Hey, learners! Welcome to the next tutorial on Python with examples. We have been working with the collection of elements in different ways, and in the previous lecture, the topic was the built-in functions that were used with the sets in different ways. In the present lecture, we will pay heed to the basic operations of the set and their working in particular ways. We are making sure that each and every example is taken simply and easily, but the explanation is so clear that every user understands the concept and, at the same time, learns a new concept without any difficulty. The discussion will start after this short introduction of topics:
How do we declare and then find the difference between the two sets?
What are the built-in functions for operations, and how do we use them in the codes?
How do we apply the operation of symmetric differences on the sets?
What is chaining in iteration, and how do we use it? Give some examples.
How do you define the union of the sets?
What is an intersection and how it is related to the sets?
What is the procedure to find whether the sets are equal or not? Give us some examples in Python.
All of these are important interview questions, and we are using the concept in this lesson to find the answer to each question in detail. The Jupyter notebook is being used for practical work. To open it, go to the search bar in Windows and search for the Jupyter notebook. After that, go to the “new” dialogue box and start Python 3 to create a new notebook.
We all come from a programming background, and it is not surprising to know that different mathematical operations are used in programming to do useful tasks. In this lecture, you will learn the importance of mathematical functions with regard to sets. We have been working on the set since the last two lectures, and we are now aware of the details of working on sets. But during the programming process, the code contains different types of calculations, and large programs are extremely difficult to run without the involvement of mathematical operations. We will explain this with the help of different examples, and this is going to be fun because these concepts have been studied by us since our early education, and now is the time to know the reasons for that study.
This is the simplest set operation and it involves the difference between the elements of the set in their corresponding values. In simple words, if we are dealing with two sets named A and B then the difference between them is a new set containing all the elements that are in set A but not in set B. In Python, there are two ways to get the difference of the set in two ways:
The difference using the sign
The difference using the function
Both of these are discussed in detail here. The difference with the sign is simple as we minus the values in the simple mathematical question. A minus sign is used between the names of the set, and we get the result.
On the other hand, if we talk about the function, Python has a built-in function that calculates the difference, and it is a more professional way to use the functions for such operations. These two methods are simply described in the example given next:
#Declaring two sets
A={12,8,34,5,90,3,2}
B={1,7,90,33,2,1,5}
#Using both methods of difference
differenceWithSign=A-B
differenceWithFunction=(A.difference(B))
#Printing results
print("A-B= ",differenceWithSign)
print("Difference with function= ", differenceWithFunction)
It is obvious that both methods give us the same results. So it totally depends on the programmer to choose the method, but here is a reminder that the order of the names of sets is important here because if we write B-A then the results will be entirely different.
If you found the difference between the sets easy then you will find it more interesting because it is similar to the difference of the set but the only change is, in the resultant set, the uncommon elements of set A and B both are included instead of only the first set. In this way, the output of the entries from both the sets and no set is ignored. To denote the symmetric difference, the “^” sign is used. By the same token, the function for the symmetric difference also exists in Python,n and the keyword “symmetric_difference” is used for this. Have a look at the code and output for this case:
#Declaring two sets
A={12,8,34,5,90,3,2}
B={1,7,90,33,2,1,5}
#Using both methods of symmetric difference
SymmetricDifferenceWithSign=A^B
SymmetricDifferenceWithFunction=(A.symmetric_difference(B))
#Printing results
print("A^B= ",SymmetricDifferenceWithSign)
print("Difference with function= ", SymmetricDifferenceWithFunction)
Compare the results with the previous operation, and the difference will be clear.
In the previous lecture, we have been working with the itertools. The current case is to check whether the itertool works with the sets or not. You will see that the union of the two sets has the same results as the chain of the two sets, but for the sake of learning, we are working on both concepts. Hence, have a look at the code and understand it. After that, type the code on your own and check the results.
from itertools import chain
apple={12,8,34,5,90,3,2}
banana={1,7,90,33,2,1,5}
totalSales=set(chain(apple,banana))
print(totalSales)
By simply declaring and using the sets in a chain format, we are getting the result of the sales of both fruits, but in the cases where both sales were equal, the entry is ignored as we are using the set that ignores the duplicates.
At the simplest level, the results of union and chain are the same, but at a higher level, the working of both of these is different; therefore, it is important to understand both concepts and practice them with the sets.
Now, let us show you an interesting example of the same function where a set is used with the string entries. When applying the chain to that particular set, the same alphabets are ignored. The programmers can represent the results as joining or separate results, and this is explained well with the following code:
from itertools import chain
A = "Python"
B = "Programming"
C = "Tutorial"
output1 = set(chain(A, B, C))
print("before joining the set :", output1)
output2 = ''.join(res)
print("After joining the set :", output2)
We know the message is not conveyed in detail and therefore, it is proved that sets are more useful when dealing with numbers.
The name of this operation is self-explanatory if we have two sets named A and B, then the union of these sets means a new set that contains all the elements of set A and set B. We have read this in our mathematics classes, but here, this will be done with the help of coding. There are some special signs that are used to define the operations, just like we indicated the difference with the help of minus signs. When we talk about the union of the sets, we use the | sign for this, and Python also has the function for the union.
There is no need for long explanations for this operation, and we are simply changing the sign of the previous example and providing you with an example of how the difference and union give you entirely different results by changing just the operation.
#Declaring two sets
A={12,8,34,5,90,3,2}
B={1,7,90,33,2,1,5}
#Using both methods of Union
unionWithSign=A|B
unionWithFunction=(A.union(B))
#Printing results
print("A | B= ",unionWithSign)
print("Union with function= ", unionWithFunction)
As a result, the resultant set contains the values of both sets, but have you noticed that the resultant set has fewer values than the collection of both sets A and B? It is because we are using sets, and the values that are identical are just shown once.
If you have understood the concept of the union of sets, then you must keep it in mind, and then by comparing the process of intersection, the concept of both these operations will be clear in your mind. It is because usually people seem confused between these two.
Let us take the case where we are discussing the recipes of two dishes that are chicken and fish and want to know the common ingredients to write in the list. For this, we will use the intersection of the set, and both of these will contain the name of the ingredients, which means these are the string sets. During the process of the intersection of sets "fish” and "chicken," the resultant set contains all the elements that are common in both sets, and all other entries are totally ignored. In this way, the person will be able to understand what things he needs from the same section of the store and where he has to go for more shopping. This will be done with the help of the following code:
#Declaring two sets of string
fish={"Onion", "Tomato", "Garlic", "Fish", "Salt", "Carrot", "Eggs", "Ginger"}
chicken={"Chicken", "Salt", "Potato", "Onion", "Garlic", "pepper"}
#Using both methods of intersection
intersectionWithSign=fish & chicken
intersectionWithFunction=(fish.intersection(chicken))
#Printing results with the same ingredients
print("fish & chicken= ",intersectionWithSign)
print("Intersection with function= ", intersectionWithFunction)
When dealing with different types of sets, let's say set Apple and set Banana, where the shopkeeper stores the data of the weekly sales of cartons, the seller wants to take the record of the day and check if the sales are the same or not. Then, he simply checks the results by applying the equal signs for the two times between the sets and getting the result. Keep in mind that the order of the set does not matter here as the set does not have the index values. So, no matter if the sales were not equal on the same day, if the weekly sales are matching exactly, then the sets will be equal.
#Making the set of apples and banana with the sales
apple={34,78.9,32,89.7,33,12.6,55}
banana={12.6,78.9,33,34,32,89.7,55}
#using if loop to check the results and print the output
if apple==banana:
print("Sales of apple and banana are the same")
else:
print("Sales of apple and banana are not the same")
Now, to elaborate on this more, the sets that are used in the previous examples are fed into the same code, and then by running the code, we can conclude the results.
