(PDF) Deep Learning vs Machine Learning...Chandigarh Mohali Gurgaon Netsmartz House, Plot no. 10, Rajiv Gandhi Chandigarh Technology Park, Chandigarh - 160101 Phone: +91-172-5055200 IT-C-6, - DOKUMEN.TIPS (2023)

WHITEPAPERDEEP LEARNING VS Machine LearningApril -2019

Deep Learning Machine Learning

vs

First of all, unlike Machine Learning, which mainly centralizesthe core of its efforts on solving real - world problems, which ofcourse is beneficial, Deep Learning is a bit more, implementingwhat has been learned through Machine Learning and taking things astep further, still. Machine Learning incorporates neural networksand AI (or Artificial Intelligence), respectively, hoping toproperly mimic the every actions of the human decision - maker, soas to more effectively enact a future response, prediction orpredictability rate, and much more. Two very narrow subsets, arewhat ML's (Machine Learning's) tools focus on, mainly.

Deep Learning must be integrated, to go deeper, requiring atargeting of problems that require further insight, and ofparticular importance, those within these areas :

1. Hidden Layers

2. Input Layers

3. Output Layers

Deep Learning and Machine Learning are not the quite the same.Yet they bear some similarities. Let us have a closer look in thiswhite paper…..

Deep Learning Itself

VS VS

Artificial Intelligence Machine Learning Deep Learning

A technique which enables machines to mimic human

behaviour

Subset of AI techniques which use statistical methods toenable

machines to improve with experience

Subset of ML which make the computation of multi-layerneural

network feasible

Deep Learning vs Machine Learning

Deep Learning, as such, falls underneath Machine Learning, asone of its major subsets, relatively speaking. It's one of thenewest terms in its field, as well as one of the best ways toincorporate it. So without Machine Learning, Deep Learning wouldnot exist ---- without Deep Learning, on the other hand, MachineLearning would not be as beneficial. The two do go hand - in -hand.

Machine Learning itself, on another note, is invaluable for whenit comes to predicting a human being's weight based upon his or herheight, for instance. Data capturing errors, and false information,mostly, may be rightfully detected through the best of the latestML. It all begins with the proper collection of all data (or datastreams), having data points represented on graphs, charts, orother visual illustrations of analysis. Predictions and results arethe two final measurements for performance across any test orstudy, respectively, holding the final word. One such formula foraddressing this could be Weight (in kg) = Height (in cm) - 100.

Three Main Forms of Learning Algorithms to Assess

The first of three encompasses Supervised Machine Learning,which is all about making the right predictions and thereforeoffering the most analytical value in advance. Value labels getsent to data points, and certain algorithms must then find theircorrelating patterns. The second learning algorithm would be thatof Unsupervised Machine Learning, which has all the more to do withassessing data points, which have not been assigned to any labelsof any kind whatsoever. Groups of clusters, here, are how data getsorganized --- this is a common Machine Learning algorithm.

Furthermore, we come to the last of the three, which would beReinforcement Machine Learning Algorithms, which mainly serve inhelping to pick out an action, respectively. These actions basethemselves on certain data points, targeting one at a time, pereach data point. The algorithm here, over time, self - learns, andin doing so, changes its own strategies and protocols (so as tobetter learn and adapt to new ones, and much more).

Certain Dependencies in Terms of Data

Machine Learning's and Deep Learning's algorithms both basethemselves off of potentially performance as the main separatingfactor. Yet when the amount of data is very small, or limited inquantity, one may find that Deep Learning algorithms may not workas well, since as has been mentioned, they are initially designedfor further, more complex and higher amounts of data, respectively.But when there is plenty of data to be broken down and processed,variated, and more (usually by means of larger organizations orfirms who hold growing numbers of clients and databases), DeepLearning is the more preferable solution of the two. It is designedfor engaging mass sums of information in short periods.

Certain Dependencies in Terms of Hardware

Deep Learning will, more often than not, rely upon high - endmachines. Traditional, Machine Learning does the very opposite andholds its reputation for doing so, working to rely mostly on low -end machines. GPU's, as such, become a more central requirement -component for Deep Learning initiatives. Countless, ongoing, Matrixmultiplication operations, as well, can be more effectively done ona larger scale --- through Deep Learning. Similar things may besaid in terms of software, as well, which is better to integrateDeep Learning to….when the need is more quantity- and quality-based.

input OutputFeatureExtractor FeaturesTraditional

MLAlgorithm

Traditional Machine Learning Flow

input OutputDeep Learning Algorithm

Deep Learning Flow

Feature Engineering

This very generic process involves domain knowledge, first andforemost, which ought to be properly put into making featureextractors with two goals. The first would naturally be to reducedata complexities, and the second might be to make all relevantpatterns a whole lot more visible, in order to learn how the inter- related algorithms have been working (and can work) their best.Time - consuming and quite costly, not to mention quite difficultto fully, properly process, it is still worth a go. Many expertshave already found value in incorporating both subsets within thiscategory.

Approaches for Problem - Solving

'Traditional learning' algorithms, for example, are what havebeen used the most, for century upon century upon century….in orderto solve all forms of known problems or abnormalities. Yet what wehave found, in the last two years, even, is that problems need tobe broken down, each into separate parts, in order to then have tobe solved, individually --- one by one. And perfectly combiningthem all, checking the work multiple times, is what would give us aresult. Machine Learning, as such, would usually divide allproblems into two separate steps, involving its detection and itsrecognition. This is just a side note to consider in assessing allof this...

Execution Time

Machine Learning is a lot quicker in both its training andexecution times, though as we noted earlier, this does not accountfor it quantity or quality of data, separately. Deep Learning willtake a lot more time ; one can not train it as quickly or easily asone would with traditional Machine Learning. Deep Learning'salgorithms envelope so many parameters, and that is why ; one maytrain a Machine Learning algorithm, however, within seconds.Sometimes, it may take just a few hours, maximum, but with DeepLearning, it's far longer, usually ---- yet many would attest thelonger wait is well worth it.

What goes into a successful model

Exploratory Analysis

Data Cleaning

Feature Engineering

Algorithm Selection

Model Training

Other

10%

20%

25%

10%

15%

20%

Labeled data, 'structured data', as we have seen, certainly hasmuch to do with comparing the two learning type subsets of AI.Firms may benefit most from Deep Learning, especially when workingwith

massive streams of information. And for solving more complexproblems, of which even Machine Learning might not address, DeepLearning is certainly the solution to choose from as we've alsojust

seen. But for all cost - based, basic needs, perhaps notrequiring much hardware or software advancements either, MachineLearning is ideal.

We see a few of the resemblances, and even cross - comparisonsbetween the two : Deep Learning and Machine Learning, respectively.There has been much to note. Each of the two categories, eitherway,

provides infinite value and future potential to nearly everysector on the globe, be it personal or professional. There remainsmuch unsaid, worth a further probe…..

Conclusion - Final Word

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Who we areHeadquartered in Rochester, New York and with multipleoffices across the globe, Netsmartz is a CMMi3 & ISO 9001:2008certified company. Formed over 19 years ago, we currently have over1000 employees, a rich experience of successfully executing 2000+projects and working with small companies to Fortune 1000clients.

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