12 Questions to Ask Yourself When Your Machine Learning Model is Underperforming
According to our Head of Research, Kamil Szelag, PhD, data scientists often spend 80% of their time preparing and refining datasets, and only 20% on model development and tuning. Below is a practical, technical checklist designed to help you debug underperforming models and realign development efforts more effectively.
Why Hypersynthetic Data is the Future of Vision AI and Machine Learning
Hypersynthetic data is redefining vision AI training by using n-dimensional feature spaces to design custom datasets that go beyond conventional synthetic datasets. By leveraging advanced simulation engines, physics-based rendering, and feature-space modeling, SKY ENGINE AI enables highly scalable, accurate, and bias-free AI training. Learn how our Synthetic Data Cloud empowers organizations to build future-proof AI systems.
What is Hyperparameter Tuning?
The goal of hyperparameter tuning is to fine-tune the hyperparameters so that the machine can build a robust model that performs well on unknown data. Effective hyperparameter adjustment, in conjunction with excellent feature engineering, may considerably improve model performance.
Supervised Learning vs. Unsupervised Learning
Supervised learning is a machine learning approach where models are trained on labeled data, making it ideal for tasks like image classification. In contrast, unsupervised learning leverages statistical models to analyze unlabeled data, uncovering hidden patterns and structures within datasets.
Using Learning Curves to Analyse Machine Learning Model Performance
Learning curves are a common diagnostic tool in machine learning for algorithms that learn progressively from a training dataset. After each update during training, the model may be tested on the training dataset and a hold out validation dataset, and graphs of the measured performance can be constructed to display learning curves.
What is StyleGAN-T?
StyleGAN-T is a text-to-image generation model based on the architecture of the Generative Adversarial Network (GAN). GAN models were obsolete with the arrival of diffusion models into the picture generation space until StyleGAN-T was released in January 2023.
What is Dataset Distillation?
Dataset Distillation is the process of choosing a subset of data samples that capture the most essential and representative aspects of the original dataset. It's used to reduce the processing needs of the training operations while retaining critical information.
What is Mask R-CNN?
Mask R-CNN, or Mask Region-based Convolutional Neural Network, is an extension of the Faster R-CNN object detection method, which is used in computer vision for both object recognition and instance segmentation.
Autoencoders in Computer Vision
An autoencoder is a type of artificial neural network that is used to learn data encodings unsupervised. The autoencoder must examine the input and create a function capable of transforming a specific instance of that data into a meaningful representation.