In recent times, both the terms \u2018machine learning\u2019 and \u2018deep learning<\/strong>\u2019 are creating a huge buzz around the AI landscape. The world is steadily becoming an artificial intelligence-first one where digital assistants together with other services act as our primary source of information. This concept is backed by the two terms we just mentioned. Both deep learning<\/strong> and usual machine learning<\/strong> are methods of teaching AI to perform tasks.<\/p>\n

Though some people use these terms interchangeably, they\u2019re not the same. In this post, we\u2019re going to learn the differences between deep learning<\/strong> and usual machine learning<\/strong> based on various factors. But before delving deeper, let\u2019s have a look at what these terms actually stand for.<\/p>\n

1- Machine learning<\/em><\/strong><\/h3>\n

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In its most basic form, machine learning is a method to implement artificial intelligence. Machine learning algorithms parse data, learn from it, and then apply that learning to make informed decisions. To understand easily how machine learning algorithms work, you can think of an on-demand music streaming service. For it to decide about which new artists or songs to recommend to a particular listener, machine learning algorithms relate the preferences of that listener to other listeners who\u2019ve a similar musical taste. Usual machine learning<\/strong> is widely used to perform all kinds of automated tasks across multiple industries, from finance professionals trying to identify favorable trades to data security firms trying to succeed in finding malware. When we refer to something that\u2019s capable of doing machine learning, it means it\u2019s capable of performing a function with data provided to it and gets better at that function progressively. Most often, usual machine learning<\/strong> algorithms work on a specific set of features extracted from the raw data. Features can be very simple like temporal values for a signal, pixel values for images, among others.<\/p>\n

It\u2019s important to understand that an algorithm isn\u2019t a complete computer program which is a set of instructions. It\u2019s a limited sequence of steps required to solve a specific problem. For instance, a search engine depends on an algorithm which grabs the text entered into the search box by a user and searches the associated database to come up with related search results. It takes certain steps to achieve a specific goal.<\/p>\n

Different types of learning algorithms are used in machine learning. Let\u2019s have a quick look at them.<\/p>\n

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• Supervised learning: It\u2019s a learning technique where the entire learning process is governed. Here, the key goal of algorithms is to predict the outcome when a set of training samples is provided together with the training labels. For example, if the assigned goal is to distinguish between pictures of boys and girls utilizing an algorithm for sorting images, the ones with a male child would come with a \u2018boy\u2019 label and those with a female child would appear with a \u2018girl\u2019 label. It\u2019s treated as a \u2018training dataset\u2019 and those labels remain in place until the program can sort the images successfully at an acceptable rate.<\/li>\n
• Unsupervised learning: Unlike the previous technique, here you don\u2019t have any training labels for the training purposes. Here, the algorithms are formulated in a manner so that they can find suitable patterns and structures in the data. In this technique, any of the two methods is used to perform the assigned task. One is called \u2018clustering\u2019 which groups similar objects together. The other method is called \u2018association\u2019 where the task is performed by determining a common pattern between the objects.<\/li>\n
• Reinforcement learning: This technique comes with an agent which learns how to behave in a particular environment by taking steps and quantifying the results. Chess can be considered as an excellent example of reinforcement learning. The program understands the rules of chess and how to play, and takes step by step actions to complete a round. The only information given to the program is whether it lost or won the match. If it loses the match, the game is replayed by the program, keeping track of the successful moves, until it wins a match finally.<\/li>\n<\/ul>\n

  That\u2019s all about the fundamentals of usual machine learning<\/strong>. Now, let\u2019s understand what deep learning<\/strong> is all about.<\/p>\n

