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Autoencoders are a class of artificial neural networks used in artificial intelligence (AI) and machine learning (ML) for various purposes, including dimensionality reduction, feature learning, data compression, and generative modeling. They belong to the family of unsupervised learning algorithms and are designed to encode input data into a lower-dimensional representation
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A Recursive Neural Network (RecNN) is a type of neural network architecture used in artificial intelligence (AI) and machine learning (ML) for tasks involving hierarchical or tree-structured data. Unlike traditional feedforward or recurrent neural networks that operate on sequential or grid-like data, RecNNs are designed to process and analyze data
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A Recurrent Neural Network (RNN) is a type of artificial neural network designed for processing sequences of data. Unlike feedforward neural networks, where information flows in one direction from input to output, RNNs have connections that loop back on themselves, allowing them to maintain a hidden state or memory of
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A Convolutional Neural Network (CNN) is a class of deep neural networks designed primarily for processing and analyzing visual data. CNNs are a foundational technology in the fields of artificial intelligence (AI) and machine learning (ML), particularly in computer vision applications. They are highly effective at tasks like image recognition,
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Transformers are a type of architecture in artificial intelligence (AI) and machine learning (ML) that has had a significant impact on various natural language processing (NLP) tasks and other sequence-to-sequence tasks. They were introduced in a 2017 paper titled "Attention Is All You Need" by Vaswani et al.
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A Feedforward Neural Network (FNN), also known as a feedforward neural network model or simply a feedforward network, is a fundamental type of artificial neural network used in machine learning and artificial intelligence. It represents the simplest form of a neural network, where information flows in one direction: from the
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Recent innovations like LaMDA, PaLM and GPTs are based on Large language models (LLMs) or Transformer-based language models (TLMs) and developed by Google and OpenAI. LaMDA: LaMDA means “Language Model for Dialogue Applications”. LaMDA (Language Model for Dialogue Applications) is a family of conversational large language models (LLMs) developed by
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Neural networks backpropagation (short for "backward propagation of errors") is a supervised learning algorithm commonly used for training artificial neural networks, a type of machine learning model inspired by the human brain. Backpropagation is the process by which neural networks update their weights and biases during training to
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Generative Artificial Intelligence (also Generative AI or Gen AI) is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models advanced in transformer-based deep neural networks enabled a number of generative AI systems notable for accepting natural language prompts as input. Recent successful outcomes
Inside AI
Artificial Intelligence (AI) can be categorized into several types based on various criteria. Here are some common ways to classify AI: Based on Capabilities: 1. Narrow or Weak AI (ANI): This type of AI is designed for a specific task and can't perform tasks beyond its programmed capabilities.
Inside AI
Deep learning is a subfield of artificial intelligence (AI) and machine learning (ML) that focuses on the development and application of neural networks, which are computational models inspired by the structure and function of the human brain. It is particularly well-suited for tasks involving large amounts of data, such as
Inside AI
Reinforcement Learning (RL) is a type of machine learning within the field of artificial intelligence (AI) that focuses on training agents to make a sequence of decisions in an environment in order to achieve a specific goal. It is a learning paradigm inspired by behavioral psychology, where agents learn by