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Introduction to Text Mining: WhatsApp Chats (Part 2)

How many times has it occurred to you that maybe, maybe if you had texted that person an hour ago, you probably would have gotten a reply? What we are going to do today is use Python to answer that question. (And yes, there will be probabilities!). Let’s us learn to Analyze Whatsapp Chats using Python.Once you’re done with this tutorial, you’ll have a working system capable of plotting the distribution of texts exchanged by you and someone else, divided into 1-hour intervals.Note: If you haven’t checked out Part 1 yet, you can find it here: Introduction to Text Mining in WhatsApp Chats using Python- Part 1Without further delay, let’s lift this baby off the ground!Dependencies (heaven?)I know, I know! But there’s nothing we can do about that, right?If you had followed the previous tutorial, you already have the dependencies. If you are starting afresh, fire up a terminal and go:pip install matplotlibThat’s it. Python is a powerful language and you’ll see how we can do basic text manipulation with what the language provides by default. Isn’t that heaven?CodeGo to or make a directory named WHATEVER-YOU-LIKE and open it in your favorite editor. Of course, you’ll need to have a text file containing your WhatsApp chat with someone in order to complete this tutorial. Detailed instructions on how to Analyze Whatsapp Chats using Python that can be found here:WhatsApp FAQ - Saving your chat historySaving your chat history - Your WhatsApp chats are automatically backed up and saved daily to your phone's memory…faq.whatsapp.comOnce you have that .txt file, you are ready to move on. In a file named — timing.py (or whatever) — follow the steps below:Step 1. Import dependenciesFirst things first, we gotta summon the libraries we are going to use:import re import sys import matplotlib.pyplot as plt If you remember, in the first part of this series, we used RegExp to match and filter out the Time and Date metadata available in an exported chat file. We are going to do a similar thing this time with only one exception: We won’t discard that information.We need sys to be able to parse command-line arguments and execute our script on a chat file by providing its name.And, you guessed it right! We need matplotlib for plotting.Step 2. Read and split file contents by new lineTo do this, we will use the splitlines method attached to every string.def split_text(filename):   """   Split file contents by newline.   """   chat = open(filename)   chatText = chat.read()   return chatText.splitlines() We open a file, read its contents and then split the file by a newline character. (Can you think of how the splitlines method is implemented under the hood?)Wrapping up tasks like these in a method is considered good practice and it makes your code easier to comprehend and maintain.Step 3. Distribute chats into AM and PM bucketsSince, WhatsApp stores messages using 12-hour format, we will have to use two buckets, AM and PM, to collect the time information attached to every text.def distributeByAmPm(linesText):   AM, PM = [], []   # RegExp to extract Time information   timeRegex = re.compile("(\d+(:)\d+)(\s)(\w+)")    for index, line in enumerate(linesText):       matches = re.findall(timeRegex, line)       if(len(matches) > 0):           # match now contains ('6:10', ':', ' ', 'PM')           match = matches[0]           if "AM" in match:               AM.append(match[0])           else:               PM.append(match[0])    return AM, PM We need to pass the splitted lines we got in the previous step to distribute_by_am_pm to get the distributed buckets. Inside it, we first compile a RegExp that will match the time string in every text. As a brief reminder, this is how text looks like in the exported file:10/09/16, 4:10 PM - Person 2: In an hour, maybe?And that’s how the RegExp maps the pattern to the original string: RegExp at work.Later, we simply use an if statement to correctly distribute the time strings into AM and PM buckets. The two buckets are then returned.Step 4. Group time strings into 1-hour intervalsHere, we’ll first create a skeleton container — a dictionary — to contain the 1-hour intervals. The key will represent the hour, and the value will represent the number of texts shared within that interval. To illustrate, this is how it’ll look, eventually:First, let’s create the skeleton container with this fairly straightforward code:time_groups = {} for i in range(24):  time_groups[str(i)] = 0  # skeleton container Then, we loop through each bucket — AM & PM — and increment the correct count in our time_groups container.# if the hour is in AM for time in AM:       current_hour = get_hour(time)       if current_hour == 12:           current_hour = 0  # Because we represent 12AM as 0 in our container add_to_time_groups(current_hour) For all time strings in AM bucket, we first grab the current_hour (and ignore the minute information because we are grouping by Hour. ) Then, if the hour is 12, we set it to 0 to map 12 AM →0 on the clock.The two helper functions, get_hour and add_to_time_groups are defined below:def add_to_time_groups(current_hour):       current_hour = str(current_hour)       time_groups[current_hour] += 1 def get_hour(time):       return int(time.split(":")[0]) We follow a similar procedure for PM with just one caveat. We add 12 to every hour (except 12pm) to map 1 pm →13 on the clock and so on.# Similarly for PM for time in PM:       current_hour = get_hour(time) if current_hour > 12:           continue # In case it has captured something else, ignore if current_hour != 12:           current_hour += 12 add_to_time_groups(current_hour) That’s it! That completes this step. I am leaving “wrapping this into a function” as an exercise to the reader. Check the Source code on GitHub to see how it would look, if you get stuck!Now that we have defined almost every single bit of our API, let’s define an analyze method that we can call each time we want to run a chat file through our defined pipeline.Step 5. Analyze!def analyze(name):   splitted_lines = split_text(name + '.txt') # Distribute into AM and PM   AM, PM = distributeByAmPm(splitted_lines) # Now group time into 1-hour intervals   time_groups = groupByHour(AM, PM) # Plot   plot_graph(time_groups, name)Notice how clean it looks? Try method-ifying your existing code and see how it goes!The plot_graph method is as below: def plot_graph(time_groups, name):   plt.bar(range(len(time_groups)), list(       time_groups.values()), align='center')   plt.xticks(range(len(time_groups)), list(time_groups.keys()))   # Add xlabel, ylabel, etc. yourself!   plt.show() Step 6. RUNFire a terminal and type (the file name may vary):# python file-name.py chat-file-name $ python timing.py amfa Timing Analysis — Chat with AmfaThe massive towers at 21, 22, 23, 0, 1, 2 intervals indicate that Amfa usually is an active texter late in the day and it’s highly unlikely I’ll get a reply at 10 in the morning. Isn’t that cool?ConclusionIn this tutorial, we learned how simple and easy it is to define complex constructs and tasks in Python and how RegExp can save our souls a lot of trouble. We also learned method-ifying our code is great practice and that Amfa is a night owl and so-not a morning person!As always, the entire code is on GitHub and your shenanigans are always welcome!
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Introduction to Text Mining: WhatsApp Chats (Part 2) 3729 Introduction to Text Mining: WhatsApp Chats (Part 2) Blog
Abhishek Soni 11 Feb 2019
How many times has it occurred to you that maybe, maybe if you had texted that person an hour ago, you probably would have gotten a reply? What we are going to do today is use Python to answer that qu...
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A Guide to Understanding Gradient Boosting Machines: Lightgbm and Xgboost