#Making the set with the sales
apple={12,8,34,5,90,3,2}
banana={1,7,90,33,2,1,5}
#using if loop to check the results and print the output
if apple==banana:
print("Sales of apple and banana are the same")
else:
print("Sales of apple and banana are not the same")
The “if” loop is an important iteration, and this is the best way to explain how we can use it for the bi-conditional cases. These loops will soon be used in many complex codes in this tutorial.
Hence, it was a fantastic tutorial on sets, and we learned a lot with the help of different operations. We had an idea about the sets and their characteristics, but in this lecture, the operations and working of the sets are well explained, and we see the difference, symmetric difference, chain, union, intersection, and equality operations of the sets in detail. I hope you get the point that we are trying to make clear. For more interesting tutorials, stay on the same website.
Artificial intelligence is becoming more and more important for data security. In this post, we'll look at how AI may assist businesses in anticipating and thwarting threats. But before going ahead we will explain the terms artificial intelligence and machine Learning.
Artificial intelligence (AI) is a discipline of computer science that focuses on making electrical equipment and software intelligent enough to do human activities. AI is a broad concept and a basic subject of computer science that may be used to a variety of domains including learning, planning, problem solving, speech recognition, object identification and tracking, and other security applications.
Artificial intelligence is divided into numerous subsets. We shall look at two of them in this article:
Deep Learning
Machine Learning
Machine learning (ML)-based computer systems have the capacity to learn and carry out tasks without explicit instructions. These systems find, examine, and comprehend data patterns using ML algorithms and statistical models. Many jobs that are typically completed by people are now routinely carried out automatically using machine learning capabilities.
A machine learning technique called unsupervised learning enables ML algorithms to carry out tasks without clear instructions and produce desired results. Based on analysis and experience, this method determines the best solutions to a problem. When given an input (a task to perform), the model can decide on its own what the optimum course of action is. The model gets better trained and becomes more effective the longer it solves the assignment.
The benefit of ML for many tasks is obvious—machines don't grow bored or upset by repeatedly performing the same monotonous tasks. By automating numerous processes in work chains, they also drastically reduce workloads. Security teams can, for instance, use AI-based solutions (which will be covered later) to automatically detect threats and handle part of them, minimising the amount of human contact necessary for specific security activities.
Data anomalies can be found with the aid of machine learning. You may train algorithms that recognise particular patterns and user behaviour using machine learning. Detecting suspicious behaviour in a workplace, such as an increase in password resets or unexpected requests for sensitive data, will be made possible thanks to this.
Computer vision can also be used to find data trends that might point to a possible system or network vulnerability management violation. Machine learning techniques are employed to forecast future examples of this behaviour based on the environment's present conditions after being trained with historical data on previous successful attacks (e.g., usage patterns).
Besides ML techniques you can rely on the use of VPN. Because you can keep your data from suspicious activities from hackers by installing a VPN. It is easy to set up a VPN on router and once you set it will start monitoring your PCs activities against malicious attacks.
Before an attack ever occurs, AI may identify it and stop it. Understanding how data is gathered, processed, and presented is just as important as looking at the data itself. AI is able to spot warning signals of impending attacks and stop them from executing in the cloud, on a network, or even in real time.
By seeing dangerous activity on your virtual machine (including malware) while you're away from home or work or even on mobile devices, AI can also assist you in protecting yourself against AI-enabled dangers of gadgets and PCs both! Additionally, there are social media platforms like Facebook and Twitter and AI also helps to keep them secure from attackers.
Artificial intelligence is becoming more and more important for data security. AI can assist businesses in identifying dangers, spotting abnormalities, and reaching decisions more quickly than ever before.
It plays a significant role in contemporary data management techniques, which in turn have significant ramifications for enterprises across all industries.
"Domain knowledge" is the capacity for people or computers to comprehend information and take appropriate action without being instructed on its workings or meaning (AKA: natural language processing).
"Machine learning" is the process through which computers or humans can perform jobs utilising data sets without any prior knowledge.
In order to learn from mistakes they made earlier in life and produce better results later on when things get difficult again, both of these strategies depend on increasing volumes of data being gathered over time.
Obtaining a thorough and accurate inventory of all devices, users, and software with access to computer systems. Inventory also heavily relies on categorization and the measurement of business criticality.
Hackers, like everyone else, follow trends, therefore what's popular with hackers changes on a regular basis. AI-based counterintelligence systems can provide current knowledge about global and industry-specific threats to assist in making crucial prioritising decisions on the basis not only on what may be employed to defend your organisation, but also on what is likely to be utilised to attack your organisation.
It is critical to comprehend the significance of the numerous security technologies and verification activities that you have implemented in order to keep a stable security posture. AI can assist you in determining your information security program's strengths and weaknesses.
Accounting for IT assets, threat sensitivity, and control efficacy, AI-based solutions may forecast how and where you will be compromised, allowing you to allocate resources and tools to areas of weakness. AI-derived prescriptive insights can assist you in configuring and improving policies and processes to most greatly increase your organisation's cyber resilience.
AI-powered systems can give greater context for prioritising and response to safety warnings, for quick incident response, and for surfacing root causes to remediate exposures and avoid future issues.
The explainability of guidance and analyses is critical to leveraging AI to enhance human information security teams. This is critical for gaining buy-in from stakeholders across the organisation, understanding the impact of various information security programmes, and reporting relevant information to all stakeholders involved, including end users, security operations, CISO, audit committees, CIO, CEO, and board of directors.
Although I have been doing this for a while, data security is currently enjoying a comeback. People are more worried than ever about their sensitive data being stolen because hackings are on the rise. The good news is that scalable data protection is possible with artificial intelligence (AI). In this article, we talked about how AI and machine learning combine to find abnormalities in massive datasets and spot trends that point to shady conduct.
Welcome to today's article on our comprehensive Raspberry Pi 4 programming guide. As we saw in the previous article, the Raspberry Pi 4 may power a single seven-segment display. In addition, we also interfaced a Raspberry Pi with 4 Seven-Segment Display Modules to display the time. However, this guide will show you how to construct a Raspberry Pi 4 crypto miner that uses very little electricity.
Cryptocurrencies have been the subject of widespread conversation for some time now. It's possible to use your computer to create them, and they can be used as currency. Because of this, the Raspberry Pi can also be used for Bitcoin mining. It's also possible to mine other cryptocurrencies. One drawback of mining is that the cost of electricity often exceeds the revenue it brings in. So, let's check out how to construct a solar-powered, money-making cryptocurrency miner with a Raspberry Pi.
| Where To Buy? | ||||
|---|---|---|---|---|
| No. | Components | Distributor | Link To Buy | |
| 1 | Raspberry Pi 4 | Amazon | Buy Now | |
A pool account
Bitcoin Wallet
Raspberry Pi
Raspbian image SD card
USB Bitcoin miner
Crypto mining, the digital equivalent of the gold mining industry, involves a combination of complex mathematical calculations and blind luck. Mining is crucial for cryptocurrencies as it is the only way to update the distributed ledger (Blockchain).
Despite Bitcoin's popularity, there are other digital currencies available. All cryptocurrencies use blockchains to ensure that all transactions are legitimate and that users cannot spend the same cryptocurrency more than once.
To simplify things for the unfamiliar in the web3 environment, let's say that a blockchain is a distributed ledger that maintains track of all transactions made over it. Similar to how a bank keeps a record of who gave money to whom, how much was sent, and when it was sent, blockchain stores this unchangeable data within distributed blocks linked together via a network.
Users, known as miners or validator nodes, provide the network's computational power to verify all of the blockchain's transactions. This blog post will not delve further into smart contracts, which are computer programs that can be set up to run automatically on a blockchain if and only if specific criteria are met.
Bitcoin and Ethereum miners are sometimes pictured as a large server farm full of powerful graphics processing unit (GPU) or application-specific integrated circuit (ASIC) devices that work tirelessly to solve complex cryptographic puzzles issued by the blockchain in exchange for financial rewards. The consensus technique for validating submissions and awarding incentives varies from blockchain to blockchain.
