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Category: Data Science Courses

Sherlock Holmes Has Always Been a Data Analyst. Here’s Why

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The job of a data analyst or scientist revolves around gathering a bunch of disorganized data, and then using them to build a case through deduction and logic. Finally, following that you will reach a conclusion after analysis.

Sherlock Holmes Has Always Been a Data Analyst. Here's Why

Below quote from Sherlock Holmes is relevant –

“When you have eliminated the impossible whatever remains, no matter how Improbable it is must be the truth.”​

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He always started each case by focusing on the problem.

The problem would sometimes arrive in the form of a letter, sometimes as an item in the newspaper, but most often, it would announce itself by a knock at the door. The client would then present the mystery to Holmes and he would probe the client for salient information. Holmes never relied on guesswork or on assumptions. For Holmes, each new case was unique, and what mattered were reliable and verifiable facts about the case. These gave the investigation an initial focus and direction.

Deduction, Reasoning & Analytics

“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”

Similarly a data analyst is expected not to assume or formulate theories, which can make the reasoning biased. In his stories, Sherlock Holmes demonstrates his keen powers of observation and deduction from data in front of him. He can decipher how the light enters in Watson’s bathroom based on how his beard is shaved; he attests one person has lived in China from one of his tattoos; he discovers previous financial situation of a man who he had never seen before just looking to the hat the man had just used.

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A data scientist has powerful computational and statistics tools that help him finding patterns amid so much data.

 

In the end, a data analyst’s introduction can be similar to what Sherlock said:

“My name is Sherlock Holmes. It is my business to know what other people do not

know.”

–Team Cosmos

You can learn more about Data analysis by taking up Data analyst certification courses. DexLab Analytics also offers Business analyst training courses.

 

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Shadowing a Data Architect for a Day!

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Shadowing a Data Architect for a Day!

A data architect is a noteworthy role in the present analytics industry. One can naturally evolve from a data analyst or a database designer to a data architect after gathering sufficient experience in the field. The prominence of this role showcases the emergence of the online websites and other internet avenues which require the integration of data from several unrelated data sources.

These data sources can be anything from:

  • External sources, like market feeds (for e.g. Bloomberg) or other News Agencies (like, Reuters)
  • Or they could be internal sources like exiting systems that collect data, for instance HR operations that gather employee data

Here is a depiction of a day in the life of a successful data architect:

Data analyst certification from a reputable analytics-training institute can help to speed up your process of evolution from being a data analyst to becoming a successful data architect!

 

Shadowing a Data Architect for a Day! from Infographics


 

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Harnessing Big Data for Water Management

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World Water Day: Save Water with Big Data

Appalling forces are re-establishing the relationship between humans and water.

In the past, communities developed slowly, while the weather remained constant. Water sources never depleted at tumultuous rates as it has today. Water is no longer a dependable resource. That’s why many countries and cities are embracing smart technologies to manage water efficiently and preserve it for the coming generations.

As we observe the United Nations World Water Day on Wednesday, 22nd March, it is apt to assess the development being made in conserving this diminishing resource.

World-Water-Day-Save-Water-Save-Water-Save-Nature

 Today, the Internet of Things (IoT) – a blooming worldwide network of devices and appliances linked to the internet – has materialized as a propitious solution to save water and protect clean drinking water, especially in cities.  

To begin our discussion, Netherlands is on its way to develop a pioneering program to address the relevant problems of increasing sea levels, surging number of droughts and the effect of extreme weather changes on its trains, bus networks and roadways, and the efficiency with which the entire nation tackles situations like this. The ambitious project, Digital Delta draws in local and regional water jurisdictions, top-notch scientists and proliferating businesses to implement Big Data technology for upgrading the systems of its €7 billion water management, while restricting the costs of preserving water by 15%.

Prophecies about Urban Centres
data_flow

Plummeting freshwater resources: a serious challenge faced by the global population is now at its apex. An overwhelming 89 percent of the world population thrives on enhanced water supply systems, which results in a loss of more than 32 billion cubic meters of fresh water, through physical leakage. Thereby, more than 50 percent of world population will be vulnerable in water-stressed regions by 2025. And by 2040, the figures will further push the energy demand by 56%, making US the second highest energy consumer across the globe.