I have, in the past, used and tuned models without knowing what they do. I have mostly been successful at this because most of them had just a few parameters that needed tuning like learning rate, no. of iterations, alpha or lambda and it's easy to guess how they might affect the model.So, when I came across LightGBM and XGBoost during a Kaggle challenge, I thought of doing the same with them too. I found it pretty complicated to understand the theory behind them so I tried to get away with using them as black-boxes.But I soon found out that I can’t. Its because they have a HUGE number of hyperparameters.. ones that can make or break your model! To top it off, their default setting is often not the optimal one. So, in order to effectively use the model, I had to get at least a high-level understanding of what each parameter represents and understand which ones might be the most important.The motive behind this articleMy aim is to give you that quick, high-level, working knowledge of Gradient Boosting Machines (GBM) and making you understand Gradient boosting through LightGBM and XGBoost. This way you will be able to tell what’s happening in the algorithm, what parameters you should tweak to make it better and go directly to implementing them in your own analysis.It's great if you want to know more about the theory and the math behind gradient boosting. First, let me explain to you what is Gradient boosting is and then point you to some excellent resources to understand the theory and the math behind GBM using the parameters of XGBoost and LightGBM.What is Boosting?Boosting refers to a group of algorithms which transforms weak learner to strong learners.Boosting algorithms are classified into: Gradient BoostingXGBoostAdaBoost etc.What is Gradient Boosting in Machine Learning: Gradient boosting is a machine learning technique for regression and classification problems which constructs a prediction model in the form of an ensemble of weak prediction models.Elements in Gradient Boosting AlgorithmBasically, Gradient boosting Algorithm involves three elements:A loss function to be optimized.Weak learner to make predictions.An additive model to add weak learners to minimize the loss function.In the article, “A Kaggle Master Explains Gradient Boosting”, the author quotes his fellow Kaggler, Mike Kim says-My only goal is to gradient boost over myself of yesterday. And to repeat this every day with an unconquerable spirit.With each passing day, we aim to improve ourselves by focusing on the mistakes of yesterday.And you know what? — GBMs do that too!An ensemble of predictorsGBMs do it by creating an ensemble of predictors. Each one of those predictors is sequentially built by focusing on the mistakes of the previous one.What’s an ensemble?It is simply a group of items viewed as a whole rather than individually.Now, back to the explanation...A GBM basically creates lots of individual predictors and each of them tries to predict the true label. Then, it gives its final prediction by averaging all those individual predictions (note however that it is not a simple average but a weighted average).Q-  “Averaging the predictions made by lots of predictors”.. that sounds like Random Forest!That is in fact what an ensemble method is. And random forests and gradient boosting machines are just 2 types of ensemble methods.One important difference between the two is that the predictors used in Random forest are independent of each other whereas the ones used in gradient boosting machines are built sequentially where each one tries to improve upon the mistakes made by its predecessor.You should check out the concept of bagging and boosting. So, check out this quick explanation to do that.Q: Okay, so how does the algorithm decide the number of predictors to put in the ensemble?It does not. We do. And that brings us to our first important parameter — n_estimators : We pass the number of predictors that we want the GBM to build inside the n_estimators parameter. The default number is 100.So, let’s talk about these individual predictors now.In theory, these predictors can be any regressor or classifier but in practice, decision trees give the best results.The sklearn API for LightGBM provides a parameter- boosting_type and the API for XGBoost has parameter- booster to change this predictor algorithm. You can choose from —  gbdt, dart, goss, rf (LightGBM) or gbtree, gblinear or dart (XGBoost). [Note however that a decision tree, almost always, outperforms the other options by a fairly large margin. The good thing is that it is the default setting for this parameter; so you don’t have to worry about it.]Creating weak predictorsWe also want these predictors to be weak. A weak predictor is simply a prediction model that performs better than random guessing.Q: Wait a second.. that seems backwards. Don’t we want to have strong predictors that can make good guesses? Nope. We want the individual predictors to be weak so that the overall ensemble becomes strong. This is because every predictor is going to focus on the observations that the one preceding it got wrong. When we use a weak predictor, these mislabelled observations tend to have some learnable information which the next predictor can learn. Whereas if the predictor were already strong, it would be likely that the mislabelled observations are just noise or nuances of that sample data. In such a case the model will just be overfitting to the training data. Also note that if the predictors are just too weak, it might not even be possible to build a strong ensemble out of them.Now back to creating a weak predictor.. this seems like a good area to hyperparameterise.These are the parameters that we need to tune to make the right predictors (which are decision trees):max_depth (both XGBoost and LightGBM): This provides the maximum depth that each decision tree is allowed to have. A smaller value signifies a weaker predictor.min_split_gain (LightGBM), gamma (XGBoost): Minimum loss reduction required to make a further partition on a leaf node of the tree. A lower value will result in deeper trees.num_leaves (LightGBM): Maximum tree leaves for base learners. A higher value results in deeper trees.min_child_samples (LightGBM): Minimum number of data needed in a child (leaf). According to the LightGBM docs, this is a very important parameter to prevent overfitting.Note: These are also the parameters that you can tune to control overfitting.The subtree marked in red has a leaf node with 1 data in it. So, that subtree can’t be generated as 1 < `min_child_samples` for the above caseSubsamplingEven after we do all this, it might just happen that some trees in the ensemble are highly correlated.Q: Excuse me, what do you mean by highly correlated trees?I mean decision trees that are similar in structure because of similar splits based on same features. This means that the ensemble as a whole is going to store less amount of information than what it could have stored if the trees were different. So we want our trees to be as little correlated as possible.To combat this problem, we subsample the data rows and columns before each iteration and train the tree on this subsample. These are the relevant parameters to look out for:subsample (both XGBoost and LightGBM): This specifies the fraction of rows to consider at each subsampling stage. By default, it is set to 1, which means no subsampling.colsample_bytree (both XGBoost and LightGBM): This specifies the fraction of columns to consider at each subsampling stage. By default, it is set to 1, which means no subsampling.subsample_freq (LightGBM): This specifies that bagging should be performed after every k iterations. By default, it is set to 0. So make sure that you set it to some non-zero value if you want to enable subsampling.That is it. Now you have a good overview of the whole story of how a GBM works. There are 2 more important parameters though which I couldn’t fit into the story. So, here they are —learning_rate (both XGBoost and LightGBM): It is also called shrinkage. The effect of using it is that learning is slowed down, in turn requiring more trees to be added to the ensemble. This gives the model a regularisation effect.class_weight (LightGBM): This parameter is extremely important for multi-class classification tasks when we have imbalanced classes. I recently participated in a Kaggle competition where simply setting this parameter’s value to balanced caused my solution to jump from the top 50% of the leaderboard to the top 10%.You can check out the sklearn API for LightGBM here and that for XGBoost here.Finding the best set of hyperparametersYou can use sklearn’s RandomizedSearchCV in order to find a good set of hyperparameters. It will randomly search through a subset of all possible combinations of the hyperparameters and return the best possible set of hyperparameters(or at least something close to the best).But if you wish to go even further, you could look around the hyperparameter set that it returns using GridSearchCV. Grid search will train the model using every possible hyperparameter combination and return the best set. Note that since it tries every possible combination, it can be expensive to run.Where can you use these algorithms?They are good at effectively modeling any kind of structured tabular data. Multiple winning solutions of Kaggle competitions use them. Here’s a list of Kaggle competitions where LightGBM was used in the winning model.They are simpler to implement than many other stacked regression techniques and they easily give better results too.There is another class of tree ensembles called — Random Forests. While GBMs are a type of boosting algorithm, this is a bagging algorithm (did you check the link about bagging and boosting that I mentioned above?). So, despite being implemented using decision trees like GBMs, Random Forests are much different from them. Random Forests are great because they will generally give you a good enough result with the default parameter settings, unlike XGBoost and LightGBM which require tuning. But once tuned, XGBoost and LightGBM are much more likely to perform better.Below diagram is the sample of Random ForestsAlright. So now you know all about the parameters that you need to in order to successfully use XGBoost or LightGBM to model your dataset!Before I finish off, here are a few links that you can follow to understand the theory and the math behind gradient boosting (in order of my preference) — “How to explain Gradient Boosting” by Terrance Parr and Jeremy HowardThis is a very lengthy, comprehensive and excellent series of articles that try to explain the concept to people with no prior knowledge of math or the theory behind it.“A Kaggle Master Explains Gradient Boosting” by Ben GormanA very intuitive introduction to gradient boosting. (P.S: It is the very article that gave me the quote which I used in the beginning)“A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning” by Jason BrownleeIt has a little bit of history, lots of links to follow up on and a gentle explanation with no math (!).You can also take up Machine learning courses to understand these things better.
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A Guide to Understanding Gradient Boosting Machines:  Lightgbm and Xgboost

