How Deep Learning Mimics the Brain in Processing Data

Muneeb Chawla is an Aurora, Colorado-based IT professional with experience in technical areas such as natural language processing and machine learning. Among the areas of the latter domain in which Muneeb Chawla has in-depth knowledge is deep learning. This focuses on algorithms that reflect artificial neural networks, which are based on the function and structure of the human brain.

The advantage of a brain simulation approach is that it improves and simplifies the use of learning algorithms, and also represents a viable pathway toward real artificial intelligence (AI). Another important aspect of such systems is scale, with larger neural networks able to be trained with huge data loads in ways that boost performance. This avoids the “performance plateau” common in other types of machine learning.

Beyond scalability, deep learning models offer automatic feature extraction (feature learning), which uses a “hierarchy of concepts approach.” This enables the computer program to drill down and build on simple concepts when learning complex concepts. With computing power steadily increasing and ever larger datasets available, neural networks have the potential to achieve real-time object and sound recognition, which in turn is used to increase the performance of many everyday tasks.

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