Why Deep Learning is better than Machine Learning techniques?

Mar 28, 2019 by

In the current age, Artificial Intelligence is at its booming stage. Out of the blue, it has become the new buzz word. Seeing what is happening under the hood is quite overwhelming. But, it has two popular concepts under it- Machine Learning and Deep Learning. Recently, Deep Learning has taken over the machine learning in this department- all because of the accuracy level.

As every company is now creating models i.e. training the machines to predict and do all the stuff? Machine Learning has been helping a lot in achieving that. With some algorithms to parse data and learn from them to make decisions, this is how everything is going on. From Netflix recommendations to recognizing the friend’s photo in your Facebook profile picture, it is all because of the machine learning tools and to learn these techniques from the books and you can buy updated books on these techniques by using AliExpress Coupons India.

But, the training machines is a complex and tedious task and also, it needs a lot of domain expertise. To make it little easy going, AI designers have shifted to deep learning. But, what is Deep Learning?

Deep Learning is nothing but a subset of Machine Learning which is more accurate and flexible with each concept nested to other and relationships maintained. Also, the abstract representations computed in terms of less abstract ones.

Diving deep into it, a deep learning technique shifts from low level to high level. It is just like going up from stairs- incrementing one level up. So, here the technique goes through hidden layer architecture and learns from low-level categories and goes up to high-level categories. For example: in case of image recognition, it will identify light and dark areas first, then lines and in the end, the shape to allow facial recognition. Each node in the network carries one aspect of the whole image and altogether the nodes represent the whole image. Also, every node is given a weight that represents the connection and strength of its relationship with the output.

Deep Learning Features

The biggest advantage of deep learning is that its accuracy and the amount of data it can handle. The large chunk of data can be trained in the deep learning technique which will further provide new innovations. As per one of the leaders of the Google Brain Project, Andrew Ng, “The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.”

On the contrary side, Deep Learning requires high-end machines than Machine Learning as the GPU plays a significant role in any Deep Learning model.

To reduce the complexity of the data, most of the work had to be done by the domain expert in the machine learning techniques. But, in the deep learning algorithms, it all goes in incremental order to reach and learn the high-end features.

As per Jeff Dean, Google Senior Fellow and SVP, Google AI, “Deep neural networks are responsible for some of the greatest advances in modern computer science, helping make substantial progress on long-standing problems in computer vision, speech recognition, and natural language understanding.”

Also, the problem solving approach in Deep Learning is end to end whereas, in Machine Learning techniques, the problems are broken down in different parts and then aggregated again to reach the final product. In deep learning techniques like Yolo net, the image is taken as output with location provided, then we get the name of objects. But, while using the ML algorithm like SVM, an algorithm is required to identify all the objects having HoG and then recognize the particular objects and the updated book on machine learning you can buy using Flipkart Offers Today.

Yes, it is true that the Deep Learning algorithm takes longer to train. For example, the ResNet algorithm takes about two weeks to train completely. But, ML algorithms can take seconds or hours to get trained. But, in the test phase, Deep Learning algorithms take less time to run. But, ML algorithms take quality time as the data keeps on increasing. (Not true in all ML algorithms)

The main issue felt by deep learning technique is interpretability and that’s why many companies are still stuck with Machine Learning techniques. For example: if you are using a deep learning technique to calculate the relevance score of a page or a document. The performance might be par excellent and it will come with a score. But, the catch is- why this score came out? Yes, one might find it mathematically but in actual, we don’t know how neurons or nodes performed collectively to give this score. But, in machine learning algorithms like decision trees and logistic regression, you might not get such issues.

Overall, talking about Deep Learning, it easily outperforms machine learning technique when it comes to a large chunk of data or as we use the term nowadays i.e. “Big Data”. Also, deep learning can easily solve complex problems and doesn’t need any domain expert or feature engineering. Yes, you might need a high-end machine to apply deep learning techniques, but it is worth it.

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1 Comment

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    Sandy

    Nice job, worth reading twice

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