A Guide to Understanding Gradient Boosting Machines: Lightgbm and Xgboost

Tutorials
I have, in the past, used and tuned models without knowing what they do. I have mostly been successful at this because most of them had just a few parameters that needed tuning like learning rate, no....
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Creating Your First Offline Payment Wallet With Django (Part-3)

In part-2 of this article, we have successfully created a login system for the users now let us work on providing the main functionality which is the wallet management and get things working.Let us look into the steps for Django offline wallet managementStep 1:  Display the balance of each personThe very first thing before starting on with the transaction is the need to display the balance of each person which we will be storing in a table, so let’s create it, in accounts/models.py we create a model called balance.from django.contrib.auth.models import User class balance(models.Model):         user = models.ForeignKey(User)         balance = models.IntegerField(default = 0) Notice that Username will be the ForeignKey as we are mapping balance with the given user. When we have successfully created the table now we migrate, on terminal we writepython manage.py makemigrations python manage.py migrate Step 2:  Display the balance of a particular userOur next step will be to display the balance of a particular user in the profile dashboard which we have created in the previous part, now let us write a query to fetch balance for the user and send it to the front-end for displaying it. Below is the updated code in accounts/views.py to achieve this.from models import balance def profile(request):      user = balance.objects.get(user=request.user      return render(request,'profile.html',{"balance":user.balance})Please note that we are fetching the balance of the user in from the database at the time of showing the profile but we have not saved it into database till now so this may break our code so to make this work we have to insert this record into the balance table at the time of profile creation of the user in the signup function so we add the following lines to the code inside the if block in the signup function which will set the default balance of the user to 0 initially in the balance model.instance = balance(user = request.user, balance = 0) instance.save()Step 3:  Display the balance to the userNow we have to display the balance to the user, so to do this we just have to display the variable balance which we have sent int the profile function in profile.html.Balance: {{balance}} Adding above line of code in the file should now display the balance of the user on logging in.Step 4:  Setting up offline paymentWe are able to display the balance to the user, Now we come to our last step of setting up offline payment with Django, wherein the user can transfer an amount from his wallet to wallet to some other user which will lead to deduction of the amount from the wallet of the user and addition of amount in the wallet of another user, so let us try to implement it.Lets start with the front-end where we make a form where the user can insert the username and amount for making the transfer. So we add following lines to profile.html. Now we again refresh the page to see the following pageLet’s write the back-end for handling this form now, as discussed earlier when a transfer request is made we have to deduct the amount from the user and put it in wallet of some other user, which is just a matter of few queries, lets see the code for thisdef profile(request):  msg=""  if request.method == "POST":    try:      username = request.POST["username"]      amount = request.POST["amount"]      senderUser= User.objects.get(username=request.user.username)      receiverUser=User.objects.get(username=username)      sender = balance.objects.get(user = senderUser)      receiver = balance.objects.get(user = receiverUser)                 sender.balance = sender.balance - int(amount)      receiver.balance = receiver.balance + int(amount)      sender.save()      receiver.save()      msg = "Transaction Success"    except Exception as e:      print(e)      msg = "Transaction Failure, Please check and try again"  user = balance.objects.get(user=request.user)  return render(request,'profile.html',{"balance":user.balance,"msg":msg})In the above code in the try block we are getting the form data from the user try to search for the credentials for the receiver and the sender and fetch their balance from the balance table and add the amount to be transferred from to the receiver’s wallet and deduct the same from the sender’s wallet and we return the transaction message to the profile.html which can be displayed in the same way as discussed earlier. That concludes this part of the article, the complete code for this can be found on this repository , please feel free to make any issue or pull request, Further steps for Implementing offline payment gateway in Django may include:Adding transaction history page for a particular user which can be done by saving the transactions history in a new table having fields sender, receiver and amount.Display more accurate error messages on transaction failure.Adding features like notification and messaging in the profile dashboard.Making the pages look good using CSS and Bootstrap.Any other enhancements you may think up.Feel free to fork and contribute to the repository and leave a comment here if help is needed. That is all for this article, I hope this article helped you to learn how to set up an offline payment with Django. Thanks for reading.
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Creating Your First Offline Payment Wallet With Django  (Part-3)

Creating Your First Offline Payment Wallet With Django (Part-3)

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In part-2 of this article, we have successfully created a login system for the users now let us work on providing the main functionality which is the wallet management and get things working.Let us lo...
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How to Install Node Js on Mac

Node.js is an open-source, cross-platform JavaScript run-time environment that executes JavaScript code outside of a browser. Node.js lets developers use JavaScript to develop wide variety of applications like network applications, command line tools, web api, web applicationsIn this document, we will cover installation procedure of nodejs on mac operating systemPrerequisitesThis guide assumes that you are using mac os. Before you begin, you should have a user account with installation privileges and should have unrestricted access to all mentioned web sites in this document. Audience:This document can be referred by anyone who wants to install latest nodejs on macSystem requirementsmacOS >= 10.104 GB RAM10 GB free spaceInstallation Procedure 1.Download1.Visit nodejs download page here2.Click on macOS Installer to download the latest version of node installable package.2.Install1.Click on the download node-vxx.xx.xx.pkg ( for example node-v10.15.0.pkg) in previous step to start the installation which brings up below screen. Please click continue2.By clicking continue in previous step you will be asked to accept license, please click continue3.Please accept the agreement by clicking Agree4.Click continue5.Click install, which would prompt you for the credentials6.Provide username and password and click Install Software 7.On successful installation you will see the below screen which shows the summary of the installation.To access the node and npm executable from terminal make sure /usr/local/bin is in your $PATH. You can verify that by running echo $PATH command in terminal 3.Testing InstallationOpen terminal and run below command to test node node -vYou should see an output like below (Note: Your version may vary depending on your date of installing as nodejs team make an aggressive release but make sure your node version is > v10.0.0)Open terminal and run below command to test npm npm -v You should see an output like below (Note: Your version may vary depending on your date of installing as nodejs team make an aggressive release but make sure your npm version is  >5 )Running the first Hello World applicationOpen any text editor and write the following code console.log("Hello World Node!!")Save the file as helloworld.jsOpen command prompt and navigate to the folder where you save helloworld.js and  run below command node helloworld.jsHow it worksnode command line with file name as an argument will load, read, compile and execute the instructions from the file and execute them. Since we have one line of JS code which prints the text “Hello World Node” to console inside helloworld.js file you see the output as shown in above screenshot.
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How to Install Node Js on Mac

How to Install Node Js on Mac

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Node.js is an open-source, cross-platform JavaScript run-time environment that executes JavaScript code outside of a browser. Node.js lets developers use JavaScript to develop wide variety of applicat...
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How to Install Node Js on Windows

