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.
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 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.
What is Transfer Learning?
Assume you have an issue you want to tackle with computer vision but just a few images to base your new model on. What are your options?
Zero-shot learning in Computer Vision/Vision AI
Zero-shot learning (ZSL) is a machine learning technique that enables a model to categorise items from previously unseen classes without getting any explicit training for those classes.
What is a neural network?
The development of neural networks is an active subject of study, as academics and businesses attempt to find more efficient ways to handle complicated problems using machine learning.
What is Knowledge Distillation?
Deep neural networks have grown in popularity for a variety of applications ranging from recognising items in images using object detection models to creating language using GPT models. Deep learning models, on the other hand, are frequently huge and computationally costly, making them challenging to deploy on resource-constrained devices like mobile phones or embedded systems. Knowledge distillation solves this issue by condensing a huge, complicated neural network into a smaller, simpler one while retaining its performance.
Accelerating Model Development and AI Training with Synthetic Data, SKY ENGINE AI Platform for 5G RAN
One way to bridge the data gap and accelerate model training is by using synthetic data instead of real data for training. SKY ENGINE AI provides a platform to move deep learning to virtual reality. It is possible to generate synthetic data using simulations where the synthetic images come with the annotation that can be used directly in training AI models.