The debate on Machine Learning vs. Deep Learning has gained considerable steam in the past few years. The fundamental strength of both these technologies lies in their ability to learn from available data. Though both of these offshoot AI technologies triumph in “learning algorithms,” the manner in which Machine Learning (ML) algorithms learn is very different from the learning methods of the Deep Learning (DL) algorithms.
While ML directly observes data patterns and establishes correlations, DL algorithms learn progressively from intricate layers of knowledge. DL is considered a subset of ML, where learning happens through a layered network of algorithms commonly known as an Artificial Neural Network (ANN). The ANN is closest to the human brain in terms of functioning.
Machine Learning vs. Deep Learning: Which is Hotter?
The race for research and patents in the fields of Machine Learning and Deep Learning is on today and will continue to rise long into future.
While Machine Learning is a particular type of Artificial Intelligence, which facilitates automatic learning of algorithms by studying available data on their own. Machine Learning algorithms are smart programs that can search, access, and learn from data without any programmatic intervention. ML models are often used in industry sectors to predict risks and opportunities.
The SD Times claims that Big Data would not have happened if ML was not around to extract value from it.
On the other hand, the report Gartner’s Hype Cycle for Emerging Technologies named Deep Learning as one of eight emerging technologies of 2017 and beyond. Deep Learning may be considered a subset of ML, where learning happens through a layered hierarchy of disclosed knowledge.
In the DATAVERSITY® article The Business Benefits of Deep Learning, the cerebral (akin to the functioning of the human brain) quality of learning, typically found in DL has been described very well. The most marked difference between the two learning approaches is probably that ML algorithms deal with supervised data while DL algorithms are applied on unsupervised turfs.
Gartner provides the following forecasts:
- By 2019, 10 percent of customer-service staff will be engaged in bot interaction
- By 2019, startups will lead the AI economy, leaving the four giants behind.
- By 2020, enterprise success will depend on cognitive technologies.
- Also by 2020, at least 20 percent of businesses will engage staff in neural networks.
Similarities Between Machine Learning and Deep Learning
The Zendesk blog post A Simple Way to Understand Machine Learning vs Deep Learning uses the term “data” to unify ML and DL. According to this post, while ML algorithms learn from data to make decisions or predictions, DL algorithms interact with data in layers to make the learning progressive and cumulative.
It is common knowledge that both ML and DL deal with “learning algorithms,” though the learning methods are very different. The “common thread” between ML and DL can be explained through the analogy of a self-driving car. The basic implication is that when humans cannot write programs to solve problems, machine intelligence of ML or DL takes over to teach computers to become self-solving machines.
Another similarity between ML and DL lies in the application areas like computer vision, image recognition, information retrieval systems, marketing automation, medical diagnostics, and NLP, where both ML and DL algorithms have been applied successfully. The SAS Institute talks about some of the popular applications of ML.
Dissimilarities Between Machine Learning vs. Deep Learning
As mentioned earlier, the primary difference between ML and DL lies in the approach to learning in each case. Analytics India Magazine demonstrates how the “iterative learning process” employed in ML differs from the layered learning approach used in DL.
The article Deep Learning and Machine Learning Differences: Recent Views in an Ongoing Debate discusses how DL models have been described to be completely “unsupervised” students – learning on their own – one layer at a time.
According to Forbes the primary difference between Machine Learning vs. Deep Learning is in the actual approach to learning. DL requires very high volumes of data, which algorithms use to make decisions about other data. Moreover, DL algorithms can be applied to any types of data – image, audio, video, speech, etc, which is not usually possible with ML.
Data Science Central looks at the differences between different branches of Data Science or AI a little more holistically, and this is where readers will discover the diverse operational approaches to dealing with business data.
Some of the bigger dissimilarities between Machine Learning vs. Deep Learning are:
- Problem-handling: In ML, a large problem is broken into smaller chunks and then each chunk solved separately. Finally, all solutions are put back together. In DL, the problem is solved end to end.
- Volume of Data Usage: ML algorithms have proved their mettle both in massive-data and large-data scenarios. Deep Learning algorithms can perform when the data volume is massive. But can any of these algorithms really work when the data volume is low?
- Processor Requirements: ML algorithms manage quite well in ordinary machines but DL algorithms need high-performing machines to perform well.
- Feature Engineering: In ML, “feature extraction” is still handled manually, while in DL, feature extraction happens automatically during the learning process. As the manual process is both time and labor intensive, DL has reduced a lot of work by automating this critical phase of feature extraction.
- Training Time: In DL, because of intricate neural layers, the training time is longer and more complex. In ML, algorithms can be trained to learn in a very short time.
- Interpretability: In ML, precise rules are offered by algorithms to explain the decisions behind specific choices, whereas in DL, the decisions appear “arbitrary,” giving the user little interpretive capability to rationalize choices. That is why, DL algorithms could never work in forensic science, where evidence must be rationally explained in a Court of Law.
Comparative and Contrasting Features Between ML and DL
Gartner claims that DL algorithms are outperforming humans in many tasks like product recommendations, demand prediction, speech-to-text services, etc. In ideal cases, ML and DL jointly provide the best results.
At the highest level of applications, DL and supervised ML algorithms perform similarly, for example both DL and supervised ML algorithms can be trained to identify groups of objects within images in a huge image library. Where DL stands apart from ML is that DL can directly handle all types of data such as audio, video, image, whereas ML has to process the raw data and involve the Data Scientist to train the algorithm before making the model trained. This self-teaching capability of DL algorithms makes them very powerful.
The Feature Extraction process in ML is still handled by a human Data Scientist, which puts an additional burden on the processing time and performance. The automatic Feature Extraction available is DL places it a few light years ahead of traditional ML. The tremendous time and labor burden imposed on human Data Scientists is more removed in DL as the algorithms can automatically focus on right features without any intervention from a human scientist.
The relief provided by automatic feature extraction in DL is countered by the network topology design requirement. In many cases, Data Scientists use large data clusters concurrently, which puts a heavy load on the execution time and efficiency. That is why, DL algorithms run best in supercomputers capable of processing large vector operations very quickly.
Compared to Machine Learning, a significant drawback of Deep Learning is that is requires very expensive machines to perform. To make Deep Learning “affordable,” for businesses, the Data Scientist community has adopted graphics processing units (GPUs), which can perform billions of operations very fast. Another related characteristic found in the DL world is the presence of open-source libraries, which are canned solutions for many image and voice recognition applications. The major open-source libraries available today are TensorFlow, Cafee, or MXNet.
The article Deep Learning Updates: Machine Learning, Deep Reinforcement Learning, and Limitations discusses how the latest developments in the fields of AI and DL are gradually turning machines into self-thinking entities like humans. If Gartner’s biggest technological prediction for the current decade of “all things digital” is to become a reality, Machine Learning and Deep Learning, among other smart technologies, must continue to increase in efficiency, business value, and operational levels.
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