Node.js is an open-source, cross-platform JavaScript run-time environment that executes JavaScript code outside of a browser. Node.js lets developers use JavaScript to develop wide variety of applications like network applications, command line tools, web api, web applications In this document, we will cover installation procedure of nodejs on windows 10 operating systemPrerequisitesThis guide assumes that you are using Windows 10. Before you begin, you should have a user account with installation privileges and should have unrestricted access to all mentioned web sites in this document.Audience:This document can be referred by anyone who wants to install latest nodejs on windows 10 System requirementsWindows 10 OS4 GB RAM10 GB free space Installation Procedure1. DownloadVisit nodejs download page here     2. Click on windows Installer to download the latest version of node installer.2. Install  1. Click on the downloaded node-vxx.xx.xx.msi ( for example node-v10.15.0.msi) in previous step to start the installation which brings up below screen. Please click Next   2. By clicking next in previous step you will be asked to accept license, please accept by clicking checkbox and click Next 3. Click Next4.Click Next5. Click Install, this may need elevated permissions provided necessary rights requested6. Click Finish3.Testing InstallationOpen command prompt and run below command to test nodenode -v You should see an output like below (Note: Your version may vary depending on your date of installing as nodejs team make an aggressive release but make sure            your node version is > v10.0.0)Open command prompt and run below command to test npmnpm -vYou should see an output like below (Note: Your version may vary depending on your date of installing as nodejs team make an aggressive release but make sure your npm version is  >5 )Uninstall nodejsPress windows + R  to open run and type appwiz.cpl  and press ok.This will open Programs and Features the look for node.jsDouble click node.js or right click and select uninstall which will prompt as below and then choose YesNodejs uninstallation process will initiate and would ask you to authorize the same via user control. Choose Yes, this will take a while and completeRunning the first Hello World applicationOpen any text editor and write the following codeconsole.log("Hello World Node!!")Save the file as helloworld.jsOpen command prompt and navigate to the folder where you save helloworld.js and  run below commandnode helloworld.jsHow it worksnode command line with file name as an argument will load, read, compile and execute the instructions from the file and execute them. Since we have one line of JS code which prints the text “Hello World Node” to console inside helloworld.js file you see the output as shown in above screenshot
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How to Install Node Js on Windows

How to Install Node Js on Windows

Tutorials
Node.js is an open-source, cross-platform JavaScript run-time environment that executes JavaScript code outside of a browser. Node.js lets developers use JavaScript to develop wide variety of applicat...
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How to Upgrade to the Latest Version of Node Js (Linux, Ubuntu, Osx, Others)

IntroductionJust like all the other vast number of open-source frameworks, Node.js is one of the highly recommended technologies. Minor updates turn out at regular intervals to keep the architecture steady and solid as well as provides enhanced security among most of the version branches.  There are many upcoming techniques to update Node JS to the latest version. Each method is designed keeping in mind the various operating systems. So there is no reason to hold you back from upgrading Node.js to its latest version. Due to this Node JS development company also focusing on this. Here is an accumulated list of ways to introduce the most up to date version of Node. These methods are both simple and effective ways to upgrade to the latest version release. This article explores the upgrades on Linux-based systems, Windows, as well as Mac OS machines. Make sure that before you begin, check the version of Node.js you're using at present. You can do so by merely running the command ‘node – v’ in a command line terminal. Every now and then, when you upgrading to Node JS latest version, you have to make a great deal of edits to the ‘Dockerfile’ as well as ‘circle.yml’ files. This can be a very monotonous and tedious errand.To simplify this undertaking, you can run an ‘update-node’ in the directory which contains most of your archives. You need to enter the new Node.js version as one of the parameters using the command$ update-node One key thing to remember is that ‘update-node’ does not submit or push any changes on its own. You can still manage the outcome of the update. This allows you to initially check what are the updates performed by the ‘update-node’ command. To submit and push for every single change in a repository, you are better off using ‘forany’ command.Here is the list of ways how to update node js to the latest version. It covers all the three primary operating systems: Linux, Windows, and Mac.Ways to update Node JS on LinuxFor Linux users, there are multiple ways or steps to upgrade Node js in your system, three ways to be specific. The first option is probably the highly recommended method. The simplistic nature and highly effective methods are very popular among developers and coders. In worst case scenarios, if you are not able to use the Node Version Manager, you can alternatively use the package manager or the binary packages.Option 1: Updating Node JS with the help of Node Version ManagerA common terminology for Node Version Manager is NVM. It is probably the best way to upgrade to the latest version of the technology. You also need to get your hands on a C++ compiler. You will also need to use libssl-dev package and the build-essential package. You can first run the update and then get the packages. You can run the update by the commands1    sudo apt-get update2    sudo apt-get install build-essential checkinstall libssl-devIn the event you need to update the NVM or install a version of it, you can get the installation script from any of the online portals or website.Once it is installed, you will need to reboot the terminal for the changes to take effect. For checking if the installation was successful you could run the command ‘–v nvm.’ Based on the output, you can figure out if the installation was done correctly. This is a one-time effort after which it is a walk in the park to get Node JS updated to the latest version. For information on the currently installed versions, you can use nvm Isand. To get a list of available versions, you can use nvm Is-remote.Once you are confident about the version details of the Node that you want to install, you can install it by running the command ‘nvm install .’ You can also check out the list of nvm versions and set a default version using alias command.Option 2: Update Node JS version through a Package Manager If at all you are not able to upgrade the Node JS version using NVM, then the next best option is to use a package manager. Node package manager or NPM can be instrumental in discovering, using and sharing codes as well as to manage any dependencies on previous versions of Node JS. The node package manager is part of installation setup for Node JS. To check out the current version of the package manager, you can use the command ‘npm –v.’ For installing the most recent update, you can use the command ‘install npm @ latest –g.’ Once updated, you can check if the operation was successful by running ‘npm –v.’You will also need to have access to the Node Package Manager ‘n module’. You can clear the npm cache with the command ‘npm cache clean –f.’ Once the cache is cleared, run the command which will install the n module, i.e., ‘sudo npm install –g n.’ Now you can choose the most stable version of Node, or you can specify the version using the version number.Option 3: Update Node using Binary Package k(Ubuntu/Debian/CentOS and Linux)This is least sought out path to update the version of Node JS and is probably the last resort. You can go to the official downloads page for Node JS to get your hands on the 32-bit binary file or the 64-bit binary file for Linux. You can use the command ‘wget https://nodejs.org/dist/v6.9.2/node-v6.9.2-linux-x64.tar.xz‘. You can download this file through a browser or with the help of a console. However, it is important to remember that the Node JS version could possibly change with the release of newer updates. Extract the file and install it using ‘xz-utils.’ The next step is to install the binary package using the command ‘1 tar -C /usr/local --strip-components 1 -xJf node-v6.9.2-linux.x64.tar.xz’.Updating the Node JS Version (Windows and Mac OS) with Installers Once you check out the downloads page for Node JS, you can see that there are binary packages for Windows and Mac. You can also use the existing and pre-defined installers, i.e. *.msi for Windows platform and *.pkg for a Mac. This can hasten and ease the installation process. Once you download the file in your local storage and run the file, the wizard takes over and handles the rest of the installation process. Once an update has been downloaded, the version numbers of the Node in the NPM will automatically be updated from the older versions. You can also learn more about the Important Tips and Features in Node.js to Practice here.ConclusionThe above steps will ensure that your version of Node Js is upgraded and the updates are completed. However, this does not make any changes in your packages or modules. Updating the packages along with the dependencies are also necessary. This is required for increasing compatibility levels and enhance security. Sometimes, Node JS modules become obsolete and are not compatible with the latest versions. You can use ‘npm update’ command to update the command modules to their most recent version.
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How to Upgrade to the Latest Version of Node Js  (Linux, Ubuntu, Osx, Others)

