Deep learning

Deep Learning, an approach of AI, emerged from a decade’s explosive computational growth as a serious contender in the field. Thus, deep learning is a particular kind of machine learning whose algorithms are inspired by the structure and function of human brain.

Deep learning is the most powerful machine learning technique these days. It is so powerful because they learn the best way to represent the problem while learning how to solve the problem. A comparison of Deep learning and Machine learning is given below:

Data Dependency

The first point of difference is based upon the performance of DL and ML when the scale of data increases. When the data is large, deep learning algorithms perform very well.

Machine Dependency

Deep learning algorithms need high-end machines to work perfectly. On the other hand, machine learning algorithms can work on low-end machines too.

Feature Extraction

Deep learning algorithms can extract high level features and try to learn from the same too. On the other hand, an expert is required to identify most of the features extracted by machine learning.

Time of Execution

Execution time depends upon the numerous parameters used in an algorithm. Deep learning has more parameters than machine learning algorithms. Hence, the execution time of DL algorithms, specially the training time, is much more than ML algorithms. But the testing time of DL algorithms is less than

ML algorithms.

Approach to Problem Solving

Deep learning solves the problem end-to-end while machine learning uses the traditional way of solving the problem i.e. by breaking down it into parts.

In the next post we'll discuss about Convolutional neural networks.