  2- Deep learning<\/em><\/strong><\/h3>\n

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  Though deep learning<\/strong> has been around for some time now, these days it\u2019s getting more attention because of widespread adaptation. It\u2019s a subset of machine learning and also comes with supervised, unsupervised, and reinforcement learning. Though deep learning<\/strong> is inspired by the way the human brain works, it needs high-end machines and huge amounts of big data to provide optimum performance. Unlike usual machine learning<\/strong> algorithms which break problems down into different parts and individually solve them, deep learning<\/strong> solves a problem from end to end. A deep learning<\/strong> technique is capable of learning categories incrementally via its hidden layer architecture. Probably the biggest advantage of using this technique is the more data you feed deep learning<\/strong> algorithms, the better they get at solving a task. And technology\u2019s \u2018Big Data Era\u2019 is capable of providing massive amounts of opportunities for innovations in deep learning<\/strong>. There\u2019s an array of methods are used in this technique. Some of these include convolutional neural network, recurrent neural network, generative adversarial network etc. In the earlier example for usual machine learning<\/strong>, where images of boys and girls were used, algorithms were used by the program to sort those images based on spoon-fed data mainly. But with deep learning<\/strong>, there\u2019s no data given to the program to use. It scans every pixel within an image to identify edges which can be used to separate a boy from a girl. Then it\u2019ll put shapes and edges into a ranked order of probable importance in order to determine those two genders.<\/p>\n

  3- Differences between deep learning and usual machine learning<\/em><\/strong><\/h3>\n

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  Now that you\u2019ve gained an overview of usual machine learning<\/strong> and deep learning<\/strong>, it\u2019s time to learn about the differences between both based on some important points.<\/p>\n

  3.1- Data dependencies<\/em><\/h4>\n

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  The biggest difference between usual machine learning<\/strong> and deep learning<\/strong> lies in their performance as the volume of data increases. Usual machine learning<\/strong> algorithms usually perform well even if the volume of the dataset is small. On the other hand, deep learning<\/strong> algorithms require a massive amount of data to perform perfectly.<\/p>\n

  3.2- Feature engineering<\/em><\/h4>\n

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  Feature engineering refers to the process of putting the domain knowledge into the modeling of feature extractors to lower the complexity of data and make the patterns more visible in order to learn the algorithms working. The process is expensive and difficult in terms of expertise and time. In usual machine learning<\/strong>, performance depends on hand-crafted features as inputs. Here, features stand for pixel values, textures, shape, position, orientation, and color. The performance depends on how well these features are identified and extracted. On the other hand, deep learning<\/strong> doesn\u2019t rely on hand-crafted features and performs a hierarchical method of feature extraction, which means it learns features layer-wise. Hence, deep learning<\/strong> lowers the task of creating new feature extractor for each and every problem.<\/p>\n

  3.3- Hardware dependencies<\/em><\/h4>\n

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  Usually, deep learning<\/strong> relies on high-end machines while usual machine learning<\/strong> can be performed on low-end machines. For example, GPUs (graphical processing units) are an integral part of deep learning<\/strong> functioning. On the other hand, you can implement a usual machine learning<\/strong> algorithm on a CPU with fairly standard specifications.<\/p>\n

  3.4- Training time<\/em><\/h4>\n

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  Generally, deep learning<\/strong> algorithms need a long time to train because of the presence of a huge number of parameters. For example, a deep ResNet (deep residual network) takes around two weeks to train fully from scratch. On the contrary, usual machine learning<\/strong> needs much less time to train, from a few seconds to a couple of hours.<\/p>\n

  3.5- Problem-solving technique<\/em><\/h4>\n

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  You need to divide a problem into different parts in order to solve it using usual machine learning<\/strong>. For example, you need to do multiple object detection. The task involves identifying what\u2019s the object and where is it actually present in an image. In a usual machine learning<\/strong> approach, the problem would need to be divided into two steps: first is object detection and second is object recognition. On the other hand, in deep learning<\/strong> approach, the process would be done end-to-end. For instance, you\u2019d need to pass in the image, and it would come out with the location together with the object\u2019s name.<\/p>\n

  3.6- Industry ready<\/em><\/h4>\n

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  Usual machine learning<\/strong> algorithms are generally easy to be interpreted. They\u2019re interpretable regarding the parameter it chose and the reason behind it. On the contrary, deep learning<\/strong> algorithms are simply a black box. Even if those algorithms can outshine humans in performance, they\u2019re still not reliable in the context of to be deployed in the industry.<\/p>\n

  Conclusion<\/em><\/strong><\/h3>\n

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  Both the usual machine learning<\/strong> and deep learning<\/strong> have the potential to transform the business landscape. Machine learning is already being heavily integrated by businesses across different industries to gain a competitive advantage. Deep learning<\/strong> is considered one of the most high-end techniques to deliver state-of-art performances. Both of these applications have been surprising researchers each day with their capabilities to do wonders and we can expect to see this trend to be continued in the future.<\/p>\n