Transparency An Overview of TensorFlow and how PerceptiLabs Makes it Easier In A New Visual Approach to Machine Learning Modeling, we talked about how TensorFlow is one of the most popular machine learning (ML) framework today, but it’s not necessarily an easy one for beginners to start building ML models. That's why we decided to create a GUI on top
Transparency A New Visual Approach to Machine Learning Modeling Machine learning (ML) tools are exploding and specializing, giving users the option to build and manage their ML models in different ways ranging from writing code, relying on frameworks to using automated solutions, each with their pros and cons. The good news is, PerceptiLabs has developed a next-generation ML tool
Machine Learning Venture Beat: Getting inside the head of a machine learning scientist We were excited to be featured in Venture Beat, in their Article titled: Getting inside the head of a machine learning scientist. Check it out to see how PerceptiLabs allows you to visualize what data scientists see when they are building a machine learning model. https://venturebeat.com/2020/10/
Machine Learning Four Common Types of Neural Network Layers (and When to use Them) Neural networks (NN) are the backbone of many of today's machine learning (ML) models, loosely mimicking the neurons of the human brain to recognize patterns from input data. As a result, numerous types of neural network topologies have been designed over the years, built using different types of neural network
Model Management MLOps: An “Ops” Just for Machine Learning If you’ve been around software development for a while, you’ve undoubtedly come across numerous terms appended with the word “Ops”, such as “DevOps”, “TestOps”, or “DataOps”. Of these, “DevOps” (short for “development and information-technology operations”) is probably the most well-known. It refers to a set of software development
Transparency The Importance of Transparency in Machine Learning Models Digital systems are only useful if they can be trusted to do their job. With traditional deterministic systems, we could derive a degree of certainty that a system was working correctly through processes such as unit tests, end-user testing, code reviews, and design documentation. AI is inherently non-deterministic in nature,