Raspberry Pi users can choose from several different coins to mine, but not all are profitable. The most profitable option is the one you should choose. The USB miner is crucial to mining since it dramatically boosts productivity. In mining, you have two primary options:
For anyone interested in beginning mining using a USB miner like NEWPAC, selecting a cryptocurrency that uses the SHA-256 algorithm is a must. Bitcoin (BTC), Bitcoin Cash (BCH), Bitcoin SV (BSV), and many others are just some of the cryptocurrencies that use the SHA-256 algorithm. However, Bitcoin is the most lucrative and should be explored first if you plan to mine using a Raspberry Pi.
The Raspberry Pi's central processing unit (CPU) can be used to begin mining in the absence of a dedicated USB miner. In such a scenario, you should go with Monero (XMR), the coin that can be mined with the least effort using a Raspberry Pi.
After calculating electricity and equipment costs, I found that bitcoin mining with a regular computer could have been more worthwhile. Most bitcoins are now mined using specialized computers called ASIC bitcoin miners; nevertheless, amateurs and enthusiasts still have some success mining by joining a mining pool. What if we set up a mining rig powered by a Raspberry Pi and solar panels and "deducted" the cost of the equipment? As the number of miners for Bitcoins increases, the difficulty of mining rises, and the rewards for miners decrease, the industry has become very competitive. Despite this discouraging information, I've decided to move on with this plan and shift my focus to alternative crypto assets.
Since we are utilizing a Raspberry Pi rather than an ASIC bitcoin miner, individual crypto mining was not an option. Despite my best efforts, I could not locate any mining pools that supported the Raspberry Pi operating system among the many available for Windows and macOS. Since Linux miners are written for the x86 architecture, Raspberry Pi cannot participate in the mining process. Linux mining software that runs on x86 processors like those found on most personal computers is supported.
Please note that the purpose of this paper is to promote further study of blockchain technology and cryptocurrencies, not to create any of those assets. The techniques outlined here are workarounds that need to be endorsed by the developers. Instead, you can download the free software linked with your preferred mining pool and install it on your personal computer.
We'll first sign up for an account on minergate, a crypto mining pool with over 3.5 million users worldwide that supports Bitcoin, Gold, Zcash, Ethereum, Ethereum, and monero. Since Monero is the only crypto I have had success with, this guide will focus solely on that one.
Turn on your Raspberry Pi.
Press Ctrl-T or launch a Terminal window in Raspberry Pi OS using Desktop. Please use the standard login procedures while using Raspberry Pi Lite.
If you're already in the Terminal, you can install the updates and prerequisites immediately.
sudo apt-get update && sudo apt-get upgrade -y
sudo apt install git automake autoconf libcurl4-openssl-dev libjansson-dev libssl-dev libgmp-dev
cd cpuminer-multi
Please use the below three commands to compile the mining code. This process will take a few minutes if you're using a Raspberry Pi 4.
sudo ./autogen.sh
sudo ./configure
sudo ./build.sh
Let's begin monero mining once we've installed and set up the mining program on our Raspberry Pi. To activate the miner, run the following line in the Terminal, substituting YOUR EMAIL with the address you used to create your minergate account.
./cpuminer -a cryptonight -o stratum+tcp://xmr.pool.minergate.com:45700 -u YOUR_EMAIL
The mining software will begin running, and if you're lucky, you'll see some 'accepted' shares marked with a "yes."
Please log in to minegate/internal so we can inspect your Minergate Dashboard. This can be done on a PC or laptop using the Chromium web browser or on a Raspberry Pi using the Raspberry Pi Desktop interface. Find the Monero icon at the bottom of your screen. The ONLINE status will be displayed if Monero is connected and functioning correctly. Congratulations! You have started Monero mining!
Now that we have a basic understanding of blockchain and cryptocurrencies, the issue of which currency is superior naturally emerges. The original cryptocurrency was Bitcoin, but there are now thousands of others, each with unique characteristics.
Though Bitcoin transactions may be traced back to specific senders and recipients through their hash values, this is a significant drawback of the cryptocurrency.
Monero is a cryptocurrency with unique rules in this regard. It's likewise mineable and based on a blockchain, but unlike bitcoin, the transactions here are anonymous and difficult to track. This is why most exchanges will not let you buy or sell Monero and why mining is the best option if you want some.
Many more cryptocurrencies exist besides Bitcoin and Monero, such as the technically superior coins Ethereum and the humorous currency Dogecoin. The Raspberry Pi can be used to mine a large number of them.
We'll utilize the Crontab approach to ensure that our cryptocurrency miner is always running on our Raspberry Pi.
crontab -e
If you haven't already, you'll see the message "no crontab for pi, Choose an editor" when you try to set the crontab.
Select 1 and press Enter.
Clicking here will launch a new crontab file; once it has opened, go to the bottom and add the following command, substituting YOUR EMAIL with the email you used to sign up for your Minergate account.
@reboot sudo /cpuminer-multi/cpuminer -a cryptonight -o stratum+tcp://xmr.pool.minergate.com:45700 -u YOUR_EMAIL
To keep your crontab, hit Ctrl-X and then y.
Type "sudo reboot" into the Terminal to restart the Pi.
After being powered on for almost 8 hours, my Raspberry Pi 4 has successfully calculated 357 good shares. Successful miners receive compensation when their shares are valued. If I do the math and get the appropriate answer, but my Pi is slower than another computer, I get a bad share. Only the first miner will be compensated if a miner submits a valid response before anyone else. Every invalid share is a penalty for the miner because of the possibility of fraud. I began to worry when my first four shares were flagged as invalid.
357 good shares = 0.000001410642 Monero = 0.00015569 USD
For 8 hours, I earned $0.000100, which is less than a penny. I was required to have at least 0.05 Monero (equivalent to about $5.811 USD) to make a withdrawal. (As of the date this article was published, the exchange rate was.) To attain the minimum withdrawal criterion of 0.05 Monero would take me 3,762 years at a rate of accumulating 0.000001410642 Monero per 8 hours.
As was mentioned at the outset of this piece, the aim of this activity was education regarding bitcoin, not financial gain.
Mined cryptocurrency rewards are divided up based on hash rates and shares. My hash rate swung between 1.6 and 33.3 hashes per second. The pool averaged 10.27 MH/s, around 3 million times faster than my Pi. As a point of comparison, 1 MH/s equals 1,000,000 hashes/ sec.
Additionally, a tiny commission is added to your transactions by the Minergate. Choose a Pay Per Share structure or one based on chance (with more significant potential gain).
Many 'time out' and send line failed' errors appeared on my Pi as I wrote this essay. On occasion, a Pi reboot was required, but on other occasions, the miner resumed operations without any more intervention.
Even though my Raspberry Pi wasn't a "money maker" in the cryptocurrency mining game, we still had a great time seeing it effectively compute and accumulate excellent shares.
A person can easily mine bitcoins at home with minimal equipment. A powered external USB hub may be the way to go if you want to avoid shelling out the cash for a desktop PC. Bitcoin mining can be facilitated and made more profitable by using a powered external USB hub. Raspberry Pi version B, compatible with most PCs, is also readily available and inexpensive. You can use Bitcoins to buy and sell on websites or keep them safe in a digital wallet
when you have Bitcoins.
Remember that large commercial Bitcoin miners employing thousands of computers will be your main competition. Still, a Pi 4 mining system is a fantastic (and entertaining) method of earning Bitcoins with little work. Because of the high cost of maintaining the hardware, mining Bitcoin using a Pi 4 is not financially sound. For Bitcoin mining, you'll also need hardware that's up to the task.
To be sure, a Pi 4 mining system can be a fantastic (and entertaining) method of earning Bitcoins without much effort on your part. However, even if you only make a few Satoshi, you'll still gain valuable experience and knowledge, so it's a good use of time. Be mindful of your monthly electricity costs, though.
