blockchain

Each of these tracks will be split in to several workshops diving deeper in to the subject each time.

What you can expect during our Meetups: Hands-on, talks, networking, food and drinks!

We hope to see you at our events!
IBM Developer Advocacy team

* Bring Your Own Device

Missed a session? All content is made available on: http://ibm.biz/content-code-ldn

Code of Conduct : https://github.com/IBMCodeLondon/infos/blob/master/code-of-conduct.md (https://github.com/arlemi/IBMCode_London/blob/master/code-of-conduct.md)

Upcoming events (2)

Data Science - Python and Pandas

CodeNode - Skills Matter

Pandas is one of the main Python libraries for manipulating and analysing structured data and one of the first things to learn if you want to get started with data science. This workshop is an introduction to Pandas for beginners where you will learn about: - Jupyter notebooks - Pandas data structures - Transforming and exploring data - Visualising data After an introduction to each of the subjects there will be some exercises to practice what you have learned. Nice to have is some knowledge of Python, but you should be able to follow along without as well. Please bring a laptop as this is a hands-on workshop. We will use IBM Watson Studio to go through the exercises, so there is no need to install anything beforehand. Agenda: 18.30 - 19.00: Registration, Food, Drinks and Networking 19.00 - 19.15: Short introduction 19.15 - 21.00: Hands on workshop Speakers: Margriet - Developer Advocate (@MargrietGr) Yamini - Developer Advocate (@yaminigrao)

Deep Learning Hands-on Workshop (In Partnership with Digital Catapult)

In collaboration with Machine intelligence Garage we are offering this hands on workshop which gives you a quick overview of several deep learning frameworks. With each framework, you’ll learn about the framework’s benefits, supported platforms, installation considerations, and supported back ends. Deep learning isn’t a single approach but rather a class of algorithms and topologies that you can apply to a broad spectrum of problems. While deep learning is certainly not new, it is experiencing explosive growth because of the intersection of deeply layered neural networks and the use of GPUs to accelerate their execution. Big data has also fed this growth. Because deep learning relies on supervised learning algorithms, the more data, the better to build these deep learning structures. Agenda: 18.30 - 19.00 : Registration, Food, Drinks and Networking 19.00 - 21.00 : Introduction, followed by a walkthrough of the Use Case Why GPUs Matter For Deep Learning ~ 15 mins What even is "Deep Learning"? Seriously, behind the scenes, how does this stuff work? We'll see a really quick worked example for recognising hand written digits, discussing the theory, and the infrastructure limitations that make GPUs THE platform for Deep Learning. Working with BIG data - Going beyond a GPU ~ 15 mins As we continually search for bigger, more complicated, problems the demands we place on the GPU continue to grow rapidly. So where next - how can we continue to build models and algorithms that scale as our data does or enable us to tackle even more intricate problems? In this session we'll talk about the Open Source Large Model Support. IBM's solution to the problem that enables TensorFlow, Caffe and PyTorch developers to overcome the GPU memory limitation and keep growing their deep learning workloads. Style Transfer - Replicating Art with AI (LAB) ~ 60 mins In our hands on lab we'll discuss how Style Transfer can be used to take the "style" or visual effect from one image and apply it to another, mimicking the style of an artist. We'll explore the theory behind the process, before getting hands on and running our own Jupyter notebook in Watson Studio to perform Style Transfer on our own images using the Watson Machine Learning service. Speaker: Chris Parsons - Machine Learning Developer Advocate at IBM

Past events (45)

Kubernetes 101

CodeNode - Skills Matter

Photos (124)