How to Upgrade to the Latest Version of Node Js (Linux, Ubuntu, Osx, Others)

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IntroductionJust like all the other vast number of open-source frameworks, Node.js is one of the highly recommended technologies. Minor updates turn out at regular intervals to keep the architecture s...
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Top Artificial Intelligence Implications You Can’t Ignore in 2019

Artificial Intelligence has paved its way to different industries and revamped the business arena. According to International Data Corporation, Artificial Intelligence will aid 40% of digital transformations in 2019, and by 2025, the technology will facilitate 75% of commercial enterprise apps.According to Ibrahim Hadid, Director of Research at the Linux Foundation, ‘2019 is going to be the year of open source Artificial Intelligence’ and Peter Trepp, CEO FaceFirst says, ‘Artificial Intelligence will help elevate in-store customer experiences.’Artificial Intelligence has hooked startups and enterprises, but the technology will soon become an integral and consistent feature in business across industries. The massive potential of AI is evident from the AI industry’s expected market value of $190 billion by 2025.This article explores the top AI implications for business strategies.More Transparency in Artificial IntelligenceArtificial Intelligence aims to behave like humans, in terms of wisdom, intelligence, and decision-making. However, it gives birth to several ethical reservations and cannot be adequately utilized unless blindly trusted. It is assumed that AI accesses users’ data, search history, and other personal data. The misconceptions and inability to comprehend AI’s decision-making is a significant hurdle in its success and will be addressed in 2019. This will also be the beginning of the best AI prediction strategies for 2019.It is difficult to know the pattern adopted by Artificial Intelligence to reach a conclusion. However, it is possible to identify factors which contribute to the decision. In 2019, AI will become more transparent, to build the trust of businesses and consumers.Even businesses are reluctant to adopt Artificial Intelligence, to avoid liabilities in the future. Therefore, we will witness enhanced transparency for maximum utility for AI. Recently IBM revealed its technology to track the decision-making process of AI. It helps in monitoring the decisions, process of deciding, potential biases in data, decision weighting, and more.Enhanced transparency will encourage business to use AI in its processes and foster trust in the consumers’ minds. Artificial Intelligence will Enhance Job OpportunitiesUnlike popular belief that robots and machines will reduce job roles for humans, Artificial Intelligence will create more jobs in 2019 than it will take away, creating a positive impact on the job market. Robotics and AI will create 7 million job opportunities in the United Kingdom, between 2017 and 2037. Research by Gartner shows that AI will take away 1.8 million jobs and generate 2.3 million jobs. Most of the jobs will be created in health, hospitality, and technical services while reducing jobs in logistics, transport, and public administration.Artificial Intelligence shares responsibilities for repetitive tasks. For example, chatbots resolve customer queries about product deliveries, order updates, availability of products, expected delivery time, and so. The data is automatically fetched from the system using keywords. The customer service representatives can spend time on productive tasks instead. However, in some jobs, the final decision remains with humans, such as of doctors. AI helps in data analytics and diagnostics, but the human professionals make the last call.AI will replace humans in administrative work but create more jobs in intellect-based industries, such as healthcare and law. AI Assistants will Become Smarter and Wiser2019 will witness smarter and wiser digital assistants, aided by Artificial Intelligence. This will pave the path of the top Artificial Intelligence future trends for 2019. Users are already familiar will the AI assistants in the firm of Alexa, Cortana, and Siri; therefore, the acceptance of AI assistant will be easier for consumers. According to Global Web Index, 1 out of 5 adults uses voice search in mobile phones at least once a month.Alexa, Cortana, and Siri still have a robotic touch to their conversations. The coming year will see more natural and meaningful discussions with the AI assistants. Don’t be surprised if AI assistants enable online and offline interactions with televisions, refrigerators, and other electronic devices.Siri, Alexa, and other digital assistant perform basic tasks such as making calls, finding meaning, playing songs, or guiding the way. The assistants will become active in 2019 to plan journeys, suggest lunch and dinner menus, arrange calendars, anticipate behaviors, ordering taxis, recommending eateries, and more.The technology allows AI assistants to better understand humans via extensive exposure to users’ communication, behaviors, habits, preferences, interests, and desires. The natural language algorithms will efficiently encode the user’s speech into readable data for computers, enabling AI assistants to help their human users in a wide array of tasks.Be ready to interact with AI assistants at home, work, recreational areas, hospitals, and beyond.Artificial Intelligence is here to stay and grow. 2019 will bring some jaw-dropping AI uses that will leave you stunned.Look for your Artificial Intelligence platform with the best web development company and strengthen your competitive edge today!
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Top Artificial Intelligence Implications You Can’t Ignore in 2019

Top Artificial Intelligence Implications You Can’t Ignore in 2019

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Artificial Intelligence has paved its way to different industries and revamped the business arena. According to International Data Corporation, Artificial Intelligence will aid 40% of digital transfor...
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Hadoop and Its Core Features: The Most Popular Tool of Big Data

