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Neuralnetworks come in various forms, each designed for specific tasks: Feedforward NeuralNetworks (FNNs) : The simplest type, where connections between nodes do not form cycles. Models such as Long Short-Term Memory (LSTM) networks and Transformers (e.g., Data moves in one direction—from input to output.
Deep Learning, a subfield of ML, gained attention with the development of deep neuralnetworks. Moreover, Deep Learning models, particularly convolutionalneuralnetworks (CNNs) and recurrent neuralnetworks (RNNs), achieved remarkable breakthroughs in image classification, natural language processing, and other domains.
With advancements in machine learning (ML) and deep learning (DL), AI has begun to significantly influence financial operations. Arguably, one of the most pivotal breakthroughs is the application of ConvolutionalNeuralNetworks (CNNs) to financial processes. 1: Fraud Detection and Prevention No.2:
Here are some of the key applications of Deep Learning in healthcare: Medical Imaging Deep Learning algorithms, particularly convolutionalneuralnetworks (CNNs), excel at analysing medical images like X-rays, CT scans, and MRIs. This data can be used to detect early signs of health issues and provide personalised interventions.
On the other hand, the generative AI task is to create new data points that look like the existing ones. Discriminative models include a wide range of models, like ConvolutionalNeuralNetworks (CNNs), Deep NeuralNetworks (DNNs), Support Vector Machines (SVMs), or even simpler models like random forests.
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