Although you might make a few dollars mining on a Raspberry Pi, you won't become filthy rich overnight. Your electric bill may skyrocket if you've amassed a sizable Raspberry Pi fleet for mining. You can generate a small profit with a solar panel designed for the Raspberry Pi. The revenues won't make you rich, though; mining Monero with a Pi 4 and 100H/s of hashing power will net you just $1 per year. Making an annual average of $20 from mining using a USB miner is possible with Bitcoin.
We have developed a cryptocurrency miner that generates no additional costs whatsoever. The hash rate is a severe drawback of this design. Bitcoin mining on the Pi 4 is only profitable if the values of cryptocurrencies are supposed to remain the same. The upfront investment in equipment is more than the yearly return on investment from mining. One's perspective could alter if one were to speculate on the possibility of dramatically increasing prices. Those who are just sitting on unused hardware are in the same boat. A little setup is not worthwhile. The following guide will teach you how to set up a fingerprint sensor on your Raspberry Pi 4.
Thank you for being here for today's tutorial of our in-depth Raspberry Pi programming tutorial. The previous tutorial demonstrated the proper wiring of the photoresistor sensor to the GPIO pins. Finally, we learned how it might be included in a Python script for data collection and analysis needs. We also looked at the functions of each component in the circuit. However, I'll walk you through installing a Pi 4 Print Server in this guide. While installing the program is straightforward, setting it up so that a Windows network can locate the print server requires a little more effort. Rather than spending hundreds of dollars upgrading to a laser printer, you may easily upgrade your current USB printer to laser quality by installing a print server.
Because of this software, you no longer have to have the printer physically linked to a single computer, and you may place it wherever you choose and share it with as many computers as you like. In addition, it's a fantastic method of printer sharing that eliminates the need for a pricey tower computer to be on and active all the time. CUPS is the program we'll be using to make this happen. Common Unix Printing System, or CUPS, is the foundation of Linux printing applications. But, the program facilitates communication between your computer and printer. It would help if you visited available printing to verify that the CUPS printing software supports your printer model.
| Where To Buy? | ||||
|---|---|---|---|---|
| No. | Components | Distributor | Link To Buy | |
| 1 | Raspberry Pi 4 | Amazon | Buy Now | |
Raspberry Pi 4
Wi-Fi
USB Printer
Since the Raspberry Pi print server is included in the Debian Jessie distribution, setting it up is a breeze. In this lesson, I'll be using Raspbian, so if you're unfamiliar with it and would like to learn how to set it up, check out my guide on how to do so.
We must ensure the Raspberry Pi is up-to-date with the most recent software to get started. Just type in the appropriate instructions into the terminal to accomplish this.
sudo apt update
sudo apt upgrade
We can begin setting up the print software after the Pi 4 has been upgraded. Here, we will be setting up CUPS.
CUPS, short for Common Unix Printing System, is a printing system designed for computers running UNIX-like operating systems. The software transforms the host computer into a print server. A CUPS-enabled server may receive print jobs from various client devices, sort them, and send them to the correct printer for output. Conveniently, this program can handle the administration of your printers, whether they're linked locally through USB or remotely via the network. Using the terminal, enter the following command to install the software. Considering HP has CUPS that support its open source project, HP printers, in particular. Even if your specific printer model isn't listed as being directly supported by CUPS, you may still be able to find a compatible generic driver online that will get the job done. These links will take you to a list of CUPS-compatible printers.
sudo apt install cups
We still have some work to do after CUPS's installation is complete. The first step is to include the pi user in the lpadmin set of users. With this group, the pi user can manage CUPS settings without logging in as the superuser.
sudo usermod -a -G lpadmin pi
To make sure it functions properly on your home network, there is one more thing we must do to CUPS: make it available to every computer on your network. At this time, Cups is configured to refuse connections from addresses outside the local network. By entering the following two commands, we can make it listen to all incoming connections:
sudo cupsctl --remote-any
sudo systemctl restart cups
After this, any machine on the network can send prints to the Pi 4 print server. The following command can be used if you need to know your Raspberry Pi's local IP Address.
hostname -I
If you know your Raspberry Pi's IP address, you can use it to access the website at the address below. Be sure to replace "192.168.1.105" with your IP address.
We'll examine how to configure SAMBA so that Windows can find the Raspberry Pi print server. Furthermore, we will demonstrate how to install a printer using the CUPS interface.
A proper SAMBA configuration is required if you use your print server in conjunction with Windows. To get SAMBA up and running with the CUPS print drivers, we'll have to install it and tweak its settings.
First, check that SAMBA is installed; to do so, we can use the terminal's built-in install command. Just by typing this into the terminal, we can accomplish our goal.
sudo apt install samba
Now that SAMBA is installed on our Pi 4, we can access its config file and make some changes. The following command will cause the file to be opened in the nano text editor:
Sudo nano /etc/samba/smb.conf
Once the file has been opened, it must be scrolled to the end. To do this quickly, press the Control key plus the V key. The following lines should be added or edited once you reach the very end of the file. The file already contained the "[printers]" and "[print$]" sections; all I had to do was update the values to reflect the following.
[printers]
comment = All Printers
browseable = no
path = /var/spool/samba
printable = yes
guest ok = yes
read only = yes
create mask = 0700
[print$]
comment = Printer Drivers
path = /var/lib/samba/printers
browseable = yes
read only = no
guest ok = no
To save the file, hit CTRL+X, Y, and ENTER. SAMBA needs to be restarted to pick up the updated settings. The following command, when entered into the terminal, will restart SAMBA.
sudo systemctl restart smbd
It's easy to set up a printer using CUPS, but first, we need to open the program's graphical user interface. For the IP address of your Raspberry Pi, enter "hostname" into the terminal.
hostname -I
To access the IP configuration page for your Raspberry Pi, type the following into your web browser and enter the IP address you just jotted down. Replace "192.168.1.105" with your IP address when entering this address.
The following homepage is what you should see. Here, we'll go to "Administration" on the main menu.
You'll be directed to Cups's control panel when you click here. On this page, select the "Add Printer" option.
The "Add Printer" screen has been brought up, allowing us to choose the printer we wish to configure Cups with. That printer is a Canon MG2500 series machine. When you've made your print choices, click the "Continue" button.
Ensure the printer is turned on and plugged into the Raspberry Pi through a USB connection if it does not appear here. If your Raspberry Pi still doesn't show up, try restarting it while ensuring your printer is on and connected.
Choose your printer's model from the dropdown menu here. CUPS will automatically identify the printer model and install the appropriate driver when possible. However, this may only sometimes work, so you may need to sift through the list to locate the proper driver manually. Once you've double-checked everything and are pleased, click the "Add Printer" button.
After completing the steps on this screen, the printer will have been added successfully. Here, you can give it a name and a summary that mean whatever you choose. If you have more than one printer in your residence, specifying its location will make your life easier. If you want other computers to be able to use the printer, you must also turn on "Share This Printer." If everything looks good, hit the "Continue" button.
After finishing the printer setup process, you will see the screen shown in the image below. Several of the printer's more nuanced settings are accessible through this panel—the number of pages printed, the quality of the printout, and so forth.
Having finished setting up our Raspberry Pi print server, we will now discuss how to add it to Windows. Having SAMBA set up earlier in the course should make this step less painless.
Installing a CUPS printer on Windows requires selecting the driver that will allow Windows to communicate with and comprehend the printer. Launching "My Computer" or "This PC" and then clicking "network" in the left-hand navigation pane is a quick method to get to Windows' network page, where you can get started. When you get there, you should see a screen like the one below, where your Raspberry Pi's hostname (in my instance, RASPBERRYPI) is displayed. If you double-click your Raspberry Pi's share, it may prompt you to log in. If entering anything other than "enter" fails to log you in, try "pi."
The printers used with your Pi 4 print server should now be displayed on the screen. Select the printer you wish to use by double-clicking on it.
You'll see the cautionary message below if you try to double-click this. Select "OK" to proceed with the tutorial.
