Why Deep Learning is outpacing traditional machine learning in a big way?

Artificial intelligence is all the rage! Suddenly, everyone, whether they understand it or not, is talking about it. The trend of artificial intelligence seems unstoppable, but it really boils down to two very popular concepts: machine learning and deep learning. Recently, however, deep learning has grown in popularity because it reigns supreme in terms of accuracy when training with large amounts of data.

To show you the attention deep learning is getting, here are Google's keyword trends:

Google's "Deep Learning" Trends

The software industry is now moving toward machine intelligence. Machine learning has become an essential part of every industry as a way to create machine intelligence In a simpler way, machine learning is a set of algorithms that parse data, learn from them, and then apply what they learn to make informed decisions.

Examples of machine learning are everywhere. It's how Netflix knows which show to watch next or how Facebook recognizes your friend's face in a digital photo. Or how customer service reps know if you'll be happy with their support before you take a CSAT survey.

One thing about traditional machine-learning algorithms is that they look complex, but they still act like machines. They require a lot of specialized domain knowledge, and human intervention can only meet their needs, no more and no less. And for AI designers and the rest of the world, that's where deep learning holds more promise.

What is deep learning?

In effect, deep learning is a subset of machine learning that achieves power and flexibility by learning to represent the world as a nested hierarchy of concepts, each of which is associated with simpler concepts, while more abstract representations are computed using less abstract concepts.

In finer terms, deep learning techniques progressively learn categories, such as letters, through a hidden layer structure, and then define higher-level categories (such as words) and higher-level categories (such as sentences). In the example of image recognition, it means recognizing light and dark areas before classifying lines, and then recognizing shapes to allow recognition of faces. Each neuron or node in the network represents an aspect of the whole and they **** the same to provide a complete representation of the image. Each node or hidden layer has a weight to represent the strength of its relationship with the output, and the weights are adjusted as the model evolves.

Deep Learning Architecture

Significant Features of Deep Learning

One of the major strengths of deep learning, and a key part of understanding why it has become popular, is that it is driven by large amounts of data. The "big data era" of technology will provide tremendous opportunities for new innovations in deep learning. As Andrew Ng puts it, "AI is similar to building a rocket ship. You need a huge engine and a lot of fuel. If you have a big engine and very little fuel, the rocket ship won't get into the right orbit. Or, if you have a small engine and a ton of fuel, you can't even get the rocket ship off the ground. To build a rocket, you need a huge engine and a lot of fuel.

To make an analogy with deep learning, that means the rocket engine is the deep learning model, and the fuel is the massive amounts of data that we're allowing these algorithms to learn."

Deep learning requires high-end machines that are the opposite of traditional machine learning algorithms. GPUs are now an integral part of executing any deep learning algorithm.

In traditional machine learning techniques, most application features need to be identified by domain experts in order to reduce the complexity of the data and make the patterns easier for learning algorithms. The great advantage of the deep learning algorithms discussed earlier is that they attempt to learn high-level features from the data in an incremental manner. This removes the need for domain expertise and hard core feature extraction.

Another difference between deep learning and machine learning techniques is the approach to problem solving. Deep learning techniques tend to solve problems from start to finish, machine learning techniques need to break down the problem statement into different parts. The problem is solved first and then its results are merged in the final stage.

For example, for a multi-object detection problem, deep learning techniques like the Yolo network take an image as input and provide the location and name of the object at the output. But in usual machine learning algorithms like SVM, a bounding box object detection algorithm is first needed to identify all possible objects in order to use the HOG as an input to the learning algorithm to identify relevant objects.

Often, deep learning algorithms take a long time to train due to the large number of parameters. The most popular ResNet algorithm takes about two weeks to train completely from scratch. Traditional machine learning algorithms take seconds to hours to train, a scenario that is completely reversed during the testing phase. Deep learning algorithms take much less time during testing. However, if you compare it to a nearest neighbor algorithm (a machine learning algorithm), the testing time increases as the data size increases. While this does not apply to all machine learning algorithms, some of these algorithms also have short test times.

Interpretability is the main issue why many industries use other machine learning techniques in deep learning. Let's take an example, let's say we use deep learning to calculate the relevance score of a document. It provides very good performance, close to human performance. But there is a problem, it doesn't reveal why it gives that score. In fact, you can mathematically figure out which nodes of the deep neural network are activated, but we don't know what the neurons are supposed to be modeling and what those neuron layers **** the same thing. So we can't interpret the results. This is not machine learning algorithms like decision trees, logistic regression, etc.

When to use deep learning or not?

1. Deep learning performs other techniques if the amount of data is large. However, traditional machine learning algorithms are more preferable due to small amount of data.

2. Deep learning techniques require high end infrastructure to train in a reasonable time.

3. When there is a lack of domain understanding of feature introspection, deep learning techniques outperform other domains because you don't have to worry about feature engineering.

4. Deep learning really excels at complex problems such as image classification, natural language processing, and speech recognition.