Saving Water Globally

In the meantime, most of the world cities should re-invent and re-structure their assets to pull together advanced functions encompassing different complex systems and to associate with new powerful allies. Urbanization comes with its own costs. Day by day, these networks are growing more complicated and even more expensive. By delving deeper into the interconnections of systems, the societies will be in a better position to grasp how to run them more efficiently.

Water has never grabbed eyeballs, as it has today. Many countries are not at all prepared to manage such burgeoning complexities of water management. Besides, water management authorities are constantly under pressure to harness their power for flood protection and drinking water standards.

Reality Check: Water demand is set to rise by 30% by 2030. Ever increasing population and swelling urbanization are the reasons behind such calamitous figures.

Smart City Technology – The Key to Urban Sustainability

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New Jersey Institute of Technology (NJIT) revealed that by 2025 smart city technologies would multiply to an industry estimating $27.5 billion. Moreover, nearly 88 smart cities will develop by the end of 2025. Smart cities whirl around the concept of using improved, interconnecting technologies to make environment safe, lives easier and urban living cost-effective and more efficient.

Societies are enduring new weather extremes. It is the high time to use big data and analytical science to cure the growing complexities in managing our water systems. Smart technology is the only viable option that can take future generations towards a sustainable future.

Seeking data science courses online? Visit us at DexLab Analytics. We offer a wide array of highly interactive online courses in data science.

 

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Knock! Knock! It’s Time to Change Your Bad Data Habits

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Knock! Knock! It’s Time to Change Your Bad Data Habits

Do you follow your instincts instead of data and insights?

Do you prefer storing data in different databases, in separate formats with varying values?

Habits are subject to change. Though it may take some time, but eventually it evolves. Good and bad habits make a person. Good habits don’t demand attention, but bad habits often need to be looked into.

If you suffer from bad data habits, then you must make sure you deal with it. It has to be a thing from your past rather than a dominating present. After all, data is incredibly important for business organizations to proliferate and generate decent revenues.

 

As per Experian’s Data Quality Report, 83% of companies consider their revenue suffers from inaccurate and insufficient customer data. It happens because of time and money wastage on insubstantial resources, which leads to a humungous loss of productivity and profit.

Bad Data Habits: The Ugly Truth

Data is the essence of business. From email delivery to customer feedback to profit generation, the impact of data trickles from strata to strata.

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Sadly, many companies fail to fathom the significance of data and continue storing data on multiple systems, instead of a single location, in various formats without actually knowing ways to handle it. This eventually results into huge data pile-ups, where the entire data silo becomes difficult to manage.

However, if you have the right tools and a zeal to ensure data quality, you can confidently manage your data, eradicate duplications and fix errors before they inflict damage to your fundamentals. Besides, prudent strategies, time-to-time reviews and absolute determination are necessary; read this article to gain more insights about how to work on your bad data habits.

Let awareness do the work

Detailed information about customers is crucial for better assistance and quicker efficiency. So, you should always tell your customer support team to derive more information about their customers in order to serve better.

Understand your data needs

What data is important for your business? Once you know that, you will be able to apprehend your customer’s needs and expectations more effectively. Moreover, be sure that the data is accessible to all those who really needs it, otherwise it won’t be fruitful.

Introduce Standardised Data Quality Policies

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For high quality data, make sure you introduce standard data policies and procedures. Also, ensure that the people working in your organization are acquainted with the ways of recording and storing it.

Initiate Regular Reviews

Data degradation is common. Human beings commit mistakes. Hence, it is important to regularly review and cleanse data in order to avoid future discrepancies.

Integration and Installation of the Right Tools

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Integrate your network to ensure the data is stored on one server, but accessible from multiple locations. This will help you get an entire picture of your company’s business performance over varied mediums. Install any of the improved Data Cleaning Software to make sure your data is free of duplicates and perfectly formatted right from the start.

 

To brush up your analytics skills, get enrolled in a Data analyst course. Visit DexLab Analytics.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
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To learn more about Big Data Course – click here.

Concocting Data with GIS

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Concocting Data with GIS

In supreme and sophisticated geospatial realm, data have been predominant. Or, should I say it is the matured fosterling of Geographic Information Systems (GIS). Choose, whatever suits you; subject to whom you work for or what you need to work on. The meat and potatoes? To excel on location analytics, concentrate only on the best most current data.

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In today’s world, data is valuable. It is vital and veritable. It is indispensable in Geographic Information Systems (GIS).