If you are looking to learn Hadoop, this is the right stage you have landed at. In this Hadoop tutorial, you can learn from the basics to the advance level in a very simplified manner. Are you looking to learn Hadoop? If yes, then you have landed on the right page. This tutorial will take you through the basics to the advanced level of Hadoop in a very simplified manner. So without wasting much time let’s plunge into the details of Hadoop along with the suitable practical scenarios. In this Hadoop tutorial, we are going to coverHow it all started What is all about big dataBig data and Hadoop: restaurant illustration What is HadoopHadoop solutions Hadoop features Hadoop mandatory services to set upHadoop tutorial: how did it start?Before starting the technical part in this Hadoop tutorial, let’s discuss some exciting story about how Hadoop came into existence. Hadoop was started by two people Doug Cutting and Mike Cafarella, who were in mission mode to build a search engine which could have a capability to index 1 Billion pages. They had undergone research for that, and they came to know that it requires to set up a system which hardware costs half million dollars and a monthly running cost of $30,000, which is a considerable capital expenditure for them. However, soon they realized that it is tough for their Architecture to support one billion of web pages on it.In 2003 they red a paper clip about the Google Distributed File System known as ‘GFS’ which used in Google's production. Now the news on GFS is something exactly they were looking, and it became a solution for their storage problem of the vast amount of data that's get generated in the process of web crawl and indexing.  Later in 2004 google came up with one more invention which is MapReduce into this technical world. These two inventions from Google led to the origin of software called “HADOOP.” Doug has given the following on the google contribution to the development of the Hadoop framework“Google is living a few years in the future and sending the rest of us messages.” So with the above discussion, you would have got an idea about Hadoop and how powerful it is.Hadoop tutorial: what  Big Data is all about?The world is transforming at a rapid speed with the technological advancements in each sector, and we can get the best out of everything. In the olden days we used to have landlines, but now we have moved to smartphones which can perform all most all works like a PC. Similarly, we used to store the data into floppy drives back in the 90’s.  And these floppy drives had been replaced by the hard disks due to its limited storage and processing capability. But now we can store terabytes of data on to Cloud, without bothering about the storage limitations. Let’s consider some crucial sources from where the significant portions of data get generated. Have you heard about IoT? Now it’s been a disruptive technology in all industries. IoT connects all physical devices to the internet or to the other methods, to perform certain things without human intervention in between the Devices. Here let us discuss the best example of IoT is smart Air conditioners in homes. The Smart AC’s are connected to the internet, and it can adjust the temperature inside the room by monitoring the outside temperature.  With this, you could get an idea of how much data has been generated by the IoT devices by the massive no of different devices worldwide and its share in contributing to big data.Now We should consider the main contributor to big data is social media. Social media is one of the biggest reason for the evolution of big data, and it gives the information about population behavior. The following image will give you a clear idea about the amount of data generated by social media every minute.Apart from the data rate of data generating, the second challenge comes with the unstructured or unorganized data sets which make processing a problem.Big Data & Hadoop - AnalogyHere let’s take a restaurant as an example to get a better understanding of the problems related to bid data and how Hadoop solves it.John is a businessman who has opened a  small restaurant. Initially, he had one chef with one food shelf, they started receiving two orders per hour and the restaurant set up was enough for handling orders.Here we are going to compare restaurant scenario with a traditional system where data was used to generated in a consistent way, and our storage system like  RDBMS was capable enough for processing it just like John’s chef. Here in this scenario, the chef is compared with traditional processing and the food shelf is compared with data storage as shown in the above image. After a few months, Bob thought to expand his business and started taking online orders as well by adding few more Cuisines to the menu to serve a large number of people.  The Orders were rose to an alarming level of 10 per hour, and it became tough for a single chef to handle the extra work. Aware of that situation John started to think about the measures that he needs to take for handling the case. The same thing happened with big data; suddenly the data started generating at a rapid rate because of the data growth factors such as social media, etc. now the traditional system, just like a chef in john restaurant unable to cope up with the situation. Thus, there was a need for different kind of solution to tackle the problem.After a lot of research john came up with an idea to increase the no of chefs to four, and everything is functioning well, and they can handle the orders.  But soon this solution was lead to another problem which is a food shelf since four chefs were sharing the single shelf it became a hurdle for them. John started to think again to put a stop to the situation.In the same fashion, to handle the data processing of vast volumes of data many processing units were installed( just like John hired extra chefs to manage the orders). But even in this case setting up additional processing units was not a solution to the problem. Here, the real bottleneck is the central unit for data storage.  In other words, the whole performance of the processing units was driven by the primary Storage. If it is not efficient the entire system would be affected. Hence, there was a storage problem to resolve.  John came up with another idea to divide the chefs into two categories, i.e., junior and senior chefs and assigned each junior chef with a food shelf. Each senior chef was appointed with two junior chefs to prepare meat & sauce.  According to John's plan, among two junior chefs, one will make the meat, and the other will prepare sauce, so both the ingredients will be transferred to the head chef and he will mix both the ingredients to prepare the final order. Hadoop works similarly as John’s restaurant does. The way food shelves distributed in John’s restaurant, in the same fashion the data will be stored in a distributed manner with replications, to give fault tolerance.  For parallel processing, first, the data gets processed by the slave nodes, where, it is stored for a while to obtain intermediate results, and later these intermediate results are merged by the master node to produce the final result.The above analogy must have given you a fair idea about how big data is a problem statement and how Hadoop is a solution for it. As we discussed in the above scenario, there are three significant challenges with Big Data. The first problem is storing a massive amount of data:Saving vast amounts of data in a traditional storage system is not possible. The reason is known to everyone, the storage capacity is limited, but the data is generating at a rapid speed.The second problem is storing diversified data: As we know data storage is a problem, but even there is another problem is associated with it which is storing heterogeneous data. The data is not only colossal, but it presented in different formats such as semi-structured, structured, and unstructured. So you have to make sure that you have a multitasking system to store the diversified data which is generating from different sources and in different formats. The final problem is the processing speed:As we all aware big data consists of a significant number of datasets it is tough to prepare the data in a less span of time.To overcome the storage issue as well as a processing issue there are two components created in Hadoop which are - HDFS & YARN. HDFS stands for Hadoop Distributed File System, it resolves the storage problem by storing the data in a distributed manner, and it is easily scalable. YARN Stands for  Yet Another Resource Negotiator, and it is designed to decrease the processing time drastically. Let’s move ahead and understand what Hadoop is?What is Hadoop?Hadoop is an open source programming structure which is intended to store the colossal volumes of informational collections distributedly on expansive groups of the ware. Hadoop programming structured on a paper discharge by google on MapReduce, and it can apply to all thought of practical programming. Hadoop was produced in Java programming dialect, and Doug Cutting and Michael J. Cafarella structured it.Hadoop Key features:Reliability:When machines are working in tandem mode process if one device fails, there is another device ready to take charge of the responsibility of it and perform the functions of it without any interruption in between. Hadoop is designed with an inbuilt fault tolerance feature which makes it highly reliable. Economical:Hadoop can operate on standard commodity hardware (your PC or Laptop). For instance, in a mini Hadoop cluster, all data nodes requires a standard configuration setup like 5- 10 terabytes hard disk,  Xeon processor and 8-16 GB RAM is enough. So, the cost for Hadoop is very economical and easy to operate on your regular PC or Laptops. And more importantly, Hadoop is an open source software, so, you need not pay costs like licensing. Scalability: Hadoop has the inbuilt capacity of integrating with cloud computing technology. Especially, when Hadoop is installed on cloud technologies you need not consider the storage problem. You can arrange the systems and hardware according to your requirements.Flexibility:Hadoop is very flexible when it comes to its performance in dealing with different methods of data. Hadoop is a flexibility feature to process the different kinds of data such as unstructured, semi-structured, and structured data. The above are the four features which are helping in Hadoop as the best solution for significant data challenges. Let’s move forward and learn what the core components of Hadoop are.Core components of HadoopWhile you are setting up the Hadoop cluster, you will be provided with many services to choose, but among them, two are more mandatory to select which are HDFS (storage) and YARN (processing).  Let’s get more details about these two.HDFS ( Hadoop distributed file system) The main components of HDFS  are Namenode and Datanode. First, let’s discuss the NameNodeYARN:YARN consists of two essential components Resource Manager and Node Manager Resource Manager:It works at the cluster level and takes responsibility to run the master machine. It stores the track of heartbeats from the Node manager. It takes the job submissions and negotiates the first container for executing an application. It consists of two components: Application manager and Scheduler. Conclusion:The above explanations and examples might have given you a brief idea about big data, how does it get generated, problems that related to Big data, and how Hadoop is useful to solve these problems.Happy learning and I will come up with a new post soon.    
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Hadoop and Its Core Features: The Most Popular Tool of Big Data

Hadoop and Its Core Features: The Most Popular Tool of Big Data

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If you are looking to learn Hadoop, this is the right stage you have landed at. In this Hadoop tutorial, you can learn from the basics to the advance level in a very simplified manner. Are you lo...
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Native vs. Cross-Platform Apps – You’ll Be the Winner

