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.
How to Split Dataset in Machine Learning?
To prevent overfitting and to correctly evaluate your model, divide your data into train, validation, and test batches
What is EfficientNet?
First introduced in 2019 by a team of Google AI researchers, quickly became a go-to architecture for many difficult tasks
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.
The Virtuous Cycle of Synthetic Data in AI-powered Products
The virtuous cycle of data needs to be expanded by new modalities including synthetic data to further enhance product development and customers willingness to share more data.
Adaptive Construction Intelligence with Deep Learning in the Metaverse Using SKY ENGINE AI Platform to Improve On-site Predictive Analytics
The SKY ENGINE AI Platform facilitates development of computer vision and AI models for construction site safety monitoring, inspection, and predictive analytics to optimize operations and prevent incidents.
A Comprehensive Strategy For Computer Vision By Combining Data-Centric And Model-Based Approaches With High Quality Synthetic Datasets
In this article, you'll discover how to think about your machine learning models from a data-centric standpoint, stressing the relevance and value of data in the AI models creation process.
Unlocking Accurate Warehousing And Inventorying Solutions With AI Training In The Metaverse And Synthetically Generated Data
In this article, you will discover how SKY ENGINE AI’s platform with synthetic data generation tools and AI models training in virtual environment enable designing of computer vision systems for warehousing whereas mitigating bottlenecks and issues with inventorying accuracy.
Advancing AI Sports Analytics Through Combined Power of a Synthetic Data-Driven SKY ENGINE AI Platform and NVIDIA RTX
Developing AI solutions for sports analytics is challenging, but advanced AI algorithms can analyze detailed behavioral patterns in team-based sports, providing coaches with insights that are difficult to observe manually.