What's happening?

Registration through Zoom required.

PDXWIT Job Board Live: The networking event that comes to you!

Are you ready to get hired up?!? Then this virtual meetup is for you. PDXWIT’s Job Board LIVE is tailor-made to help you find the career of your dreams.

Eight companies with open roles on the PDXWIT job board will share their open positions (local and remote) during our Recruiter Roll Call. You’ll have a chance to chat with them in breakout rooms after the presentation, so bring your questions, your professional statements, and be ready to share your brilliant skills and qualifications.

In order to create a productive and thoughtful space, we are limiting capacity to the first 60 attendees.

We will have ASL and live closed captioning for this event. Please reach out to [email protected] with any additional accessibility needs you have.

Agenda: 12:00 p.m. - Event Begins 12:03 p.m. - Intros, Announcements, Lightning Talk 12:10 p.m. - Recruiter Roll Call 12:20 p.m. - Breakout Rooms (Recruiter and Networking) 1:00 p.m. - Event Ends

Mission: We are building a better tech industry by creating access, dismantling barriers and fueling belonging.

Our events are inclusive. ALL people who support our purpose are welcome.

адвокат Дніпро

Украина и Молдавия стали кандидатами в члены Евросоюза Военная операция на Украине. Онлайн 8 концертов в день: каким был шоу-бизнес в эпоху «Ласкового мая» ВЭБ заявил о готовности продать ЦСКА «качественному инвестору» США введут пошлины на азотные удобрения из России Боррель исключил транзитную блокаду Калининградской области Cisco решила уйти из России и Белоруссии Зеленский заявил об усталости тех, «кто не попадает под обстрелы» Дуров исключил передачу Google данных пользователей Telegram https://gorod.dp.ua/inf/pravo/oneplace/?place_id=20059

This free 2-day workshop (June 28 & 29th) is sponsored by Data Stack Academy, and will teach beginner-level Python programming skills. We'll also talk about how and why to start a career in data engineering, and how to learn skills on your own or find a data engineering bootcamp. Please register in advance at https://cutt.ly/dsa_events

RainSec is an informal group of like-minded security professionals who meet to network and discuss topics of interest in a non-work, non-vendor setting.

While our target audience is experienced information security professionals, this is a public event open to any interested parties. If you have a friend or colleague who might benefit, please pass an invite along.

Follow @PDXRainSec on Twitter for last minute updates.

Because COVID-19 still presents significant risk to some members of our community, we ask that you observe the following and make your own risk assessment decision regarding your attendance:

• If outdoor seating is available and weather permits, we will be outside.
• If you are feeling sick, stay home!
• If you've had a recent COVID exposure, stay home!
• If you are not vaccinated, stay home!
• If you are otherwise considered high risk for COVID, please stay home until you feel it is safe to gather in large groups (20-40 people). We don't want to be the cause of anyone's illness or death.

We miss all of you, and we look forward to seeing you as soon as it is safe for you to attend.

You can also find us on Meetup.com and Google Groups.

If you're looking for more security-focused community meetups in the Portland area, check out pdx-infosec.com.

If there is anything that is universally true in machine learning, it is that more data is always better. That’s why data scientists always respond to the “How much data do you need?” question with “How much data can you get?”

But sometimes you don’t have much data, because: a) It’s genuinely hard to get; or b) You haven’t been gathering it for very long and it takes time. This is a much more common circumstance than you might think. And you are not alone if you're experiencing this issue. Luckily, and not surprisingly, the machine learning community has come up with numerous approaches for dealing with it. Come sit in on an exclusive conversation with Dr. Tim Oates, professor and practitioner, who will share what appeals to intuition as to why the methods work and provide concrete examples of their application to simple problems.

We will discuss transfer learning, open source datasets and synthetic data, few shot learning, active learning, and semi-supervised learning. By the end of the evening, you should understand what all of these methods are, when they are applicable, and what kinds of results you can expect from using them.

Dr. Tim Oates is Chief Data Scientist at Synaptiq and an Oros Family Professor of Computer Science and Technology in the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore County. He received a Ph.D. degree from the University of Massachusetts, Amherst in 2001 with a focus on Artificial Intelligence and Machine Learning, and spent a year as a postdoc in the MIT AI Lab. He is an author or co-author of more than 150 peer reviewed papers in AI, ML, and data mining. Dr. Oates served as Chief Scientist for a big data startup in the contact management space, and has consulted in a wide variety of industries, including healthcare, construction, amusement parks, publishing, and social media.

If there is anything that is universally true in machine learning, it is that more data is always better. That’s why data scientists always respond to the “How much data do you need?” question with “How much data can you get?”

But sometimes you don’t have much data, because: a) It’s genuinely hard to get; or b) You haven’t been gathering it for very long and it takes time. This is a much more common circumstance than you might think. And you are not alone if you're experiencing this issue. Luckily, and not surprisingly, the machine learning community has come up with numerous approaches for dealing with it. Come sit in on an exclusive conversation with Dr. Tim Oates, professor and practitioner, who will share what appeals to intuition as to why the methods work and provide concrete examples of their application to simple problems.

We will discuss transfer learning, open source datasets and synthetic data, few shot learning, active learning, and semi-supervised learning. By the end of the evening, you should understand what all of these methods are, when they are applicable, and what kinds of results you can expect from using them.

Dr. Tim Oates is Chief Data Scientist at Synaptiq and an Oros Family Professor of Computer Science and Technology in the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore County. He received a Ph.D. degree from the University of Massachusetts, Amherst in 2001 with a focus on Artificial Intelligence and Machine Learning, and spent a year as a postdoc in the MIT AI Lab. He is an author or co-author of more than 150 peer reviewed papers in AI, ML, and data mining. Dr. Oates served as Chief Scientist for a big data startup in the contact management space, and has consulted in a wide variety of industries, including healthcare, construction, amusement parks, publishing, and social media.