Study and Innovative Approach of Deep Learning Algorithms and Architecture

Abstract

Deep learning is becoming increasingly important in our everyday lives. It has already made a big difference in industries like cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition, to name a few. Traditional learning, classification, and pattern recognition methods necessitate feature extractors that aren't scalable for large datasets. Depending on the issue complexity, deep learning can often overcome the limitations of past shallow networks that hampered fast training and abstractions of hierarchical representations of multi-dimensional training data. Deep learning techniques have been applied successfully to vegetable infection by plant disease, demonstrating their suitability for the agriculture sector. The chapter looks at a few optimization approaches for increasing training accuracy and decreasing training time. The authors delve into the mathematics that underpin recent deep network training methods. Current faults, improvements, and implementations are discussed. The authors explore various popular deep learning architecture and their real-world uses in this chapter. Deep learning algorithms are increasingly being used in place of traditional techniques in many machine vision applications. Benefits include avoiding the requirement for specific handcrafted feature extractors and maintaining the integrity of the output. Additionally, they frequently grow better. The review discusses deep convolutional networks, deep residual networks, recurrent neural networks, reinforcement learning, variational autoencoders, and other deep architectures.