To second that, today’s tech-efficient society is anchored on location-based data, than ever, especially with the rise in Twitter, Google, Facebook and other social media apps, which collects and stores data from their highly-valued users to sell them off to money-grubbing advertisers.  Though secretly. On the other hand, cell phones go a step ahead in broadcasting your current location data 24/7. Otherwise, how would your friends know that you are safe when a severe earthquake rattled your neighbouring city! (Thanks to location settings)

Feisty Predicaments

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However, the real challenge lies in data identification and consumption. Countless number of users gets baffled when it comes to finding data, and if found, how to consume it to set off their business determinations. To solve this, many imminent think tanks of tech industry came out with direct and decisive solutions. Some of them were loaded with an abundance of data, i.e. digestible and disintegrated. By disintegration, they meant that the data was categorized into: points of interest, roads, boundaries and demographics, for easy comprehensibility. Furthermore, industry data bundles concerning telecommunications, retail and insurance fields were added to make the coverage global and profitable. To top it off, quality content and sprawling file formats boosted the results and mechanisms, both.

Conflux of GIS and BI

Location technology – Does this ring a bell? Yes? Then you would be familiar with GIS but others, particularly new Business Intelligence users and consumers must have just started taking baby steps on basic mapping. For BI, maps are the backdrop against which business analysts project their business data, stats and analytical information. Analysing the data to understand the insights of consumers is crucial, directly affecting the business decisions and revenues thereby. For example, heat maps, used to see the concentration of installations, customers and IoT devices provides an unparalleled accurateness of spatial relationships, which is impossible to obtain from the spreadsheets.


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One of the integral location analytics issues is to help in identifying the high-risk zones at the time of natural disasters, like tornadoes, earthquakes, floods, hurricanes or mudslides. For example, in the US, the East Coast is vulnerable to a lot of hurricanes and floods, whereas earthquakes and mudslides snap the West Coast time to time. Assessment of these location problems is intrinsically important for mortgage underwriters, insurance agents and public safety departments. And best data along with effective geo-coding is the solution to all the inconveniences. 

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Interested in a career in Data Analyst?

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Data Analytics for the Big Screen

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Can the film industry leverage more on data analytics?

Film making as an industry is as dependent on good marketing as it is on good content.

Data Analytics for the Big Screen

And it is here that data analytics comes to the picture, for not only does it govern marketing strategies of a Studio but in future it might govern the creative half as well.

For a conventional Hollywood blockbuster, an average of $70 Million are spent within 10-12 weeks and data analytics might direct us as to how much cash needs to be spent and where. Nowadays companies such as IBM are experimenting with Deep Sentiment Analysis, which tries to gauge the market sentiment by listening to the constant stream of content being posted by the users in a given area. The data comes from all sorts of sources, both structured and unstructured, which then needs to be cleaned before gaining any actionable insights from it. Nowadays, companies are working towards developing Market Optimisation Models where they can use historical data to create models, which are then fed current data in order to guide marketing budget allocation decisions. Another way studios are using data analytics is to predict market reaction in USA and Europe by analysing moviegoer’s reaction to the initial run of the movie (usually in smaller markets of Asia). They then proceed to rebrand/improve its offering to make it more ‘commercial’ for a given region.


But does this seemingly endless data and ever improving predictive model point towards a future, where Big Data might tell writers what to write, directors how to direct and actors how to act? If the answer is in affirmative, then are we diluting cinema as an artistic medium? Studios, such as Netflix have now extracted about 70,000 unique characteristics from its movie collection, and now they are analysing how the presence/absence of a characteristic has an impact on the movie revenue/rating/viewing. It then uses these findings to develop and fine-tune the shows it will produce in future. This increasingly ‘scientific’ manner of developing movies is taking over at other studios as well, along with experts fearing that this practice might lead to the industry losing its experimental and creative edge.

With proved benefits, including increased revenue and minimal risk, it is imperative for studios to invest into Data Analytics. It has become imperative to design their marketing strategy using this mine of user data to make their offerings economic, popular, efficient and successful.

Seeking data analytics certification courses to boost your business growth? Go through our comprehensive Online Courses in data science at DexLab Analytics.





