What we're about

"Learn AI/ML by Practice". Join us to learn and practice AI, Machine learning, Deep learning and Data Science technology together with like-minded developers.

Due to the Covid-19, we have moved all our meetup events online: https://learn.xnextcon.com/event/eventsbypage/1

Our goal is to congregate with AI enthusiasts from all over London to learn and practice AI tech, through tech talks, workshops, bootcamps, hackathons, ML Summit etc.. we regularly invite tech leads from innovated companies, successful startups to share their practice experiences and practices in the world of AI, Machine Learning, Cloud, Data.

If you’d like to speak at future meetups, co-promote your meetup or inquire about partnership opportunities, please feel free to reach out to us.

===Tentative Agenda =====

5:30pm - 5:50pm, Snack & social
5:50pm - 6:00pm, Intro/announcement
6:00pm - 7:00pm, Tech Talk 1 and Q&A
7:00pm - 8:00pm, Tech Talk 2 and Q&A
8:00pm - 8:30pm, Mingle

**Learn applied AI tech online with 100,000+ developers globally, via webinars, workshops, courses, bootcamps: https://learn.xnextcon.com
** AI Developer Conference (Seattle, San Francisco, New York, Beijing, London) : http://www.xnextcon.com

Upcoming events (4)

AWS AI/ML Bootcamp: Build ML Pipeline with NLP, TensorFlow, SageMaker

**this is online event, make sure to register and attend here: https://learn.xnextcon.com/event/eventdetails/W20091909 Free virtual AWS AI/ML bootcamp. You can join from anywhere with zoom. This full day (6hours) hands-on bootcamp is for developers of all skill level to come together to learn deep learning on NLP using Tensorflow with Amazon Sagemaker. Get free deep learning training. Together we will work through several deep learning labs, build an end-to-end AI/ML pipeline for natural language processing with Amazon SageMaker. You will get hands-on experience with the deep learning, NLP, BERT, Tensorflow and Sagemaker. Every attendee will receive a free AWS instance for this bootcamp The bootcamp includes 6 modules: 1) Ingest, analyze, and visualize a public dataset 2) Transform the raw dataset into machine learning features 3) Train a model with our features 4) Optimize model training using hyper-parameter tuning 5) Deploy and test our model both online (real-time) and offline (batch) 6) Automate the entire process with a SageMaker pipeline Agenda: [30 mins] Setup [30 mins] Ingest Data [30 mins] Explore Data [15 mins] Q&A / Break [30 mins] Prepare Data [30 mins] Train Model [30 mins] Q&A / Meal Break [30 mins] Optimize Model [30 mins] Deploy Model [30 mins] Create Pipeline [15 mins] Q&A / Wrap Up Attendees will learn how to: * Ingest data into S3 using Amazon Athena and the Parquet data format Visualize data with pandas, matplotlib on SageMaker notebooks and AWS Data Wrangler * Analyze data with the Deequ library, Apache Spark, and SageMaker Processing Jobs * Perform feature engineering on a raw dataset using Scikit-Learn and SageMaker Processing Jobs * Train a custom BERT model using TensorFlow, Keras, and SageMaker Training Jobs * Find the best hyper-parameters using SageMaker Hyper-Parameter Optimization Service (HPO) * Deploy a model to a REST Inference Endpoint using SageMaker Endpoints * Perform batch inference on a model using SageMaker Batch Transformations * Automate the entire process using StepFunctions, EventBridge, and S3 Triggers Pre-requisites: * Modern browser - and that is it! * Nothing will be installed on your local laptop

