11 Dez 19
ML Conference – The Conference for Machine Learning Innovation
UNDERSTAND YOUR DATA
Making sense of your data is becoming key for every modern predictive business. At ML Conference you will develop a deep understanding of your data, as well as of the latest tools and technologies.
OPTIMISE YOUR MODELS
Learn from seasoned experts which methods, libraries, services, models and algorithms to use and, crucially, hear their war stories of training cutting edge machine learning algorithms.
ENHANCE YOUR BUSINESS
Make machine learning central to your business model: join ML Conference to gain key knowledge and skills for this new era of data driven business.Visit the website to learn more about the speakers, the program and how to get tickets: https://mlconference.ai/
This hands-on workshop is for developers of all skill level to come together to learn machine learning, Tensorflow and language processing.
Get Free official Google training. Together we'll work through several Machine learning labs. You will get hands-on experience with the Machine Learning and Tensorflow. The event features a blend of tech talks, code labs, discussions, demos and networking.
Then after our live session, you will have free access to more labs you can finish at home. Complete all labs in the quest and earn a Google-hosted badge for your online profile, and additional 30 days access to the training platform to compete any labs you want.
- 5:00-5:30pm: Checkin & food
- 5:30-5:35pm: welcome and intro
- 5:35-6:00pm: lecture on Machine learning and NLP, by Tal Perry
- 6:00-7:00pm: code labs 1, by Gad Beram
- 7:00-7:15pm: break* 7:15-8:15pm: code labs 2, by Gad Beram
- 8:15:-8:30pm: Wrap up and close
- Gad Beram is the Machine Learning Tech Lead at DoiT International (Google Cloud partner) and ML GDE
- Tal Perry, Google ML GDE
This is our last meetup for 2019 and the wind of change is blowing, both Meili and Jessica are stepping down from active organizing to switch in a more support role and would like to share their latest leanings and celebrate with you this amazing time.
Christmas is also close, we hope to get also some Christmas atmosphere flowing after the talks!
Talk 1 : Lessons learned from 6 years in Pyladies Berlin, by Meili Triantafyllidi
Pyladies Berlin is already running for 6.5 years. It has been an amazing time. Meili will give an overview of all the learning within these years. Meili is a Pyladies Berlin organizer since the chapter started in 2013. She is also a Senior Python Engineer. She loves Pyladies community, but she is currently stepping down to focus more in learning German.
Talk 2: Sorting Algorithms in Python, by Jessica Greene
Recently in a an attempt to improve my Python Skills I looked at different mathematical algorithms and how they can be programmed in Python. In this talk I will discuss those algorithms, how Python allows us to implement them and what I learnt along the way. Jessica joined the PyLadies Berlin team after joining a Meetup when first learning to code in early 2018. Her 2020 goal is to combine her support of the community with her pursuit of becoming a better programmer through coaching and designing workshops <3
Women in Machine Learning and Data Science (WiMLDS) in Berlin! WiMLDS meetups aim to inspire, educate, regardless of gender, and support women and gender minorities in the field. All genders may attend our meetup.
Our speaker, Katharina Rasch is a computer scientist with a PhD from KTH Stockholm. From 2014 to 2017 she was a data scientist / computer vision researcher at zalando. Now she is a freelance data scientist in Berlin. At the moment, Katharina is obsessed with professionalising AI development. Less chaos, please!
As a data scientist I often feel envious of the tooling available to software engineers. Tools for build automatisation, continuous integration, code review, etc help software engineers follow established best practices. In contrast, many of us data scientists have taken to building our own tools for things like managing experiments, for tracking data, for enabling reproducibility. Of course, writing such tools is hard and takes a lot of effort.
Fortunately, the good news is: more and more software supporting data science best practices is becoming available to us. From stand-alone packages such as DVC, polyaxon to Software as a Service solutions such as floydhub, valohai. The bad news is: there really are a lot of these tools around and it is hard to know which one to go with.
In this talk I want to show you, how readily available tools can help you follow best practices in data science. I will focus on the model development phase of a data science project, I will not be talking about tooling for model deployment. I will start with an overview of available tools and will then do a deep-dive comparison of 2-3 tools and show how they support you with things like
- Versioning data
- Tracking which data / code / library versions / parameters are used in which experiment
- Easily comparing / visualising experiment results
- Enabling everybody in your team / future you to replicate experiments
I will also compare them on non-technical dimensions such as
- Ease of use / collaboration
- Price (especially for SaaS solutions)
- Vendor lock-in
After this talk you should have a good idea of which tools already are available and which things you can/should look for when deciding if a tool is right for your project.
