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Deep Learning and Machine Learning Differences: Recent Views in an Ongoing Debate

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The science of Machine Learning (ML) has been around since the 1970s, but low horsepower processors and limited data forced the progress of Machine Learning to slow down in the 1980s. Ever since Big Data has enabled the use of unlimited “variety, volume, and velocity” business data, Machine Learning resurfaced as a powerful game changer in the world of software algorithms.

Google’s acquisition of UK-based Deep Mind resurrected the struggling field of Deep Learning (DL) and renewed the self-training possibilities of machines. In Deep Learning, smart algorithms can aid computers to learn from one layer of data and apply that learning to the next layer without programming intervention. While Machine Learning encompasses the entire field of learning algorithms, Deep Learning involves specific types of learning models where the human programmer is not required to train computers.

Deep Learning algorithms train the computer to use the learning from one layer of a neural network to the next layer, thus feeding on past experience to create new knowledge. The super powerful processors and open availability of data have removed all the earlier barriers in Deep Learning and have paved the way for much awaited self-guided Artificial Intelligence applications.

The Reawakening of Machine Learning for Artificial Intelligence

The somewhat aborted progress of Artificial Intelligence (AI) research was refueled by advancements in computer processing and unlimited Big Data mainly because both ML and DL could now be properly applied in AI applications. Since 2012, the prominent market players have taken active interest in Machine Learning and Deep Learning to establish their leadership in AI research. In the last few years, ML and DL have literally invaded the daily lives of a digital society, where everything from online searches to online shopping is enhanced by these two revolutionary technologies.

While AI is the umbrella technology which deals with intelligent machines, Machine Learning is a sub-field of AI, and DL is a further sub-field of ML. Machine Learning has given rise to a number of specialized techniques like Decision Trees, Linear Regression, Random Forest, or Neural Networks – where Deep Learning is an off-shoot of Neural Networks.  This Forbes post  makes a fair attempt to describe the multi-layered building blocks of Deep Learning algorithms that aptly justify the use of the term “deep” in DL.

A Glance Back at Machine Learning – the Past Training Algorithms

Machine Learning was conceived to tackle those problems for which computer programs were not readily available. Specific problems such as recognizing hand writing or comprehending cannot be solved through programming. As these activities are integral to the human brain functions, it is not easy to mimic such functions in programming language. So to tackle such problems, the scientists conceptualized a framework of studying unlimited examples through which computers could learn to decipher solutions. In ML, the computer behaves much like the human child – studies many examples of a particular problem, then uses that knowledge in a similar situation to arrive at a solution. The algorithm aids the computer to study the available data samples and apply the acquired knowledge in a new situation.

Though both ML and DL teach machines to learn from data, the learning or training processes implemented in the two technologies are distinct. While both ML and DL train the machine to learn from available data, the different training processes in each produce very different results.

A Glance at Deep Learning – the Present Training Algorithms

Deep Learning is a specific type of Machine Learning where the learning happens in successive layers – each layer adding to the knowledge of the previous layer.  During the era of the  Perceptron learning algorithm, an AI genius Marvin Minsky first proposed the need for layered learning to work around the ambiguity of natural language. The article titled Everything You Need to Know About Deep Learning and Neural Networks provides a historical perspective of the fields of Neural Networks and Deep Learning.

Deep Learning, quite simply, trains the machine to do what the human brain does naturally. The feature extraction process is critical to Machine Learning helps the machine to make the final decisions. Just feeding unlimited data to an algorithm would never work unless feature extraction was present to guide the machine about the specific learning objects. Feature extraction posed a challenge to traditional Machine Learning as the human programmer had to intervene and guide the machine which features it should be looking for in the learning process. This additional workload not only burdened the programmer but also left gaping loopholes for decision errors!

The emergence of Deep Learning changed all that as it relied on natural learning by adding layers of knowledge one on top of the other. DL models are capable of selecting the right features on their own, thus eliminating the need for a human programmer. This revolutionary advancement in learning algorithms not only saves human time and labor but also minimizes the possibility of decision errors.

