Deep Learning Approaches for Image Recognition and Natural Language Processing
Main Article Content
Abstract
Deep learning has emerged as a transformative technology in the fields of image recognition and natural language processing (NLP), enabling unprecedented levels of accuracy and efficiency. the latest deep learning approaches applied to these domains. In image recognition, convolutional neural networks (CNNs) have revolutionized the ability to detect and classify objects within images, with applications spanning from medical imaging to autonomous vehicles. We explore various architectures such as Alex Net, VGG, Res Net, and more recent innovations like Efficient Net and Vision Transformers (ViTs). For natural language processing, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer models like BERT and GPT have significantly advanced the understanding and generation of human language. This review discusses the underlying principles of these models, their training methodologies, and their performance on benchmark datasets. Additionally, we address the challenges associated with deep learning, including computational resource requirements and the need for large annotated datasets. Ethical considerations, such as bias in model predictions and data privacy, are also examined. provide researchers and practitioners with a thorough understanding of the current state of deep learning in image recognition and NLP, highlighting key advancements and identifying future research directions to overcome existing limitations and enhance the capabilities of these technologies.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (pp. 6105-6114).
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.