Native vs. Cross-platform is an age-old debate. It is a debate that has kept the tech community divided for years. Many claimed to have found the ultimate answer but both cross-platform and native app development technologies are in a constant state of evolution. And due to this changing nature of technologies, it serves to revisit these topics from time to time to find out which of these options is currently in the lead.Both the native and cross-platform apps have a dynamic list of pros and cons. These factors can affect everyone involved with the app, including the app owner, the app users, and the app developers. Now, app developers have preferences based on the technologies they are most comfortable with. Today, however, we will limit the scope of discussion to app owners and users.So, let’s start with the basics.How Important Is the Mobile Application Platform?Apple’s iOS and Google’s Android are by far the biggest and most popular mobile platforms in the world. According to stats, a majority of the global market is captured by Apple and Android stands second.                                                                               Image sourceThe US, for instance, has a bigger iOS market share i.e. 56 per cent compared to 43.5 per cent of Android. Not to forget, there are other smaller platforms such as Windows. This means if you choose your app simply in the global data, you might lose an important target region. That is why you need to choose your app platform wisely. You need to know where your audience is and build your app’s presence on each of those platforms. Now there are two ways to do that - you either build a native app for each platform or create a single cross-platform app supported by multiple platforms. Let’s take a look at how this decision will affect your app:The Basic Difference between Native and Cross-Platform AppNative apps are developed exclusively for a specific platform. These apps are developed in a language compatible with the platform. Apple, for instance, prefers Objective C and Swift for iOS while Google favors Java for Android. Using these acceptable languages, developers can make better use of the innate features of these platforms. A native app developed for Android will not function on iOS and vice versa.Cross-platform apps are compatible with multiple platforms. Due to the market share of Android and iOS, most cross-platform apps are limited to these two operating systems. These apps are developed in HTML and CSS since these standard web technologies are platform independent. There are several cross-platform application development tools that allow developers to create these apps with little trouble. Now that you know the difference between cross-platform and native apps, let’s see how they compare.Performance – Native vs. Cross-PlatformNative apps make the best use of resources and utilize the platform’s capabilities up to its full potential. This means native apps are high performing apps that are fast, responsive, and less likely to crash. If the developers have enough knowledge of the platform they are working on, they can optimize native apps to highlight the best features and capabilities of that platform. Cross-platform apps are often plagued with performance issues. Since they are the built-on one-app-fits-all approach, it is not unusual for these apps to act out on certain devices. Winner - NativeFeatures – Native vs. Cross-Platform Native apps can make use of the device’s native feature, especially with iOS, which runs only on Apple’s proprietary devices. Another great advantage of native apps is that they allow offline features, which is something not easily possible with cross-platform apps. Cross-platform apps cannot utilize the native features of the device because they have limited access to the API. Since they are developed for different devices with varying features, developers usually avoid making assumptions about the available features.Winner - NativeFeasibility – Native vs. Cross-Platform Native app development takes twice as much time as cross-platform apps. The cost is also higher since it usually requires building more than one app. Maintenance is equally time-consuming and costly, as the developers have to identify bugs and problems for each platform and create different updates accordingly. Cross-platform apps are relatively cheaper in terms of development and maintenance. You are investing in a single app and that is all you will have to maintain. However, sometimes, the higher number of issues and bugs outweigh this advantage. Winner – Tie User Experience – Native vs. Cross-Platform The importance of user experience is increasing by the minute, which is why it is the most essential thing that you must ensure in your app. Considering the above stats, this one really is a no-brainer. With better performance, higher speed, and better device utilization, native apps offer a tremendous experience. Designers and developers have more creative freedom to create good-looking apps and smooth functioning apps. Native apps are not just responsive but also intuitive. While developers can create equally intuitive cross-platform apps, such features often come at the cost of speed. It is difficult for the developers and designers to simultaneously fulfill all the UX requirement of multiple platforms. Overall, cross-platform apps do not deliver a desirable user experience. Winner – Native A Fair Conclusion Native apps seem far superior in terms of performance and user experience. This is enough to make them the winner. However, let’s not forget that the choice truly depends on your application. Simple applications like games and content distribution apps are usually developed as a cross-platform app while apps with specific features are native.Cross-platform is also preferred for B2B apps where deployment time is of utmost importance. Many small businesses also opt for cross-platform due to their limited budget. However, compromising performance and user experience for the sake of savings is often counterproductive. It is important for you to choose a platform that meets your needs, requirements, as well as your target audience,  needs to create your winning app.
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Native vs. Cross-Platform Apps – You’ll Be the Winner

Native vs. Cross-Platform Apps – You’ll Be the Winner

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Native vs. Cross-platform is an age-old debate. It is a debate that has kept the tech community divided for years. Many claimed to have found the ultimate answer but both cross-platform and native app...
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How AR can Help Improve Customer Engagement in eCommerce?

The first age of the internet was about information and the next one will be about experiences. The biggest companies of the first generation are built around access to information. Google's main goal is to sort out the world's information. Facebook gives you information about the important moments that occurred in the lives of your near and dear ones.However, the coming generation of big companies is being built around giving people experiences. Snap Chat is meant for in-the-minute experiences with your friends with no digital information left behind. Airbnb plans to make you experience another city like a local.And, with regards to retail, an impact of Augmented Reality on E-commerce customers is changing the whole game. This can be well studied from the stats supporting the statement of how AR improved e-commerce customer engagement. This cutting-edge technology makes it possible for the owners to augment real-world shopping conditions with computer-generated resources, bringing efficiency, clarity, and comfort to the customers. But, how can retailers with little experience in this high-tech tool best use AR to help increase conversions? While there are different strategies, one of the techniques more retailers are utilizing is to improve customer engagement using AR. Here are the tips for increasing the Customer Engagement using AR:Steps to engage e-Commerce customers with ARBy Improving Customer Experience The AR technology gives customers who shop online the opportunity to view the product in form of a model. They can connect with in a similar way they would if they were visiting a physical store. This gives the clients a superior feel of the product and how it fits into their lives. As per the reports, 61% of online customers like to make purchases on sites that offer AR technology.By Providing Modification and CustomizationMost of the customers like to explore various preferences and choices before buying a product. With AR, clients would now be able to experience this virtually from the solace of their homes. Customizations including designs, colors, patterns and much more can be explored with augmented reality.By Increasing the Shopping TimeToday, most online stores offer instructional videos on a few products so as to enable the purchaser to understand the product. With Augmented reality not only they could interact with the product but they can also explore its functionalities. Generally, an average online customer spends about 2 minutes online before making a purchase, however, with the AR that time can be increased. Moreover, it has been showing that the more time a client spends in a store, the more certain they are to make a purchase.Amazon Is Already On-BoardAmazon, an eCommerce and cloud computing organization has added 'AR View' function in its app for ease of shopping. As of now, Amazon offers AR view function for the selected number of products. But, the company has plans to continue including more products in this list so that customers can test the look and feel of it.The products can be rotated to know how extensive it is and what it looks like when placed in the real area. AR view feature of Amazon application has improved shopping experience by giving customers a satisfied feeling and winning their confidence while placing the order.Incorporating AR in eCommerce Websites Made SimplerForecasting the rising need of AR-based mobile applications in the coming future, big giants like Google and Apple have built up their very own Augmented Reality platform. These platforms make it easy for developers to develop eCommerce applications having AR features.ARKit: Augmented Reality for iOS iOS app developers can breathe a sigh of relief with the accessibility of ARKit – a platform to develop AR iOS applications. This kit decreases the time and efforts of the developer in building apps that flawlessly mix digital objects and information with the real-world environment.ARCore: Augmented Reality for Android ARCore is a software development kit for Android developers to create amazing applications with Augmented Reality capacities. This kit contains every one of the tools that developers require to make greater and better bets with AR that superbly blend the virtual and real world.The Future of AR in eCommerceAugmented Reality is the future of omnichannel experience. This technology uses the perk of online shopping, for example, convenience, and beats the challenges of 'vulnerability' to deliver impactful outcomes for eCommerce business. AR enables customers to interact with the product in real time and deliver touch and feel experience like that of in-store shopping.By taking out the guesswork from online shopping, AR technology has lessened company losses resulting from return request of clients. These pain points are addressed with innovations in the field of eCommerce. AR enhanced e-Commerce customers, making their life easy and hassle-free. AR is genuinely a market differentiator and many eCommerce brands are betting big on it.
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How AR can Help Improve Customer Engagement in eCommerce?

