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Abstract 

With the fast growth in data and technology, there is a need for smart learning models that can find patterns from large and complex datasets. In Artificial Intelligence, deep learning has become the basic approach; it enables machines to learn from very large amount of data through multifaceted neural network architectures. This paper provides a complete study of important deep learning algorithmic frameworks which includes Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Generative Adversarial Networks (GANs). The first section introduces the fundamental concept of deep learning, followed by an in-depth exploration of architectures and working mechanism of each framework. Technical Assessment and Comparative analysis has been conducted based on factors like architectural complexity, computational cost, training stability, scalability, performance metrics, and applicability to various tasks. The study highlights the strengths and weaknesses associated with each framework.

The work done in this paper aims to guide the researchers and practitioners in selecting the best suitable frameworks according to their use-case requirements. Keywords: Deep Learning, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Generative Adversarial Networks (GANs).