Differences Between Machine Learning and Deep Learning

Here are some key process differences in Machine Learning and Deep Learning:

  • Manual Intervention in ML: Whenever new learning is involved in ML, the human programmer has to intervene and adapt the programming algorithm to make the learning happen. In DL, the Neural Networks facilitate layered training, where smart algorithms can train the machine to use the knowledge gained on one layer to the next layer for further learning without the presence of a human programmer.
  • Feature Extractions in Machine Learning vs. Deep Learning: In ML, the human programmer guides the machine on what type of features to look for, whereas in DL, the feature extraction process is fully automated. Thus in DL, the feature extraction is more accurate and result driven.
  • The Presence of Multiple Layers in Deep Learning: As an off-shoot of Neural Networks, DL relies on layered knowledge without programmer intervention whereas ML depends on guided study of data samples.
  • The Level of Accuracy in Deep Learning: DL’s self-training capabilities yield faster and more accurate results. In traditional ML, programmer errors can lead to bad decisions.

Unsupervised vs. Supervised Learning

In supervised learning, the machine has specific data samples to study to create knowledge; in unsupervised learning, the machine is simply thrown some data with the hope that it will recognize the patterns on its own. A game of electronic chess may involve unsupervised learning and supervised learning. KD Nugget explains in Deep Learning in a Nutshell: What it is, How it works, Why care? As Deep Learning has proved its superiority over traditional Machine Learning in virtually every facet of learning algorithms, DL is fast gaining popularity among the global AI community.

According to a blog post titled Deep Learning vs. Machine Learning vs. Pattern Recognition, the primary difference between traditional Machine Learning algorithms and Deep Learning algorithms is that DL algorithms require huge volumes of sample data and superior GPUs. This article also claims that the reason that DL has received such glowing attention is because of Google’s interest in DeepMind and its current focus on improving the services provided by Google through DL techniques.

So Why Has Deep Learning Reigned Supreme in Recent Years?

The Machine Learning Mastery article titled What Is Deep Learning? explains that DL has specific advantages over other forms of Machine Learning, which make it the most favored algorithmic technology of the current era. Deep Learning supports scalability, unsupervised learning, and layering of knowledge, which make this science one of the most powerful “modeling science” for training machines. Andrew Ng from Coursera claims that the discovery of Neural Networks, along with the availability of superfast computers, has accelerated the growth of Deep Learning while the other forms of ML have reached a “plateau in performance.”

In Learning Deep Architectures for AI, the author Yoshua Bengio emphasizes that the hierarchical feature extraction mechanism in DL safely automates the learning process to signal an era of processing independent of human involvement, which improves the chances of DL being the future AI choice of technology.

The Other Side of the Picture – is Deep Learning a Tsunami or a Tropical Depression?

See Why Deep Learning Is Radically Different from Machine Learning to understand how Deep Learning has disrupted the world of Machine Learning. KD Nuggets has reposted this blog post on their site to show readers about the apparent misconceptions surrounding DL. Even Gartner failed to recognize the power of DL and ignored it altogether in August 2016 Hype Cycle.

The article titled What’s the Difference Between Deep Learning and Machine Learning? seems to suggest that the technology giants like Microsoft, Google, Facebook, or Baidu are all leveraging the power of DL to stay ahead in the technology race. Today, with the groundwork set by Machine Learning, Deep Learning has shaped into a collection of techniques and strategies rather than a multi-layered learning system. How Deep Learning is going to affect the Data Management industry going forward is a topic covered by many; will it transform in a positive way, or is it going to cause turmoil and upheaval?

If you are further interested about the long-range impact of Machine Learning and Deep Learning in the AI world, see the DATAVERSITY® video blog by Andrew Brust, How Machine Learning and AI are Impacting the Data Industry.


Photo Credit: Sergey Nivens/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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