Select your printer brand on the left, and then select your printer model from the available drivers for that brand on the right. If your printer isn't listed here, you can identify its model online and install the necessary drivers. For me, that meant tracking down the Canon MG2500 series. When you've decided which printer to use, you may move forward by clicking the "Ok" button.
The procedure will now initiate a link to your printer. Select "Printer" > "Set as Default Printer" to make this the system's default printer.
Now that the printer has been installed on your computer, you can use it with any application that supports printing. By printing a test page, you may verify that the printer is configured correctly.
If you're having trouble printing a file, check to see if you've picked the correct printer driver in CUPS and Windows. Ensure the printer is turned on as well; the Canon MG2500 series, for example, do not immediately restart when a print job is delivered. Adding Apple AirPrint capability to your Pi 4 print server is a great way to expand its capabilities.
Apple's AirPrint printing technology eliminates the requirement for users of Apple products to acquire and install the separate printing software. By adding AirPrint functionality, you may quickly and effortlessly print from your iOS smartphone to any nearby printer. You can run an AirPrint server from your Raspberry Pi, and Cups is the software that will power it. It will take care of talking to your printer on your Raspberry Pi's behalf.
The "Avahi daemon" must be set up before AirPrint may be used on your computer. The following command will install the package onto your Raspberry Pi.
sudo apt install avahi-daemon
Using this package, you can make Apple's Zeroconf design a reality. Bonjour has become widely used to refer to this type of network architecture. Using Bonjour, AirPrint can link disparate gadgets like an iPhone and a Raspberry Pi. Once you've selected the files you'd like to print, the Bonjour daemon will forward them to the designated printer.
Let's restart the machine to see whether the AirPrint server has worked appropriately, and everything is ready. Execute this command to force the Raspberry Pi to restart.
sudo reboot
After rebooting your Raspberry Pi, you can check to see if anything went wrong. This should get you to the point where you can print from any AirPrint-enabled device.
Have you succeeded in following this guide and setting up a Pi 4 network print server? If you've followed these steps carefully, your Raspberry Pi should be ready to function as a network AirPrint server. We were able to accomplish this by putting the Avahi daemon in place. This daemon implements the bonjour protocol used by AirPrint. Feel free to leave a message below if you have any thoughts, suggestions, or problems you'd want to discuss. The following tutorial will review the steps for monitoring a patient's heart rate with a Raspberry Pi 4.
Hello students! Welcome to the new tutorial on Python. We all know that Python is one of the most popular programming languages, and there are hundreds or thousands of developers that are earning a handsome amount with the help of this easy programming language. In the previous lecture, we studied the range in the sequence, and in the present class, our concern is having the command on the sets in Python. We know you are curious about the set's details, but before this, I want to share the list of topics that will be covered in this class.
What is a set in the Python programming language?
What are some properties that distinguish the set from other data types?
What is the mutable data type, and how is it related to the set?
Introduction of the Jupyter notebook.
Can we have duplicate elements in the set?
How to add, remove, and update the elements in the set while using the Jupyter notebook.
How can we access the elements using a loop?
Give an example of how to use the length function with sets and why it is important.
All of these are important interview questions, and we will not only find the answer to them but also elaborate on them with the help of simple but understandable examples taken from daily life routines. Your duty is to perform each and every code, not only by copying it from the lecture but also test your knowledge and practising more and more by making your own examples.
Since the last few tutorials on Python, we have been studying a lot about the sequence, which is basically the representation of a collection of data types with homogeneity or heterogeneity in the elements. If we talk about the sets, these have the same properties and procedures as their other group, such as list and range, but a slight difference in their property makes them a different data type. This can be elaborated with the help of its basic definition:
“The set is the type of sequence that contains the group of different data types, and it is the collection of unordered or unindexed data types together.”
Until now, the sequence discussed had been represented exactly as it was written by the programmers in the code. Yet, in the sets, the order is not exactly the same all the time. If you are thinking it is strange, then you must know, in the higher level of programming, this property of the set works great because we get the elements in random orders.
Another difference between the set and the other sequences is the usage of the bracket, or, in other words, the declaration of the sequences. To tell the compiler that we want a set in the sequence, the programmers use curly brackets. You must have noticed that it is very rare to use curly brackets in Python, and therefore we can say that the representation of the set in Python is unique.
As we have a lot of information about the sequences, we can openly discuss the properties of the set, and the reader will easily understand them by comparing them with others. So, here are some of the properties that can be compared:
Sets are represented with curly brackets.
The elements of the set can not be duplicated; that is, all the elements are uniquely defined, and no element should be repeated; otherwise, the compiler will show the output in which the duplicate values are shown only once.
The set is a heterogeneous collection of elements, and therefore, the programmers can add one or more data types to a single set according to their choice.
The set can be empty, that is, declared with zero elements.
The set can be updated after its formation if the programmer wants to make some changes to it afterwards.
There are certain built-in functions of the set that, when used with the sets, have great applications in Python programming.
Each of these properties can be explained well with the help of TensorFlow. We have been using the Jupyter lab of TensorFlow since the start of this tutorial, and now, I want to tell you a better and more professional way to run the code with the help of TensorFlow. For this, you do not have to install any other software but the Jupter notebook already installed on your PC. Simply go to your search bar and run the Jupyter notebook. It will add a new tab with the label "home." Here, go to the “New” dialogue box and select Python 3. This will add the new project to a new tab. You can name it, but by default, it is named "untitled."
If you are practising all the codes with us by hand, you will observe that the Jupyter notebook has a better user experience, and it adds the ending of common syntaxes such as the double quotation and parentheses by itself when the programmer starts them. We will talk more about it in later lectures, but for now, we are moving towards the codes and properties.
The first thing that we want to revise here is the definition of mutable elements:
“In programming languages, mutable objects are those that are used to group different items and can change their value according to the instruction of the programmer.”
We have learned many mutable sequences, such as lists, and here, the point is to revise it to a set and not use the mutable sequences as the elements. Only data types such as strings, integers, etc. can be used as the elements in the set; otherwise, the programmer will face an error. This can be explained with the help of the code given below:
#Starting new list
myList=["Physics", "chemistry", "biology"]
#declaring a new set
mySet={myList,'a','e','i','o','u'}
print(mySet)
As a result, it is demonstrated that programmers can combine simple data types into sets, but it is not possible to create collections of mutable objects or collections of collections within sets.
In the properties, we have mentioned that the process of feeding the duplicate elements into the set is not useful because it checks for each and every element while providing the output, and if the element is being repeated, the sets ignore them. As a result, if we have the element more than once in our input, the number of elements in the input and output are not the same.
#Declaring the set
MySet={21,23.6,55,'Peach', 'Almond', 23.6,21,'Almond'}
#using iteration to print the set
for item in MySet:
print(item, end=" ")
print()
#calculating the length
length=len(MySet)
print('Numbers of elements = ',length)
This property will be more clear with the help of the following screenshot:
Hence, out of eight elements, the two duplicate elements are removed by the compiler, and we only get five elements that were calculated by the length function.
This is an interesting method that is compatible with the set in Python. Consider the situation where the programmer has declared a set and then needs to add an element to the same pre-defined set. In such cases, the addition method is useful, with the help of which the programmer simply uses the syntax of the add method and there is no need to recreate the whole set again.
NameOfSet.add(element to be added)
If the question arises about the position of the element, this will be clear with the help of an example that we are going to check:
#Initializing the set
mySet={'eggs', 'bread', 'jam',23,67,132,55}
print('Elements of my set is= ', mySet)
#adding a new element
mySet.add("oats")
#printing the set with the added element
print('Elements of my set with new element= ', mySet)
Keep the scenario in your mind that we have discussed above, but this time, there is a need to remove the lament from the set, and for this, Python has another method that simply searches for the required element from the set and removes it. Afterwards, the results can be printed on the screen to check whether the task is complete or not. The keyword to remove the element is "discard,” and it is used in the same way as the add keyword.