IBM AI Series #7: Workshop Explanations- Python Workflows for AI

This is online tech event, you can join from anywhere with zoom, please register and attend: https://learn.xnextcon.com/event/eventdetails/W20092110 Welcome to the "AI Trust, Bias and Explainability" learning series, by IBM AI. In collaboration with IBM team, we host a series of practical introductory sessions to AI trust, bias and explainability. This is the 7th session: In this workshop, we will talk on the typical data science workflow with a focus on explainability. It focuses on skills and tactics used to help data scientists articulate their findings to end-users, stakeholders, and other data scientists. From data ingestion, cleaning and feature selection, and ultimately model selection, explainability can be incorporated into a data scientists workflow. Using a combination of semi-automated and open source software, This workshop will expand and go deeper on the previous webinar, and walks you through an explainable workflow. Social networking with speakers, attendees 30mins before/after the event on slack. Join slack by the invitation: https://bit.ly/3gi7bjf . The two channels: #jobs for job posting from speakers, partners, sponsors companies, and you can Q&A with hiring managers right in the channel. #events for events Q&A, mixing and networking with speakers and other peer attendees. All sessions of the series: * Session 1, Jul 27th - AI Security Privacy-Preserving Machine Learning by IBM AI. * Session 2, Aug 10th - Explainable AI Workflows using Python. * Session 3, Aug 17th - Understanding and Removing Unfair Bias in ML. * Session 4, Aug 24th - Adversarial Robustness 360 Toolbox For ML. * Session 5, Aug 31st - Workshop: Explainable AI Workflows. * Session 6, Sep 9th - Workshop: Explainable AI Workflows.

Online AI Talk: Insights on Data Challenge in Deep Learning Projects

This is online AI tech talk event, you can join from anywhere with zoom, please register and attend here: https://learn.xnextcon.com/event/eventdetails/W20092310 Abstract: Data is the most precious resource of deep learning research. As such, it should be handled carefully, from data gathering, data annotation, data QA and data versioning. However, even if you managed to perform all the above tasks in the best possible way, data holds challenges that can dramatically affect your performance. In this talk, we discuss the fact that your data is most likely biased and that it affects the performance of your model. We will show how to identify data bias and what can be done to address it. Particularly, we focus on class imbalance. We provide illustrative experiments to accompany these ideas. Our experiments focus on an object detection task, which have additional complexities beyond vanilla classification tasks. We explore how different data balancing methods (data resampling and loss reweighting) affect the performance of minority and majority classes in such settings. In addition we will peek into the diminishing effect of annotated data. Deep learning models are notorious for their endless appetite for training data. The process of acquiring high quality annotated data consumes a relatively large amount of resources. Monitoring the diminishing effect provides a way to assess how much data is needed for the different stages of the project lifecycle and even predicting whether the current model architecture will be able to achieve the target metric. This knowledge effectively provides a tool for optimal management of time, manpower, and computing resources. Finally, we will discuss the features needed for a dataset management tool that can help identify and tackle the data challenge in your deep learning projects. We will demonstrate the effectiveness of using such a tool on popular computer vision tasks. Social networking with speakers, attendees 30mins before/after the event on slack. Join slack by the invitation: https://bit.ly/3gi7bjf . The two channels: #jobs for job posting from speakers, partners, sponsors companies, and you can Q&A with hiring managers right in the channel. #events for events Q&A, mixing and networking with speakers and other peer attendees.

4-weeks AI course: Practical Python for Machine Learning (Batch #2)

This is paid online course/training (using zoom), please go to the website to pay and enroll. https://learn.xnextcon.com/course/coursedetails/C20092816 This course is online live course. You can listen, watch, interact, Q&A with instructors from anywhere around the world. You work with peer devs on projects. If you miss the live session due to time zone or conflict, you can learn by watching session replay any time and live support on slack. Start date: 28 September, 4pm PT, Every Mon/Wed (US pacific time, check your local time zone). * 4 Weeks / 8 Sessions / 16 hours * 8 lectures / 8 coding exercises * Live Sessions, Real time interaction * Capstone project, Peer students collaboration * Slack supports to projects and homework Enrollment: https://learn.xnextcon.com/course/coursedetails/C20092816 Details: This course covers the key Python skills you will need so you can start using Python for machine learning. The course is ideal for: * Those with some previous coding experience who wants to add Python to their repertoire or level up their basic Python skills. * Aspiring programmers who are learning their first programming language In this course you will learn the fundamentals of Python primarily through a series of coding exercises guided by the instructor. Students will learn about the foundational underpinnings of Python as well as how to put that knowledge to the test with practical exercises. The course takes project-focused approach to teach you Python by building projects. 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 they work under the hood while writing code

Past events (107)

(Virtual) Google Developers AI/ML Devfest 2020

Online event

Photos (94)