Unlocking Complex Patterns Through the Power of Deep Learning for Advanced Data Analysis and Prediction

Supervised deep learning has brought changes to data analysis and prediction function by leveraging its capacity to make many number of relationships with heterogeneous forms of data. This paper compares deep learning techniques like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks in three tasks: There are three types of tasks, including image classification, time-series prediction, and text classification. In the picture simulation of the CIFAR-10 set for training, the accuracy of the ResNet model was 95% while the accuracy of VGG16 w 93%. When applied to the M4 competition dataset in time-series prediction, the LSTM model was superior to other benchmarks with MAE of 0.40 and RMSE of 0.60, with the RNN model giving MAE of 0.50 and RMSE of 0.65. It is 0.92 of accuracy, 0.91 of F1 score, and 0.921 for precision using the IMDB reviews dataset in text classification of movies using the CNN - LSTM hybrid model while the Bi-LSTM only had an accuracy of 0.89 and an F1 score of 0.889. Even though models, such as ResNet and Bi-LSTM, took slightly more time to train-approximately 5.0 hours-it was because the epoch models were significantly superior to the baseline model. The present work focuses on the benefits of deep learning architectures in providing interpretable patterns in numerous applications.