How AR can Help Improve Customer Engagement in eCommerce?

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The first age of the internet was about information and the next one will be about experiences. The biggest companies of the first generation are built around access to information. Google's main ...
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Why the invention of machine learning is considered to be the end of the human era?

Modern machine learning powers a great many applications such as social network filtering, self-driven vehicles, finding terrorist suspects, medical diagnosis, computer vision, and playing video and board games. Generally, it's regarded as a type of Artificial-Intelligence or Al. In other words, it is a subset of AI where computer programs are used to learn new things independently using the available data and information. The main idea behind this type of AI relies on mathematics, statistics, computer science and the enormous scientific work such as the development of the Least Square method (1808), Bayes Theorem (1812), and Markov Chains (1913) that was contributed by the great scientists who lived many of years ago.Machines that operate using this kind of technology are autonomous and require no human intelligence. In 2015, more than three thousand researchers from the Al and Robotics department led by Elon Musk, Stephen Hawking, and Steve Wozniak signed a letter warning about the risks of using autonomous weapons which work with freedom and independently without human involvement.This account focuses on the invention of artificial super-intelligent humans with the help of machine learning, and the dangers it poses on the existence of human beings, creating a negative impact on the human eraHistory of Machine Learning and AIOriginally, in the 1940s, when the ENIAC, the world's first electronic general-purpose computer system was being invented, the term the computer' was used to refer to a person with intensive capabilities to do numerical computations. The ENIAC, therefore, was identified as a numerical computing device and the media, in 1946 when the machine was being announced referred to it as a °Giant Brain'. No one knew exactly whether the device was a learning machine. But from the very beginning, the main idea was to create a powerful machine that could surpass learning and human thinking. Around the 1950s, the first gaming program that was believed to win against the checkers' world champion emerged. The program was used in a great deal by checkers-players to better their skills. Later, with great support from the Office of Naval Research, a United States international Navy and Marine research department, Frank Rosenblatt invented an algorithm referred to as the Perceptron that became a powerful monster when it was combined in a network with large numbers. Several years later, we saw the Department of the neural networks becoming stagnant due to limited abilities to find answers to some problems.In the 1990s, machine learning became popular although not as much as today. The combination of statistics and computer science led to the development of Artificial Intelligence which further shifted toward the so-called data-driven applications. Scientists used plenty amounts of data available to create intelligent systems that could inspect, analyze, and learn using large-scale data Today, the concept of artificial intelligence is viewed as the last invention and end of the human era.Machine Learning and Al Training ProgramsIt requires great knowledge of science, mathematics, and computational statistics to become smart in working with models and algorithms used in machine learning. Knowledge of computer hardware and software skills used in machine learning such as Python, C/Coo, R, Java, Scala, Julia, and JavaScript is needed to fully develop an Al application. The following are the current programs used to train individuals who are interested in building Artificial Intelligence applications.Artificial Intelligence Masters Programs MS degree programs in AI provide postgraduate students with a great deal of knowledge of the design, theory, development, and application of socially, linguistically, and biologically motivated computer systems. Research-oriented programs such as MPhil and MRes are also available although not in every institution. This degree, depending on the program, may include a specialization in topics such as cognitive robotics, machine learning, multi-agent systems, and autonomous system design. To pursue this career, learners should have an undergraduate degree in a related field such as Robotics and Computer Science.MS In Computer Science Online This degree is designed to train busy professionals anywhere in the world to study at their own pace. The program is non-thesis and provides a thorough preparation of the techniques and concepts related to programming, design and computer systems applications. Learners are equipped with in-depth knowledge and understanding of the essentials and current matters in computer science and engineering. It includes about thirty courses depending on the institution on topics such as robotics, AI, machine learning, and computational perception. The program requires a student to choose one of the various areas of study such as Software analysis and design, Bioinformatics, Information Technology, Computer programming, and etcetera.  M Tech Distance LearningAfter completing a B-Tech degree, you can advance your career by applying for an M-Tech degree program which takes exactly two years. Through distance learning, you can attend lectures at your own time. Additionally, you can access various programs through video chat, voice chat, and conferences which are considered a part of the course. It equips learners with a lot of research in the respective fields (Machine Learning, Computer Science and Engineering, and Mechanical and Electronic Engineering) and also an in-depth knowledge and training required to work in various research and development centers, power and mining generation companies, and consultation firms.India has the most popular universities such as Amity, Annamalai, and Indira-Gandhi Open Universities known to provide an M Tech Distance Learning degree. To pursue this career, you must have obtained an undergraduate Tech degree with the minimum qualifications required by the university council. Additionally, you must have a Certificate of Migration to be eligible for the distance learning degree in India.Machine Learning: Why it's The End of Human Era Today, the development of AI has crossed borders due to the invention of many systems that nearly imitate the behavior of an intelligent human being. Machines, things that are known to possess no life, having capacity perform assignments that require human judgment such as decision-making, speech recognition, translation between languages, visual perception, and etcetera is posing a great threat to our existence. In his book, Our Final Invention: Artificial-Intelligence and the End of the Human Era, author James Barrat cautions strongly the risks that artificial super-intelligent human beings pose to the existence of people. He further stresses how difficult it could be to predict or control a thing that is more intelligent than its own creator. Moreover, all the incredible threats and difficulties human face today including climate change, nanotechnology, nuclear weapons, biotechnology, and a lot more, are brought about by one thing: Artificial Intelligence. That's why Barratt states, in a plain and very clear language to completely go against Al. Our intelligence could be the only attribute that separates us from the aliens and other animal species out there. Humans were not the strongest animals in the world, but with their smart brains, they were able to emerge on top of all the beasts in the jungle. Sadly, our existence is put in jeopardy by things that we have created with our own hands.Scientists could be creating an Al that has the greatest intelligence than any other creature on earth that could become very powerful to do all our daily tasks or even fail us and kill us all depending on how they are programmed. Although the current era is more focused on the development of machine learning and artificial super-intelligent human beings, professionals think that the world is heading in the wrong direction. This is due to the fact that computer automated machines, no matter how intelligent they are, will never "think or understand" as the normal human brain. Additionally, comparing computer networks and the way they analyze things with their powerful algorithms to the conspiracies of the human brain is very similar to comparing oranges and apples.Conclusion To sum up, based on research and several reports published on various journals, it's very clear that the invention and development of machine learning in robotics and Al applications can become a great threat in the way of life of humans. This’s considered very possible when a machine is programmed to perform important duties, but instead develops a devastating technique for attaining their own goal. Moreover, when robots and other machines are programmed to perform destructive duties such as killing, getting into the hands of criminals and other dishonest people could lead to enormous casualties all over the world. This is the reason why the invention of machine learning becoming threats to humans in the future. 
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Why the invention of machine learning is considered to be the end of the human era?

Why the invention of machine learning is considered to be the end of the human era?

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Modern machine learning powers a great many applications such as social network filtering, self-driven vehicles, finding terrorist suspects, medical diagnosis, computer vision, and playing video and b...
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