ML practitioners wrangle data to create the best training datasets possible for their models. This generally means that the data is unbiased, well-structured, and will help to train a model that gives good predictive performance in the real world. Whether you collect data yourself from the real world, use an
Machine learning practitioners are increasingly turning to the power of generative adversarial networks (GANs) for image processing. Applications that really benefit from using GANs include: generating art and photos from text-based descriptions, upscaling images, transferring images across domains (e.g., changing day time scenes to night time), and many others.
Ever wondered how PerceptiLabs' visual approach compares to writing TensorFlow code? Recently, Robert Lundberg, CTO and Co-Founder of PerceptiLabs, gave a live coding demonstration to show just that, using TensorFlow’s flower classification model. TensorFlow, the most popular machine learning (ML) framework today, provides a tutorial on how to classify
Machine learning (ML) modeling is challenging – we know from experience! From wrangling data to choosing an appropriate ML algorithm, and then debugging and iterating on it, it can be a daunting task to create or update your model. Solving these types of issues is what fueled us to create PerceptiLabs,
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 models for image classification often use convolutional neural networks (CNNs) to extract features from images while employing max-pooling layers to reduce dimensionality. The goal is to extract increasingly higher-level features from regions of the image, to ultimately make some kind of prediction such as an image classification. To