#Initializing the set
mySet={'eggs', 'bread', 'oat','jam',23,67,132,55}
print('Elements of my set is= ', mySet)
#removing the element "oat"
removeValue=mySet.discard('oat')
#printing the set with the removed element
print('Elements of my set with discarded element= ', mySet)
So, the removal process is also very simple and understandable but the syntax must be kept in mind and before using the final set in this case, always check for the results by printing the elements on the screen as we are doing here because a little mistake on the syntax results in no removal and it may cause the problem in the code. So it is a good practice to have an eye on the elements.
The updating process of the set may include different types of updates, such as increasing the size or changing the elements' sizes. For a better understanding, the best way is to learn how two or more sets can be merged into one large set. In the previous lectures, we have seen this type of process where merging is done with the help of a method. To discuss a new method with you, here we are using the update method. The process and syntax are the same as we have seen in the previous two methods.
setToBeAdded.update(setToBeUpdated)
As a result, the final set has elements from both of these sets. But it is important to notice that both sets have to be declared first, and in the third step, we get the merged or updated search with the help of the command given above.
#Initializing the first set
myFirstSet={'eggs', 'bread', 'oat', 'jam',23,67,132,55}
print('Elements of first set is= ', myFirstSet)
#Initializing the second set
mySecondSet={'Python', 'Java', 'C++'}
print('Elements of second set is= ', mySecondSet)
#Updating the sets
myFirstSet.update(mySecondSet)
#printing the final set
print('Elements of final set= ', myFirstSet)
Hence both of these are merged together and as we are using the sets, the order of the final set is different and unarranged. Well, it is a good practice to check for the numbers of elements using the length function all the time.
We hope by now you have an idea of the for loop and how we use it with different data types in Python. Similar to the list, the programmers can access each and every element with the help of iterations (loops). So, let us review the elements of a set with the help of the for loop.
#declaring our set with the name to-do list.
ToDoList={'assignment', 'coding', 'prayer', 'washing cloths', 'doing dishes'}
#starting for loop
for work in ToDoList:
print(work, end=" ")
If we look at the output, we get the following results:
Hence, it was an interesting tutorial on the sets where we learned a lot about the topic and the details were interesting and related to our daily life. At the start, we saw the basic definition and a brief introduction to the topic. We have seen some properties of the sets that were resembling the types of sequences but these were also different in many ways and we not only studied them in detail but practically proved them in the Jupyter notebook. It was nice to use the Jupyter notebook that we are going to use onward in this series. In the next tutorial, we will put light on some other features so stay tuned with us because we are preparing our next lecture on Python.
Hey peeps! Welcome to the new lecture on the sequence data type, where we are discussing the range data type. We are interested in working on deep learning, and for this, we are learning the Python programming language from scratch. If we talk about the previous episode, we saw the byte and byte array methods that were amazing for converting the different data types into bytes. The current lecture will discuss the range data type, which is slightly different from the other types of sequences, so students will learn new and interesting concepts in the lecture; however, before we get into the details of our topic, take a look at today's highlights:
What is the range function?
How can you elaborate on the syntax of the range function in detail?
What are the three types of range functions?
Give us some examples of range functions in Python.
What are some basic questions the answer of which should be kept in mind while using the range function?
The answer to each question above will be provided before the end of this lecture. All the examples will be tried on TensorFlow for better understanding.
The range is a type of sequence in the data type that also represents the group or collection of the items together in different ways, just like other types of sequences. It is also the built-in function in Python, and while using it, the programmer gets the range object. The range is one of my favorite data types because it is easy to use and, in just a few simple steps, it provides us with the sequence of the integers according to our choice. Usually, the loops play an important role while dealing with the range function. Right now, as we have not learned about loops, you will simply have an idea of the workings and output of this data type.
The good thing about using the range function is that, unlike loops, the programmer does not have to use the logic behind the series but just has to put the values in the range function, and the results are great. Keep in mind that the range function is used with the loops, and there is less versatility in the range function when compared with the simple logical loops, but for basic workings, it is important to learn about the range, and this function is great.
The syntax of the range function is also easy, just like its group mates. You have to know about the three parameters and will determine all of them according to your requirements:
MyRange=range(start,stop,step):
for i in range(MyRange)
print(i)
Here, the semicolon at the end indicates that the syntax of the range function is complete, and the compiler now has to calculate the range arguments. Furthermore, if the programmer wants the result to appear on the same line as the interval, he can add end=" " at the end of the print. In this way, the compiler will not jump to the next line, but the results will be printed on the same line with a space between each element. Of course, the programmer has to save the result of the range function into a variable so that it can be used in the other functions. But here, it is important to mention that all of these parameters are not compulsory, but the range function gives you the independence to use one, two, or three of them.
The for loop is the iteration in the programming languages and you will learn them in detail in the coming lectures but for now, keep in mind that the range function alone can not do anything but it is fed into the for loop so that compiler can work on the iterations. The variable (usually i) is used in this loop and the results of the range function are input in this loop.
Another thing that must be mentioned here is the programmer has to choose the number of arguments according to the complexity of the series of numbers he or she wants. So here are the details of each case:
This is the most basic type of range function, in which the programmer simply specifies the point where the compiler has to stop making the range series. In all types of range functions, there is always a need for a stop parameter. Three things are to be mentioned here:
By default, the range starts at zero, and if the user does not have any particular choice for the start, the range function can be used with the only stop parameter.
Only whole numbers are printed on the screen.
The stop number, which is the limit of the range function, will not be printed on the screen.
When you put this value equal to zero, the result will be an empty range and you will get nothing.
for i in range(3):
print(i,end=" ")
Just think about the case where the default value, which is zero, is not to be used. Instead, the programmer has the option of printing the series of numbers without missing any of them and then specifying the start and stop ranges in the range function. But, as in the previous case, the stop number will not be printed on the screen, so you have to give the range of stops that you do not want on the screen, but the number before it is required there.
for i in range(3,34):
print(i,end=" ")
The third function, as expected, is the complete range function, into which the programmer feeds another step parameter. With the help of this, the programmers are able to get the series of numbers that starts from the point they want and have uniform intervals between the numbers and the ending point that is expected by the number. In short, the whole series is under the control of the programmer, but you have to notice that the steps are always uniform. The step function must not be zero and you will get the reason for this statement soon in this lecture. We can put the step value in the code discussed above and in this way, if 2 is the step value, the programmers will have half of the series as given above.
for i in range(3,34,2):
print(i,end=" ")
Here comes the action because, till now, the examples you have seen are simple examples with a simple series, but now, we are dealing with some exceptional cases that will clear some related concepts in your mind. We have divided the examples into some questions, and we will try to get the answers with the help of codes:
Till now, integers are being used in the range function but we know that integers and floats are the two most related data types and will try to attempt the range function with the help of floating values as the parameters.
for i in range(3.5,77):
print(i,end=" ")
As you can see, the compiler is throwing the error that it is not possible to use the float in the range function because it is designed only for integers. The same program will run when you remove the decimal part from the first value, which is the starting point.
Let me tell you the interesting way to get the range series with the help of inter tool chain method. But before this, you have to look at the basic definition of this tool.
“The iter-tool iterator is the pre-define module in python that provides the complex applications of the iteration in simple ways. The methods are defined in this module, and the programmers have to import them before using them.”
So, the chain method is also saved in this method, and when the programmers need to use them in a different way, they simply use the import keyword and use it in programs. As we are dealing with the range function, the iter-tool chain function is used to connect the results of two or more results in the form of a single series. Have a look at the code given next, and then read this paragraph again to get the point.
#import the chain method from the iter-tool library
from itertools import chain
# Printing two methods in a row
print("Concatenating the result")
MyChain = chain(range(4,7), range(34,55,2))
#using the method in the range
for i in MyChain:
print(i, end=" ")
The extraction of the concepts used in this program:
We can import the chain method from the library of itertools that have the iteration tools in it.
