Differences Between Machine Learning & Deep Learning

deep learning vs machine learning

deep learning vs machine learning

It is important for organizations to clearly understand the difference between machine learning and deep learning. By definition, machine learning is a concept in which algorithms parse the data, learn from it, and then apply the same to make informed decisions. A simple example would be of Netflix, which uses an algorithm to learn about your preferences and present you with the choices that you may like to watch. In the case of machine learning, the algorithm needs to be told how to make an accurate prediction by providing it with more information, whereas, in the case of deep learning, the algorithm is able to learn that through its own data processing. It is similar to how a human being would identify something, think about it, and then draw any kind of conclusion. Understanding how artificial intelligence works may seem to be highly overwhelming, but it all comes down to two concepts, machine learning, and deep learning. These two terms are usually used interchangeably assuming they both mean the same, but they are not. Both the terms are not new to us, but the way they are utilized to describe intelligent machines has always been changing.

What is Machine Learning?

Here’s a basic definition of machine learning: “Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions” As the name suggests, machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”. The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions. ML is a subset of artificial intelligence; in fact, it’s simply a technique for realizing AI. It is a method of training algorithms such that they can learn how to make decisions. Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information. Machine learning is the practice of using algorithm to break up data, learn from it and then use this data to make some predictions about a certain thing. The machine is trained using a huge amount of data to perform a certain task. It is given an algorithm that allows the machine to perform a task without extensive programming.

What is Deep Learning?

Deep learning is one of the many approaches to machine learning which tries the mimic human brain working using neurons. Artificial Neural Networks (ANN) consists of layered structure of algorithms. Each feature is represented by a node. It was deeply inspired on how the brain works specially the interconnecting of the neurons. That is the reason deep learning is more capable than the normal machine learning model. ANN has multiple layers and each layer is used to identify a unique feature of an object. Each layer will pick out a particular feature to learn and use it to identify the object. The name deep learning comes from the multiple layering which provides depth. It is a sub-category of machine learning. Similar to machine learning, deep learning also has supervised, unsupervised, and reinforcement learning in it. As discussed earlier, the idea of AI was inspired by the human brain. So, let's try to connect the dots here, deep learning was inspired by ANN were inspired by human biological neural networks. Deep learning is one of the ways of executing machine learning

Comparison Between Deep Learning & Machine Learning

Functioning:

Deep learning is a subset of machine learning that takes data as an input and makes intuitive and intelligent decisions using an artificial neural network stacked layer-wise. On the other hand, machine learning being a super-set of deep learning takes data as an input, parses that data, tries to make sense of it (decisions) based on what it has learned while being trained.

Hardware Dependencies:

Generally, deep learning depends on high-end machines while traditional learning depends on low-end machines. Thus, deep learning requirement includes GPUs. That is an integral part of it’s working. They also do a large amount of matrix multiplication operations.

Data Dependencies:

Machine learning algorithms often work well even if the dataset is small, but deep learning is Data Hungry the more data you have, the better it is likely to perform. It is often said that with more data the network depth (number of layers) also increase hence more computation. From the above figure, you can see that as the data increases the performance of deep learning algorithms increases compared to traditional machine learning algorithms in which the performance almost saturates after a while even if the data is increased.

Feature Engineering:

It’s a general process. In this, domain knowledge is put into the creation of feature extractors to reduce the complexity of the data and make patterns more visible to learn the algorithm working. Although, it’s very difficult to process. Hence, it’s time consuming and expertise.

Computation Power:

As you learned that deep learning networks are data dependent, they need more than what a CPU can offer. For the deep learning network training, you need a graphical processing unit (GPU) which have thousands of cores compared to a CPU that has very minimal cores. The computation power not only depends on the amount data but also on how deep (large) is your network, as you increase the amount of data or the number of layers, you need more and more computation power. On the other hand, a traditional machine learning algorithm can be implemented on a CPU with fairly decent specifications.

Execution Time:

Usually, deep learning takes more time to train as compared to machine learning. The main reason is that there are so many parameters in a deep learning algorithm. Whereas machine learning takes much less time to train, ranging from a few seconds to a few hours.

Training and Inference Time:

The training time of a deep learning network can range from anywhere between a few hours to months. Yes, you read it right! The training can often last for months. If you have a vast number of data, training a network a more significant data usually takes time. Moreover, as you increase the number of layers in your network, the number of parameters known as weights will increase, hence, resulting in slow training. Not only training but very deep neural networks can also take a lot of inference time since the input test data will pass through all the layers in your network, a lot of multiplication will take place which will consume a considerable amount of time. Whereas traditional machine learning algorithms often train very fast ranging from few minutes to a couple of hours, but during the test time, some algorithms can also take quite a bit of time.

Problem-solving technique:

To solve a problem using machine learning, you have to divide the problem into different parts. Let's say you want to do object recognition, for that you first traverse through the complete image and find if there is an object at each location and where exactly it is present. "Then, from all the candidate objects, you apply a machine learning algorithm let's say Support Vector Machine (SVM) with Local Binary Patterns (LBP) as feature extractor to recognize relevant objects. On the other hand, in deep learning, you give the network both the bounding box coordinates & all the object's corresponding labels, and network learns to localize and classify on its own."

Industry Ready:

Machine learning algorithms are often easy to Decode how they worked. They are interpretable regarding what parameters it chose and why it chose those parameters, but on the other hand, deep learning algorithms are nothing but a black box. Even if the deep learning algorithms can surpass humans in performance, they are still not reliable when it comes to deploying them in the industry. Machine learning algorithms like linear regression, decision trees, random forest, etc., are widely used in industries like one of its use case is in bank sector for stock predictions.

Output:

The output of a traditional machine learning is usually a numerical value like a score or a classification. Whereas, the output of a deep learning method can be a score, an element, text, speech, etc

Conclusion

We have studied deep learning and machine learning and also looked at a comparison between the two. In today's post, you were introduced to a comprehensive theoretical analysis of artificial intelligence and two techniques to implement artificial intelligence namely machine learning and deep learning. You also learned about various machine learning and deep learning algorithms and how these two learning techniques are different from each other. In Conclusion, the image above is the best summary of the difference between deep learning and machine learning. A concrete anecdote would be to consider raw data forms such as pixels in images or sin waves in audio. It is difficult to construct semantic features from this data for machine learning methods. Therefore, deep learning methods dominate in these models. Deep learning also comes with many more nuances and unexplained phenomenon than classic machine learning methods. Please let me know if this article helped frame your understanding of machine learning compared deep learning.