Pix2Pix for GANs, Reinforcement Learning with PPO, Types of Missing Data, and Jobs
GANs ‒ A Brief Theory and Practice, and Image-to-Image Conversion with Pix2Pix
Learn more about how you can use Pix2Pix for GANs to create brand new images based on an already existing image.
Reinforcement Learning with PPO
Get started with reinforcement learning with PPO, aka Proximal Policy Optimization, which allows experiments to be safe and cheap to perform.
What are the Types of Missing Data?
Let’s learn more about the three types of missing data — MCAR, MAR, and MNAR — and how you can address the issue easily and effectively.
Machine Learning With Graphs: Going Beyond Tabular Data
Have you considered looking beyond just tabular data, and instead looking at the relationships between data points for machine learning with graphs?
When to Use Survival Analysis: Applications in Industry Data Science
There are plenty of reasons to use survival analysis, such as with loan repayments, inventory management, human resources, and so on.
How to Bridge Data Science and Business Intelligence
Thurs, Sept 30th, 2:00 PM ET
Join us for a webinar panel discussion on data strategies for eliminating the silos between BI and data science teams using a semantic layer. Our featured speakers will share practical guidance and examples from how they build data infrastructures and architect a strategy to support both BI and data science programs.
4 Must-Know Gray Areas of Data Privacy and Ownership
As more organizations look to capitalize on new information for business use questions of data privacy arise and may lead to roadblocks.
The Crisis of Bad Data Science and What We Can Do to Avert it
There’s a problem with bad data science research, but luckily, there are ways that we can still address it moving forward.
6 Ways to Monetize Virtual Events
Learn how you can monetize virtual events to generate more revenue from your products or services and ensure a more satisfying online experience for attendees.
Kickstart your data science career with an immersive learning experience that starts the moment you register with on-demand, pre-conference training.
Ai+ Highlight of the Week: Advanced Fraud Modeling: Aric LaBarr, PhD | Associate Professor of Analytics | Institute for Advanced Analytics at NC State University
Check out the first 20 minutes of on-demand training to learn how to use an insurance fraud data set to solidify core concepts, use network analysis to create good features for fraud models, and more
Featured Jobs from Hiring Partners:
- Project Manager, AI Solutions Factory
- Senior Software Engineer, Machine Learning
- Principal Data Engineer, Senior Manager (Remote)
- Senior Data Engineer
- Sr. Data Engineer
- Search Engineer
- Senior Machine Learning Engineer
- Principal Data Engineer, Senior Manager (Remote)
Upcoming Webinars:
Five ways to Increase your Model Performance using PyTorch Profiler
Tuesday, September 21st, 3:00 PM — 4:00 PM EDT
In this session, we will go over the new PyTorch Profiler release features and how you can start leveraging this performance tool.
Embracing Extensible MLOps — Solving “Chicken or the Egg”
Wednesday, September 22nd, 1:00 PM — 2:00 PM EDT
This webinar introduces “Extensible MLOps,” enabling an internal solution with solid open-source foundations and a “lean” stack.
The Rapid Evolution of the Canonical Stack for Machine Learning
Wednesday, September 29th, 1:00 PM — 2:00 PM EDT
Join us in this webinar as we cover: What are the components for true MLOps; how do teams begin their journey into AI and Machine Learning; and why teams should take a data-first approach to ML.
Unsupervised Text Generation and its Application to News Interfaces
Thursday, September 30th, 12:00 PM — 1:00 PM EDT
In this talk, we’ll develop novel text generation methods that balance the goals of fluency, consistency, and relevancy without requiring any training data.
How do I trust the ML predictions? An introduction to ML interpretability
Friday, October 1st, 8:30 AM — 9:30 AM EDT
In this webinar, we will discuss the local and global interpretation of machine learning models and how we can explain the predictions.
