Discuss the advances in Neural Networks
Neural networks fall under the subset category of deep learning and in the class of artificial intelligence. Every day, huge amounts of data are being collected on a daily basis, and this means it is now easy to train the models in deep learning with higher accuracy given that more data increases the chance of accuracy in models. Neural networks are developed to operate the same as the human brain. However, given the speed and the accuracy comparison of the machine and the human brain, it can be concluded that the human brain is more complex, possess high parallel computing power as well as it is nonlinear (Sarangapani, 2018). The human brain is capable of recognizing objects incompatibly quickly in comparison to the fastest computer in the world. However, there have been numerous advancements aimed at achieving the level of processing power the brain has in artificial intelligence.
How ANN, CNN work to classify image datasets more effectively.
CNN, as well as ANN, uses the visual cortex for the classification of images. CNN, from its in invention in 1988, has been put into practical use. For instance, it is applied in amazon, Facebook, among other large sites for product recommendation (Baker, Gupta, Naik & Raskar, 2016). For CNN to recognize an image and classify it correctly, it first sees it as a set of pixels, where the pixel values range from 0 to 255. Then the computer checks for base-level characteristics or the curvature boundaries to identify the image effectively.
References
Baker, B., Gupta, O., Naik, N., & Raskar, R. (2016). Designing neural network architectures using reinforcement learning. arXiv preprint arXiv:1611.02167.
Sarangapani, J. (2018). Neural network control of nonlinear discrete-time systems. CRC press.