Google AI Releases Method To Determine Neural Network Learning Sequence
A method called Task Affinity Groupings (TAG) has been proposed by Google AI that determines which tasks should be trained together in multi-task neural networks.
Top Resources To Learn NLP For Free
These resources teach you NLP from beginner level to advanced concepts
Introduction to Probabilistic Neural Networks For Beginners
A probabilistic neural network (PNN) is a sort of feedforward neural network used to handle classification and pattern recognition problems.
A Beginner’s Guide to Extreme Learning Machine
From this post you will learn how to boost the performance of Feed-Forward Neural Network.
Intel Reveals Neuromorphic Research Chip Loihi 2
Loihi 2 addresses a practical limitation of Loihi by incorporating faster, more flexible, and more standard input-output interfaces.
MIT Releases New Framework For Machines To Work As Radiologist
In order to improve machine learning algorithms’ interpretive abilities, scientists explore underused radiology reports that accompany medical images.
Exploring Graph Neural Networks
Data Scientists at CRED, Ravi Kumar and Samiran Roy explained the essence of using graph neural networks and how the emerging technology is being utilised by CRED.
Zestimate Algorithm: Invasion of AI on Real Estates
Incorporating artificial intelligence into several stages of the mortgage process.
Guide To VOLO: Vision Outlooker For Visual Recognition
VOLO is a simple yet powerful CNN model architecture used for visual recognition and helps achieve fine-level token representation.
On-Device Speech Representation Using TensorFlow Lite
Representation learning is a machine learning (ML) method that trains a model to discover prominent features. It may apply to a wide range of downstream tasks– including Natural Language Processing (BERT and ALBERT) and picture analysis and classification (Inception layers and SimCLR). Last year, researchers developed a baseline for comparing speech representations and a new, […]
Guide to Feed-Forward Network using Pytorch with MNIST Dataset
Neural Networks are a series of algorithms that imitate the operations of a human brain to understand the relationships present in vast amounts of data. Each “neuron” present in a neural network can be defined as a mathematical function that collects and classifies information according to the specific architecture. The network, in general, comprises interconnected […]
Using GANs For High-Resolution Cosmology Simulations
The Big Bang theory posits our Universe is ever-expanding. Edward Hubble has confirmed the Expanding Universe hypothesis through analysis of galactic redshifts. Currently, our Universe is about 93 billion light-years in diameter. Recently, researchers from Carnegie Mellon University and the University of California have developed a way to create a complex simulated universe in under […]