Topic: MediaPipe: Building real time cross platform video audio ML pipelines
Video, audio (multimodal) mobile and edge use cases that utilize machine learning models (eg Tiktok, Shazam, Google Home Hub) are becoming more common. However, creating these multimodal ML applications are challenging as developers need to deal with real time synchronization of time series data during model inference and doing it cross platform on mobile and edge devices.
Google open sourced in Jun 2019, MediaPipe (https://mediapipe.dev), a cross platform applied machine learning pipeline framework that simplifies the development process. My talk will be introducing open source MediaPipe framework, walking through mobile and edge (EdgeTPU coral) demos and getting developers started on building multimodal ML applications https://mediapipe.page.link/handgoogleaiblog
Speaker: Ming Yong is a Product manager in Google Research Perception Research leading open source efforts in computer vision. In Google, he was previously product manager in Google Search and product lead for mobile video ad formats. Before Google, Ming was cofounder Socialwok, an enterprise collaboration service for Google Apps (Finalist of the Techcrunch Disrupt 2011) and Voiceroute, a startup focused on open source VOIP telephony services for small medium enterprises.
Spark is one of the technologies that many people consider for their data processing / ETL needs. In this edition of the Data Council Berlin Meetup, we want to delve deeply into the pros and cons of data pipelining with Spark.
Two of the companies in Berlin that use Spark at a significant scale are GetYourGuide and HelloFresh and we are very happy to have David Mariassy and Rodrigo Peternella reporting first hand about the challenges of applying Spark to their data problems.
PyData Berlin veteran Matti Lyra will add an additional perspective by sharing his experiences on building data pipelines with Dask.We want the evening to be opinionated. If you can share your experiences with Spark, then please reach out to Daniel or me for a talk or just come there and join the discussion.
Doors open 18:30h, there will be food at drinks.
We will host our 15th Meetup at Wunder Mobility in the Hafencity, Hamburg. This time our talks will focus on various industry use-cases of AI from the domain of predictive maintenance.
Like always, we will have some amazing speakers and the opportunity to network and continue to build our community. Thanks to our sponsors there will be food and drinks!
Talk 1: Paul-Louis Pröve - Consultant & Data Scientist @Lufthansa Industry SolutionsLI: https://www.linkedin.com/in/gopietz/Topic: Predictive Maintenance - From Wine Cellars to 40,000ft
Talk 2: Sergei Bobrovskyi - Data Scientist @AirbusLI: https://www.linkedin.com/in/sergei-bobrovskyi-382aba76/Topic: Time Series Anomaly Detection with Deep Learning
Talk1: Dr Dror Atariah // Introduction to DVC, an open-source tool for data science and machine learning projects
Talk 2: Tamara Atanasoska // Multiple dispatch (multimethods) in PythonDescription to follow
Talk 3: Rodrigo Witzel // Saving memory with Python 3.8
Talk 4: Chiara Mezzavilla // Pinning dependencies with a Github bot
Hello Google Developers,
We are excited to be hosting Jeremy Wilken https://twitter.com/gnomeontherun , a Google Developer Expert on the Google Assistant, who will be in Berlin for Voice Con in December, and offered to present at our GDG chapter.
Jeremy will give an introduction to building voice apps on Google Assistant, and then will tell us about designing for Privacy with AI: A look at how to consider privacy and ethics in your AI projects.
We will update this listing as we get a bit closer to the event with details on the venue, and more details on the event schedule.
Tech Talk 1: DIY – Create your own home assistant with Watson Services and TJBot by Julian Gehrmann & Jan Plogmeier, IBM Cognitive Consultants
Scared about Alexa and Google Home? In this talk you can learn how to build your own trusted home assistant using Watson Services, RasperyPi and a card board robot. Get inspired and try it out on your own.
Tech Talk 2: Project Debater – Can AI be better at debating than humans? By Jochen Stark, IBM Executive Architect
Project Debater – an IBM research project - is the first AI system that can debate with humans on complex topics. Of course, the goal is not to win debates against humans. This technology should rather help people build persuasive arguments and make well-informed decisions. Get insight about how Project Debater works in the background and how this can help in social media discussions.
Are you interested in Blockchain or AI? Do you want to learn where it can be implemented to bring extra value to your business? Do you want to learn more about AI for Natural Language Processing (NLP)?
If for any of those questions your answer is yes then join us at the “Practical Examples of Handling Data with Blockchain & AI” Meetup in Berlin. Snacks and drinks included! :)
For who: For everybody who is interested in the practical use of blockchain or AI/ML technologies. Also for entrepreneurs and top managers who are interested in implementing those technologies to their businesses.
