How efficiently can we mimic biological spiking process of neurons and synapses, and is CMOS a good choice for neural networks
This article is a part of Artificial Neural Networks Serial, which you can check out here. In the previous blog posts, we covered some very interesting topics regarding Artificial Neural Networks (ANN). The basic structure of Artificial Neural Networks was presented, as well as some of the most commonly used activation functions. Nevertheless, we still haven’t mentioned
This post explores the concept of abstraction in neural networks and its importance in cognition. Abstraction is the act of ignoring irrelevant details and
Neural networks are a form of artificial intelligence that is based on the structure and function of the human brain. These networks are composed of interconnected neurons that are capable of learning and adapting to new information. In this article, we will delve into the structure and workings of neural networks, explore their various applications, and discuss how they are used in search engines
Explore in-depth the technical journey of neural networks, from the basic perceptron to advanced deep learning architectures driving AI innovations today
Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this
Neural networks are computing systems inspired by the human brain. Learn about layers, activation functions, backpropagation, and the architectures powering modern AI
Learn how neural networks work, from layers and forward propagation to output generation, with simple real-world examples
Recurrent Neural Networks (RNNs) are a type of neural network in which the results of one phase are used as inputs for the next. Traditional neural networks have inputs and outputs that are independent of one another. RNNs maintain a memory of previous inputs by utilizing a hidden layer. This enables them to capture dependencies between sequential elements, making them effective for tasks involving time-series or sequential data
Exploring neural network language models, specifically recurrent neural networks, and taking a sneak peek at how embeddings are generated