To import the method, we use from and import keywords that are represented with the green bold colour in the program.
Concatenation is the process of connecting two or more data types into a single line.
When using concatenation, the for loop is used by making a variable and saving the results of two connected ranges together in the variable.
The independence to use the number of arguments between one to three is the same in the concatenation as in all cases.
In the for loop, when using concatenation, only a variable is used.
The other way to get the same results is by using both ranges with the for loop, but the code will not be very clear in that case.
If the programmer wants to get the results in column form, he or she can simply delete the “end” part in the code.
The simple answer to the question is yes, and when we go into the details, the range function simply gets the indexes the programmer wants and can provide them with the single values they require. In simple words, the programmer tells the range function its stop value, and it assumes the whole series and picks the one number demanded by the programmer. The stop range is described in parentheses when the index to be picked is mentioned in the square bracelets.
#Give the range and pick the element through the index
MyRange = range(5)[2]
print("3rd element out of 5 =", MyRange)
print()
MyRange = range(3,34)[23]
print("23rd element of this range with start and stop value =", MyRange)
print()
MyRange = range(28)[5]
print("5th element of this range with start, stop, and step value =", MyRange)
Hence, the programmer can make a range of choices and then pick one element.
During the discussion of step, we saw the basic discussion of the step argument but keep in mind, if the programmer does not want the step function, he can simply ignore it. There is not need to input the step function as zero because, in such cases, the error will be shown on the screen.
for i in range(3,23,0):
print(i,end=" ")
Hence, from the above code, it is clear that the range of the stop argument is always greater than zero. Moreover, in the same code, if the value of the step argument is greater than the stop argument, it just shows the starting point of the range and does not give the other values or any errors because logically, it is true.
Truss, in this lecture, we saw many interesting concepts about the type of sequence called range function. This is a pre-defined function that is used to represent the group of numbers, and we can control the starting, ending, and interval values between the series of this number according to our wishes. This is always used with the for loop, and different cases of range functions were discussed in this lecture. Stay with us for more Python tutorials.
If you plan to run a start-up, it helps to have some basic understanding of IT management. Here are some key pointers on how best to manage your business IT systems:
You should know how your IT systems work and how they can be managed. This will help you keep them working well, which is especially important if your business depends on them for important functions like payroll or customer service. The earlier in the process that someone understands their role and responsibilities in running the company's technology, the better off they'll be when troubleshooting problems arise later on down the road.
Make sure your hardware and software are secure.
Ensure that you have the right licenses for your software.
Use cloud storage for your data security and recovery.
Conduct regular backups using your cloud system or external hard drive
Use a firewall to protect your network from unauthorized access.
Install antivirus, anti-spyware and anti-malware software on each computer (and smartphone) in the organization so that all data can be protected from outside threats.
Cloud storage is one of the best ways to store your data in a secure, accessible and reliable way. Further, Cloud storage makes it easy to access your files from anywhere and at any time. It reduces costs because you don’t need to buy or maintain expensive hard drives, which means that you can save up money for other things like marketing materials or new equipment.
Cloud storage also has some great benefits: like It keeps your information safe from hackers who might want access to it so that they can steal it or sell it on black market sites.
It's important to have a backup of your company's data , which is why you'll need to take steps to ensure that it doesn't get lost or damaged. The most basic way of backing up your files is by using an external hard drive or USB. If you're using the cloud system provided by your hosting provider, then there is also an option for automatic backups (but this might not be suitable for all businesses).
You should also keep in mind that not all kinds of information will fit onto one computer file—for example, some documents contain links between multiple pages; others have embedded images that could be lost if they weren't backed up before being deleted from their original source material.
If you need help with an IT issue or have technical questions, it is essential to get it resolved quickly. This can be a time-consuming process and require specialist skills that many startups don't have in-house.
It's also important to keep your business running smoothly as well as making sure that any problems are dealt with promptly by the right people at the right time (and not just left until later). While hiring an IT expert may seem like a great idea at first glance—especially if they're willing to offer their services for free—it's more likely that you will end up spending more money than you would have had by using other options.
It's important to understand how your IT systems work, so that you know what services you need from providers and how to keep them working well. This will allow you to make informed decisions about what kind of support is required in the future. If a provider offers a specific service or product, then it might be worth considering whether this meets your needs rather than buying something else that does not meet those needs directly. For this purpose you can use different IP for different locations to get best advantages from expertise. You can also use IP from Saudi Arabia to check
any issue regarding IT management if your clients are based in UK or USA. You can also hire expertise from these locations too.
Because If there are any areas where an expert could help with advice or training then this would also be beneficial for both parties involved - especially if there are technical problems with one part of the system which could be fixed by someone who has experience with similar systems elsewhere.
If you are planning to run a start-up, it helps to have a basic understanding of IT management. Make sure your hardware and software are secure, use cloud storage for your data security and recovery, get IT support when you need it - both for advice and for technical issues. This is absolutely essential if you’re not an IT expert yourself.
Hey pupils! Welcome to the new tutorial on deep learning, where we are in the section on Python learning. In the previous lecture, we were discussing the tuple data type, which is a sub-class of sequences. In the present lecture, the discussion will be about the byte sequence and byte array. The whole discussion is cleared with the help of practical implementation in TensorFlow by using simple and easy codes. If you understand the concepts of list and tuple, then this lecture will be easy for you. Yet, before going into the details of this topic, you must know the highlights of the content discussed in this lecture:
What is the byte method in Python?
How can you use byte in TensorFlow?
What are some examples of bytes?
Give examples of error handling in bytes.
How can you convert integers into bytes?
What is a byte array?
What is the difference between bytes and byte array methods?
Moving towards our next sequence is the byte method which has interesting applications in Python programming. A byte is the collection or group of byte numbers and it can hold multiple values. The interesting thing about the byte is, these can not hold the negative numbers that are, the values in a byte can not have the minus sign with them. One must keep in mind that these have a range between 0 and 255 only and you can not store more or fewer values than this range. For example, if you want to add 267 or -56 in a byte, this is not possible in it. The main purpose of using this function is to convert an object into an immutable byte-represented object of which, the data and size are specified by the programmer.
To make sure you are getting the whole concept, let us practice it on TensorFlow. To start, have a look at the instructions:
Search for the “Anaconda Navigator” in your windows search panel.
In the environment section, search for the Jupyter Lab.
Launch the lab and wait for the browser to show you the local host on your browser.
Go to the new cell and start coding.
You have seen we made a byte with the different types of elements in it. Now, what if we made the byte with values that exceed its values? Let us check it with the help of the following code:
The byte is a method and to use it well, the syntax must be known. For using it, there must be three parameters that have to be decided before starting, the detail of syntax and the parameters is given next:
byte(src,enc,err)
Here, the three parameters are defined:
src=The object that has to be converted. It is the source object and has superficial characteristics.
enc= it is the encoding that is used only when the case object used in the formula is in the form of a string.
err=If the error occurs during the conversion of the string is not done properly in the step mentioned before then, the logic given here will be used to overcome this error.
Now, using the information given above, we are going to discuss the example to elaborate on the whole process. The bytes when displayed on the output have a small be with them to show it is a byte. Just look at the code below and we are going to tell you the detail after that.
msg = "We are learning Python"
string = bytes(msg, 'utf-8')
print(string)
Here, in the first line, the message is declared and stored in the variable with the name ‘msg’.
The byte function is used in the second line and as the two parameters, we are using the message declared first as the source and the encoding technique is the utf-8.
The result of this function is then stored in the variable ‘string’.
The results are printed at the end. The small b at the start of this message indicates that it is a byte and single quotation marks are the indication that our result is a string.
Here, utf-8 is the indication of the Unicode transformation format, and it is encoding the string into an 8-bit character. So, in conclusion, we can say that a byte is used to convert the string message into an 8-bit character string. The application of the byte function is at a higher level of programming.