Talk 1: “The Rise of Natural Language Processing in Business and Industry” Presentation held by Ph.D. Adam Gonczarek + Q&A session
Talk 2: Beyond the Hype: How to assess if Blockchain can benefit your Business? Presentation held by Mateusz Fejczaruk + Q&A session
We are starting the new session for the popular 4-week AI course: Deep Learning for Developers.
This course is online live course. You can listen, watch, interact, Q&A with instructors from anywhere around the world. Miss the live session due to time zone or conflict? learn session replay at any time afterwards.
- Start date: Jan. 7, 10am PT (US pacific time, check your local time zone).
- 8 sessions, 16 hours
- 10 live lectures, 16 hands-on code labs.
- Live Sessions, Real time interaction
- Forum supports to projects and homeworks
Details: learn deep learning by building deep learning models and projects.The course takes unique project focused approach to teach you deep learning by building deep learning models. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how these models work under the hood while writing code, and building neural networks.
The course balances learning theory, working on projects, and also hearing from guest speakers who are industry practitioners in the field.
Students who take this course will be able to:
- Identify and frame problems that can be solved by deep learning
- Choose the right techniques to the problems
- Understand key deep learning concepts and how deep learning models work
- Identify and fix problems with messy datasets
- Build deep neural nets for classification and regression using the Keras framework
- Build convolutional neural networks for image classification, object localization and segmentation using the Keras
- Discuss the parts and processes involved in building large scale deep learning applications
Online AI tech talks, courses, bootcamps: https://learn.xnextcon.com
AI (Artificial Intelligence) Lösungen sind mittlerweile allgegenwärtig. Quasi jeder kennt ein Beispiel und so gut wie jeder nutzt bereits einen oder mehrere Dienste, welche auf AI Technologien basieren.
Natürlich macht das Alles neugierig. Wie mag sowas eigentlich im Kern funktionieren? Was ist das? Künstliche Intelligenz? Kann man das überhaupt noch begreifen oder sogar noch lernen? Muss man dafür ein Genie sein? Oder zumindest im Mathe Leistungskurs geglänzt haben?
Wir von der PASS Hamburg wollen Euch die Gelegenheit geben mit uns in das Thema einzusteigen. Und zwar man mal ohne jegliches Marketing usw. Natürlich werden wir mal auf einen kommerziellen Dienst verweisen, aber primär werden wir ganz pragmatisch mit freien Tools on-premises Beispiele entwickeln.
Und, wirklich versprochen, das wird kein "Hier ist ein Neutron... alles easy... hier sind zwei Neutrons... alles cool... und jetzt passiert ein Wunder und es erkennt Katzenvideos" Kram. Verstehen wird klar der Fokus sein. Und glaubt uns jetzt schon mal, teilweise ist es verblüffend einfach, was da wirklich passiert.
Was werden wir nutzen bzw. zeigen? Python Jupyter Notebooks auf Windows. Scikit-learn und Keras sind die ersten Tools, welche wir nutzen werden. Und nein, Ihr müsst keine Ahnung von Python haben. Wenn Ihr schon mal eine Programmiersprache aus der Ferne gesehen habt, dann ist das sicherlich nützlich.
Im ersten Akt wird es um Basics gehen. Keine Sorge. Wenig Theorie ohne Praxisbezug dabei. Wir sprechen über AI allgemein, Unterteilung in die einzelnen Disziplinen und wie man die "richtige Frage" stellt für seinen Anwendungsfall. Es wird primär um Machine Learning gehen und wir werden auch schon ein wenig auf Deep Learning eingehen.
Hast Du Dich auch schon gefragt, wie Du Machine Learning in Deine tägliche Arbeit als .NET Entwickler integrieren kannst?
Machine Learning und Artificial Intelligence haben einen immer größeren Einfluss auf den Alltag eines Software Entwicklers.
Lange Zeit war diese Technologie nur den Mathematikern zugänglich und fand Anwendung überwiegend in der Python, R und Matlab Community. Mit ML.NET, dem neuen .NET Core, Open Source, Cross-Platform Framework von Microsoft wird .NET Entwicklern ein einfacher Zugang zu ML und AI ermöglicht.
Damir Dobric ist Microsoft Regional Director sowie Microsoft Azure MVP und bringt jahrelange Erfahrung als Entwickler, Architekt, Trainer und Autor mit. An praktischen Beispielen zeigt Dir Damir auf Basis von verschiedenen Demos, wie Machine Learning und Artificial Intelligence sinnvoll und produktiv in .NET Anwendungen integriert werden können.