 

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You Must Put These Data Analytics Books in Your Reading List This Year

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To be a successful data analyst, you must share two very important attributes that you must possess:

 

  1. You must be a voracious reader in order to keep up with the developments in the industry
  2. You must be willing to share your knowledge with the people in a simplified manner, so that everyone around you also gets access to this knowledge
     
    You Must Put These Data Analytics Books in Your Reading List This Year

 

That is because the universe around us deals in the common currency of information and wisdom, which should flow freely without any price tags on it.

Continue reading “You Must Put These Data Analytics Books in Your Reading List This Year”

How do you Like your Coffee? Strong with a Lot of Numbers!?

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How-do-you-like-your-coffee

 

In the health conscious world today, people are often engaging in hot debates as to which drinks have the most and least amounts of caffeine in them. But as data analysts we do not like to engage in any critical discussion without having a few graphs to show for our arguments! So, the moment someone put the kettle on, we turned on our computers to sip a cuppa and make a few caffeinated graphs.

The moment we head on to a cafe or a breakfast joint these days, we are bombarded with a long list of beverages which include an assortment of coffees, tea and other sugary drinks that promise to refresh our Monday-blues thirst with a shot of energizing elixir! It is not only spoiling to have so many options, but in fact confusing to say the least when the waiter/waitress keenly asks us how we would like our coffee.

But coffee, that is dubbed as the Devil’s juice (which I believe, further adds to its charm) is known to be bad for our health when drunk too much. So, we analysed and researched and downloaded some of the data of caffeine in various drinks like tea, soda and different blends of coffee pasted them on an Excel sheet and then used Proc Import to feed and read the data into SAS datasets.

The problem we faced next was that each category still had a few too many drinks to get a quick mental grasp on the matter (i.e. typically 100+). So, we took a two stage approach. The first thing we did was to plot just a handful of drinks in each of the categories which we then recognized as we have or want to drink, and then we plotted the entire list. The images of the small subset graphs are attached below:

 

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Here’s a graph of caffeine content in the most popular coffees:

2

 

3

Here is the complete list of data for all coffees:

Drink

Fluid ounces

Caffeine

mg Caffeine

  (mg)per fluid oz
Nescafe Ice Java0.9100117.6
Stok Black Coffee Shots0.44090.9
Coffee Crave Fearless Black1284470.3
Chameleon Cold Brew Coffee32216067.5
Black Insomnia Coffee1270258.5
Death Wish Coffee1266055
Coffee (Espresso)1.57751.3
Killer Coffee8.543050.8
Biggby Espresso210050
Gourmesso Coffee Pods1.46548.1
Bizzy Cold Brew1675046.9
Peet’s Coffee Espresso1.57046.7
Nespresso Coffee Capsules1.46044.4
Robusta Coffee826533.1
Gloria Jean’s Coffee26733
Shock Coffee Triple Latte823128.9
Chameleon Cold Brew RTD Coffee1027027
Stumptown Cold Brew Coffee10.527926.6
Long Black615425.7
Greek Coffee (Metrios)25025
Turkish Coffee25025
Illy Issimo Cafe6.815522.8
Seattle’s Best Brewed Coffee1226021.7
Stumptown Cold Brew Chocolate + Milk1634021.2
Coffee Bean & Tea leaf Coffee1633320.8
Starbucks Grande Coffee1633020.6
Coffee (Brewed)816320.4
High Brew Coffee816320.4
Stumptown Cold Brew + Milk1631919.9
Starbucks Doubleshot6.512519.2
Caribou Brewed Coffee1630519.1
Black Medicine Iced Coffee1120618.7
TrueStart Performance Coffee5.19518.6
Costa Coffee15.227718.2
Bulletproof Coffee814518.1
Don Tomas Estate Coffee814518.1
Starbucks Via Ready Brew813516.9
Peet’s Brewed Coffee1626716.7
Gold Peak Coffee812615.8
Cafe Viva Probiotic Coffee812515.6
Dunkin’ Donuts Brewed Coffee1421015
K-Cup Coffee812015
Keurig Vue Pack1218015
Starbucks Grande Caffe Americano1622514.1
Starbucks Iced Espresso1622514.1
Starbucks Latte Macchiato1622514.1
Barista Bros Iced Coffee16.921913
Einstein Bros Coffee1620612.9
Americano Coffee1215412.8
Cappuccino1215412.8
Dutch Bros. Coffee2025612.8
Caffe Mocha1215212.7
Starbucks Protein and Coffee1114012.7
Biggby Brewed Coffee1620012.5
Starbucks Cold Brew Coffee1620012.5
Dunkin’ Donuts Iced Coffee2429712.4
Biggby Iced Coffee1619212
Panera Bread Coffee1618911.8
Pronto Coffee66711.2
Muscle Milk Coffee House1112010.9
Starbucks Grande Caffe Mocha1617510.9
Dunkin’ Donuts Latte1415110.8
Dunkin’ Donuts Mocha1415110.8
Vita Coco Cafe11.112010.8
Starbucks Discoveries Caffe Mocha50.753910.6
Starbucks Bottled Iced Coffee1111510.5
McDonalds (McCafe) Mocha1616710.4
Peet’s Caffe Mocha1616510.3
Peet’s Iced Mocha1616510.3
Tim Hortons Large Brewed Coffee2020010
Baskin Robbins Cappuccino Blast242349.8
Starbucks Doubleshot Energy + Coffee151459.7
Latte161549.6
Dare Iced Coffee16.91609.5
Starbucks Bottled Frappuccino9.5909.5
Peet’s Iced Coffee161509.4
Starbucks Grande Caffe Latte161509.4
Starbucks Grande Cappuccino161509.4
Flat White8.5779.1
McDonalds Coffee161459.1
McDonalds Iced Coffee222009.1
V Double Espresso Iced Coffee16.11479.1
Bean and Body Coffee8729
McDonalds (McCafe) Latte161428.9
Peet’s Caffe Americano161408.8
Peet’s Caffe Latte161408.8
Peet’s Cappuccino161408.8
Peet’s Iced Latte161408.8
Skinny Cow Iced Coffee8708.8
International Delight Iced Coffee8658.1
Starbucks Verismo Coffee Pods8607.5
Coffee (Instant)8577.1
Zola Coconut Water Espresso17.51257.1
Real Beanz Iced Coffee9.5666.9
Caffe Nero Coffee12806.7
Chick-fil-A Iced Coffee14946.7
Peet’s Decaf Espresso1.5106.7
Dunkin’ Donuts Iced Latte241516.3
Biggby Creamy Lattes161006.2
Biggby Frozen Lattes161006.2
Big Train Java Chip Ice Coffee8496.1
CoolBrew Coffee10606
Tim Hortons Small English Toffee Coffee10606
Tim Hortons Small French Vanilla Coffee10606
Dunkin’ Donuts Dunkaccino14835.9
Svelte Cappuccino Protein Shake11655.9
SlimFast Cappuccino Delight Shake10404
Choffy (roasted cacao)6233.8
Starbucks Refreshers16503.1
Indulgio Cappuccino8202.5
Starbucks Decaf Coffee16251.6
Coffee Leaf Tea8121.5
Dunkin’ Donuts Coolatta24180.8
Nescafe’ Ricoffy860.8
Arby’s Jamocha Shake16120.7
Coffee (Decaf, Brewed)860.7
Coffee (Decaf, Instant)830.3

Detailed data on all kinds of popular sodas:

DrinkFluid ouncesCaffeinemg Caffeine
(mg)per fluid oz
Bawls Exxtra161509.4
Blink Energy Water16.91508.9
Flatt Cola8658.1
Afri Cola12897.4
Fritz Kola11.2837.4
Premium Cola11.2837.4
Hansen’s Diet Red8.3576.9
Bawls10646.4
Bawls Orange10646.4
Bawls Cherry161006.2
Bawls Root Beer161006.2
Mountain Dew Game Fuel201216
Monster Mutant201155.8
Pepsi Max12695.8
Ski Soda12695.8
Sun Drop Soda12645.3
Mountain Dew Black Label16835.2
DOC 360201005
Mountain Dew Voltage12554.6
Diet Mountain Dew12544.5
Mountain Dew12544.5
Mountain Dew Baja Blast16724.5
Mountain Dew Live Wire12544.5
Pepsi One12544.5
Cult Cola16.9754.4
Surge Citrus Soda16694.3
Dr Pepper 1012514.2
Mello Yello12514.2
Mello Yello Zero12514.2
Starbucks Refreshers Canned12504.2
Cheerwine12484
Diet Cheerwine12484
Diet RC Cola12473.9
Diet Coke12463.8
Diet Coke Plus12453.8
Diet Coke with Lemon12453.8
Diet Coke with Lime12463.8
Diet Vanilla Coke12453.8
Hint Caffeine Kick Water16603.8
Soda Stream16603.8
TAB Diet Cola12453.8
Zevia Cola12453.8
RC Cola12433.6
RC Cola, Cherry12433.6
Shasta Cola12433.6
Diet Pepsi UK, AU, NZ12433.5
Diet Sunkist Orange Soda12423.5
Faygo Cola12423.5
Pepsi Max UK, NZ, AU12433.5
Diet Dr Pepper12413.4
Dr Pepper12413.4
Sunkist Orange Soda12413.4
Sunkist Sparkling Lemonade12413.4
Sunkist Ten20683.4
Diet Mr. Pibb12403.3
Honest Professor Fizz12403.3
Kickapoo Soda: Joy Juice & Fruit Shine12403.3
Pibb Xtra12403.3
Pibb Zero12403.3
Diet Dr Pepper Cherry12393.2
Diet Dr Pepper Cherry Vanilla12393.2
Diet Ruby Red Squirt12393.2
Diet Wild Cherry Pepsi12383.2
Dr Pepper Cherry12393.2
Dr Pepper Cherry Vanilla12393.2
Jazz Caramel Cream12383.2
Pepsi Cola12383.2
Pepsi Diet Lemon12383.2
Pepsi Diet Lime12383.2
Pepsi Diet Vanilla12383.2
Pepsi Throwback12383.2
Pepsi True7.5243.2
Ruby Red Squirt12393.2
TK Diet Cola12383.2
Wild Cherry Pepsi12383.2
Ale 8 112373.1
Crystal Pepsi20633.1
Inca Kola16503.1
Red Flash12373.1
Co-Operative Diet Cola16.9503
Double Cola12363
1893 Cola12342.8
Big Red Soda12342.8
Caffeinated Club Soda12342.8
Cherry Coke12342.8
Cherry Coke Zero12342.8
Coca-Cola Classic12342.8
Coke Zero12342.8
Diet Cherry Coca-Cola12342.8
Diet Coke with Splenda12342.8
Diet Pepsi12342.8
Vanilla Coke12342.8
Pepsi Next12322.7
Slurpee16402.5
A&W Cream Soda12292.4
Coca-Cola Life12282.3
Red Rock Cola12262.2
Barq’s Root Beer12221.8
Diet A&W Cream Soda12221.8
Pepsi Slurpee8141.8
Faygo Moon Mist12201.6
PC Cola Diet12131.1
PC Cola12121
Ritz Cola12100.9
Canada Dry Green Tea Ginger Ale1290.8
Boost Nutritional Drink850.6
7-Up1200
A&W Root Beer1200
Barq’s Red Creme Soda1200
Coca-Cola caffeine free1200
Diet Barq’s Root Beer1200
Fanta1200
Fresca1200
Ginger Ale or Ginger Beer1200
IBC Root Beer1200
Kinley Soda1200
Mug Root Beer1200
Orange Crush2000
Pepsi Caffeine Free1200
Sprite1200
Squirt Soda1200
Tonic Water11.900
Tropicana Twister Soda2000
Vernors Ginger Ale1200

While we are not too big a fan of the dancing bologna to be added into serious graphs, but in this we figured we would add a few minimalistic pictures of the drinks to make it easier to be distinguished.

Feel free to share how well your favourite drink fared in this very hotly debated list. And for more interesting data analysis news and Big Data courses stay tuned with DexLab Analytics. 

What Makes Artificial Intelligence So Incredibly Powerful?

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What Makes Artificial Intelligence So Incredibly Powerful?

Do you also feel that Artificial Intelligence (AI) is getting eerily powerful day-by-day? That is because the structures of Artificial Intelligence exploit the very fundamental laws of physics and of the universe as per latest research.

These new findings help to answer a long-awaited mystery about a category of AI that employs an interesting strategy called deep learning. These are programs based on deep neural networks hence, the name deep learning. The way this works is that they have multi-layered algorithms in which the lower-level calculations feed into the higher level ones within the hierarchy. These deep neural networks often perform surprisingly well when it comes to solving problems which are highly complex, like beating the world’s best player of a strategic board game called Go or categorising cat photos, however no one truly knows why… Continue reading “What Makes Artificial Intelligence So Incredibly Powerful?”

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