Gated Recurrent Units (GRUs) are a type of recurrent neural network (RNN) architecture designed to address some of the limitations of traditional RNNs, particularly in handling sequential data. Introduced by Kyunghyun Cho and his colleagues in 2014, GRUs have gained popularity in various applications within artificial intelligence (AI), especially in natural language processing, time series Read More …
Tag: Neural Networks
Artificial Neural Networks
Imagine trying to teach a computer to recognize a cat in a picture. You could try to program specific rules – it has whiskers, pointy ears, a tail, etc. But what about a cat curled up in a ball? Or a blurry photo? Rule-based systems struggle with such variations. Artificial Neural Networks (ANNs) offer a Read More …
RNN – Recurrent Neural Networks
In the realm of Artificial Intelligence, many tasks involve understanding data that unfolds over time or has a sequential structure. Think of comprehending spoken language, predicting stock prices, or even generating music. Traditional neural networks, designed to process independent inputs, often fall short in these scenarios. This is where Recurrent Neural Networks (RNNs) come into Read More …
CNN – Convolutional Neural Networks
Convolutional Neural Networks (CNNs) were inspired by the way the human visual cortex works, CNNs are a specialized type of neural network particularly adept at analyzing and understanding images. They are the engine behind many impressive AI applications, from recognizing faces in photos to powering autonomous vehicles. Traditional neural networks, while powerful, can struggle with Read More …
Neural Networks
Alright, let’s trace the fascinating journey of Neural Networks within the broader history of Artificial Intelligence. For someone new to AI, understanding this evolution is key to grasping where we are today and where the field might be headed. As we discussed earlier, the early days of AI in the mid-20th century were dominated by Read More …