Deep learning is a subset of AI that focuses on making robots learn like humans do – by experience. Deep learning algorithms analyze data as the human brain does. Then, of course, humans feed the device the data training, colossal knowledge base, and pattern recognition techniques to work on later.

Why Deep Learning?

Deep learning makes tasks like driving, printing, text recognition, and natural language processing more accurate. Computer vision, which classifies objects in images, outperformed humans. For example, humans perceive images, text, and speech using their sensory organs (eyes, ears, etc.) and a neural network in their brain. Millions of neurons send and receive information in this way. Artificial neural networks also pass data through multiple interconnected neuron (nodes) layers. Furthermore, deep learning typically refers to neural networks with more than three layers (they can have 100+ layers).

Although professor Rina Dechter coined the term "Deep learning" in 1986, it has recently become a buzzword due to businesses' increasing demand for faster and more accurate services.

Voice commands to phones or laptops, driverless cars, face recognition, text translations, and sentiment analysis are examples of deep learning replacing manual work. Furthermore, deep learning is a part of the AlphaGo project, which defeated the current world champion in the Go game. 

AI vs machine learning vs deep learning

AI:  From a business standpoint, AI refers to any functional data product capable of solving problems in the same way that humans do.

Machine learning: Machine learning uses various algorithms to train computer systems to make accurate predictions and decisions when presented with new data.

Deep learning: Deep learning makes predictions based on large amounts of processed data. Moreover, it enables computers to comprehend human speech and recognize and move objects in images.

Image source: AI future of world

What challenges does deep learning have in future?

Massive datasets: 

Massive datasets are a daily challenge in deep learning—vast amounts of data scattered around the market with no discernible pattern. As a result, finding diverse example datasets becomes difficult. 

Overfitting: 

Overfitting occurs when data analysis focuses too closely on the dataset. Minor parameters can skew the results. Moreover, focusing on the wrong traits can also skew results. Correct data sets can help.

Privacy breach: 

Data privacy has gained popularity recently. The FTC recently fined Facebook $5 billion for privacy violations. Deep learning uses large datasets to achieve high accuracy. However, due to privacy laws and restrictions, deep learning cannot access critical data.

Butterfly effect: 

Deep learning can produce inaccurate results if the input data is slightly changed. It makes any algorithm unreliable for critical tasks. Hackers can corrupt data by adding undetectable "noise" to it.

Some of the applications of deep learning

• Computer vision and image recognition
• Speech recognition
• Sentiment analysis
• Personalization and recommendation engines 
• Fraud detection

Conclusion

The use of deep learning will increase in the future to enhance the performance of systems. In addition, we can expect exponential growth in deep learning models, allowing us to create new applications that free our brains from repetitive manual tasks. Moreover, deep learning eliminates the need to retrain neural networks. Instead, you could use transfer learning.