Abstract
Deep learning is a new era of machine learning and belonging to the area of artificial intelligence. It has tried to mimic the working of the way the human brain does. The models of deep learning have the capability to deal with high dimensional data and perform the complicated tasks in an accurate manner with the use of graphical processing unit (GPU). Significant performance is observed to analyze images, videos, text and speech. This paper deals with the detailed comparison of various deep learning models and the area in which these various deep learning models can be applied. We also present the comparison of various deep networks of classification. The paper also describes deep learning libraries along with the platform and interface in which they can be used. The accuracy is evaluated with respect to various machine learning and deep learning models on the MNIST dataset. The evaluation shows classification on deep learning model is far better than a machine learning model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Deep Learning Research Groups. http://deeplearning.net/deep-learning-research-groups-and-labs/
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: 30th International Conference on Machine Learning, pp. 1310– 1318. JMLR.org, Atlanta, GA, USA (2013)
Abraham, A.: Artificial neural networks. In: Sydenham, Peter H., Thorn, Richard (eds.) Handbook of Measuring System Design, pp. 901–908. Wiley, London (2005)
Sutskever, I., Hinton, G., Taylor, G.: The recurrent temporal restricted Boltzmann machine. In: NIPS’2008, Curran Associates, Inc., pp. 1601–1608 (2009)
Fischer, A: Training Restricted Boltzmann Machines. KI-Künstliche Intelligenz. 29, 441–444 (2015)
Hinton, G.E., Simon, O., Yee-Whye, T.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)
Huang, H., Li, R., Yang, M., Lim, T., Ding, W.: Evaluation of vehicle interior sound quality using a continuous restricted Boltzmann machine-based DBN. Mech. Syst. Signal Process. 84, 245–267 (2017)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. Miami (2009)
Krizhevsky, A., Sutskever, I., Hinton, G., E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 110–1114 (2012)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision–ECCV 2014. LNCS, vol. 8689. Springer, Cham (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. Boston (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–-778. Las Vegas, NV (2016)
LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521, 436–444 (2015)
Graves, A.: Generating Sequences With Recurrent Neural Networks, CoRR. (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length, and Helmholtz free energy. Adv. Neural. Inf. Process. Syst. 6, 3–10 (1994)
Socher, R., Perelygin, A., Wu, J., Chuang, J., D. Manning, C., Ng, A., et al.: Recursive deep models for semantic compositionality over a sentiment Treebank. In: Conference on Empirical Methods in Natural Language Processing (2013)
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al.: Tensorflow: A system for large-scale machine learning. In: 12th USENIX Conference on Operating Systems Design and Implementation. pp. 265–283, USENIX Association, Savannah, GA, USA (2016)
Keras: The Python Deep Learning library. https://keras.io/
Deeplearning4j: Open-source, Distributed Deep Learning for the JVM. https://deeplearning4j.org/index.html
Caffe| Deep Learning Framework. http://caffe.berkeleyvision.org
Bergstra, J., Bastien, F., Breuleux, O., Lamblin, P., Pascanu, R., Delalleau, O., et al.: Theano: deep learning on gpus with python. In: Neural Information Processing Systems. Big Learn workshop (2011)
Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: a matlab-like environment for machine learning. In: Neural Information Processing Systems. Big Learn workshop (2011)
Agarwal, A., Akchurin, E., Basoglu, C., Chen, G., Cyphers, S., Droppo, J., et al.: An Introduction to Computational Networks and the Computational Network Toolkit. Microsoft Technical Report MSR-TR-2014-112 (2014)
The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist
NIST Handprinted Forms and Characters Database. https://www.nist.gov/srd/nist-special-database-19
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Garg, D., Goel, P., Kandaswamy, G., Ganatra, A., Kotecha, K. (2019). A Roadmap to Deep Learning: A State-of-the-Art Step Towards Machine Learning. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 955. Springer, Singapore. https://doi.org/10.1007/978-981-13-3140-4_15
Download citation
DOI: https://doi.org/10.1007/978-981-13-3140-4_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3139-8
Online ISBN: 978-981-13-3140-4
eBook Packages: Computer ScienceComputer Science (R0)