Data Dependency: Deep learning requires large amounts of labeled data to perform well. In domains where data is scarce or expensive to obtain, deep learning may not be the best solution.

The time spent in data pre-processing is minimum while you could try different deep recognition patterns, and learning techniques on the real-world data. The size of the dataset if nearly 50 MB. 2.

Deep learning's availability of large data and compute power makes it far better than any of the classical machine learning algorithms.

Module 4 | Deep Learning on Sequential Data This module will teach you another neural network called recurrent neural networks (RNNs) to handle sequential data. So far, we have covered feed-forward ...