Now moving towards the next example, let us check for a relatively large code. This code gives us a demonstration of how we can use different cases of coding in a single piece of code and we will use here the empty byte, number-to-byte conversion, and list-to-byte conversion using single lines.
num = 4
list = [23,76,23,78,34]
#conversion with no argument
print ("Byte conversion with no arguments : ", (bytes()))
# conversion of number into string
print ("The integer conversion results in : ", (bytes(num)))
# conversion of list into string
print ("The iterable conversion results in : " , (bytes(list)))
The number is converted into bytes as expected, and when we try the same method for the list, where a group of numbers are to be converted into bytes, this function is capable of doing so. The output is strange for the non-programmers but the conversion is perfect here.
As we have mentioned earlier, the string is converted with the help of the byte function, but error correction is important in this case. There are some keywords that are suggested as error correction techniques. All of these will be discussed in the next section.
The second parameter of the byte tells us that we have to provide the encoding scheme, but what if the encoding process is not completed due to some error? The method parameter specifies the way to handle that error, and there is more than one way to handle it; therefore, here are the details of each way to handle the error for you. Keep in mind, coding is a vast field and the whole control is in the hand of programmers. Thus, not all errors are handled with the same kind of solution, so you must know all the ways.
The first error handler on the list is the "strict" keyword. It is used when the programmer wants to get the Unicode decoder error when the compiler is not able to convert the string into a byte.
The second error handler in the list is the keyword “ignore." What do you do when any procedure is not under your control and you are not able to do your duty? In the case of the compiler's workings, when it is faced with the same situation, it ignores the part with the error and moves towards the next line. In this way, the result obtained is not complete, but you get the answer to other parts of the code. It works great in many situations, and sometimes, you get interesting outputs.
The third one has a nice implementation of error handling. With the help of this handler, the error that occurred in the string will be replaced by a question mark, and then the compiler will move towards the next character. In short, the error will be gone, and instead of skipping the error, there will be a question mark that will indicate the error.
All the discussions above will be cleared up with the help of the code given in the next line. The error in all the problems is the same, but the outputs—or, in short, the way the compiler deals with the error—are changed.
#Declaring the variable with the string having the error in it.
MyString = 'PythonfÖrDeepLearning'
# using replace error Handling
print("Byte conversion with replace error : " +
str(bytes(MyString, 'ascii', errors='replace')))
# Giving ascii encoding and ignore error
print("Byte conversion with ignore error : " +
str(bytes(MyString, 'ascii', errors='ignore')))
#using strict error Handling
print("Byte conversion with strict error : " +
(bytes(MyString, 'ascii', errors='strict')))
Here, when we talk about the “strict” error handler, we get this error with the pink box because it is just like a simple error, as we are calling it, and we want strict action behind the error. Moreover, in the other two lines of code, the output is a little bit different, as the description tells us.
The conversion of bytes does not end here; instead, it has other conversion techniques as well. Python gives programmers the ease of converting integers into bytes, but it is, somehow, a little bit easier than string conversion because it is a pre-defined function and you just have to insert the values accordingly.
int.from_bytes(bytes, byteorder, *, signed=False)
In the syntax given above, three things are to be noticed;
It is the byte object that has to be converted into an integer.
This function determines the order in which the integer value is represented. The value of byte order can be "little," which stores the most significant bit at the end and the least significant bit at the beginning, or "big," which stores the MSB at the beginning and the LSB at the end. Big byte ordering computes an integer's value in base 256.
By default, the value of this parameter is false, and it indicates whether you want to get the 2’s complement of the value or not.
To understand the bytes well, you have to know first that Python is an object-oriented programming language; therefore, it works by creating objects from different pieces of code. While working with the byte function, an immutable byte object is formed. The size of this immutable sequence is just as large as an integer that ranges from 0 to 254, and it prints the ASCII characters. It is a Python built-in function that has many interesting applications in a simple manner. As with the other types of sequences, tuples also contain a number of items, but they can also be empty. The only conditions are that the data should be enclosed in parentheses and the elements should be separated with commas. Another important piece of information about this data type is that it cannot be changed once you have declared it.
As the name of this data type resembles the previous one, both structure and information are also similar. Byte arrays also have the parentheses representation, and you can use different data types in them, but the only difference between the byte array and the simple byte in the sequence is that the former is immutable, and you can change the size of the byte array after you declare it in Python. It makes the byte arrays more useful than the simple bytes in the sequence.
The reason why we are discussing both of these data types here is, there is only a slight difference between the byte and the byte array. You can modify the byteArray and that is not possible in the byte. So, have a look at the code below where we are modifying the array declared by
ourselves.
MyArray=[99,5,1,34,89]
print('The byteArray is: ', MyArray)
#Modifying the ByteArray
MyArray[2]=56
print('The modified array is: ',MyArray)
But for this, you must keep in mind that you can only use values between 0 and 255. All the other conditions are the same as the bytes.
Hence, today we have seen a piece of great information about a special type of method that converts different types of data into bytes in a specific manner. We have seen the details of methods that must be known to a Python programmer. The whole discussion was put into TensorFlow in the form of codes, and we get the outputs as expected. The next lecture is also related to this, so stay with us for more information.
Hey peeps! Welcome to another tutorial on data types in Python. Our purpose in Python education is to get a grip on the basic concepts so that we may work on deep learning easily. In the previous lecture, we read a lot about lists as we are working on the subtypes of the sequence data type. In the present lecture, you are going to understand more types of sequences. If you know the list well, this lecture will be a piece of cake for you. We will start the introductions in just a bit, but before that, there must be a quick review of the topics that you are going to understand:
How do you introduce the tuples in Python?
What are some important characteristics of a tuple that must be kept in mind when we are dealing with it?
How can you practically perform the tuples in TensorFlow?
How do you associate tuples with the list?
Can you delete, update, add, or delete the elements in a tuple? If yes, then how?
Which is the way to delete the tuple entirely?
All of these are important interview questions about the tuple and if you are dealing with these data types, you must know and perform these by yourself in TensorFlow. All the information about tuples will be discussed with you and you have to practice more and more in the code to understand the concepts.
For the concept of a list, we have given you the reference of an array. This time, we will be mentioning the name of the list for better understanding. If we talk about the topic of today, a tuple is also a group of items that are arranged when required in Python. The working and the definition of the tuple seem like a list because both of them are types of a single data type, which is the sequence. But there are some differences that make them ideal for different kinds of situations. The following points will make this more clear:
The tuple is represented by enclosing the data in parentheses.
A tuple is immutable, which means that once you declare the items in the tuple, you can not change them as you can in a list.
When you try to change the value of the element in the tuple, an error is shown on the screen, so while declaring the new tuple, you have to be very clear about what is on your mind and what you want to do with the tuple.
In a tuple, you can also use the function in the tuple and it becomes easy to perform different tasks through codes.
A single element in the tuple is necessary to build the tuple. In other words, unlike the strings, the tuple must contain at least one element in it.
In the tuple, any data type can be saved whether it is boolean, int, or string. Moreover, there is a possibility to add different types of data in a single tuple. So we can conclude that we can have a homogeneous or heterogeneous tuple according to our choice.
As we have mentioned, when using the tuple, we get the ordered list of objects, which means we get the specific order, and this order, once mentioned, can not be changed, unlike other data types of the sequence.
Not just the order, but the size, sequence, and entries are unchangeable, so during the initialization and declaration of the tuple, the concept must be clear about what you want from the particular tuple.
Another thing that must be kept in mind is, the tuple has the index form, and therefore, every element has a specific number of indexes. This is the reason, the elements are easily accessible, and hence, you can also have duplication in the tuple. The elements are recognized by the index number, and therefore, the compiler knows when which element is being called.
The length is the number of elements in the tuple, and we have read about the length function in the previous lecture. Similar to the list, a tuple can also be used in the length function, and the programmer gets the length of the tuple. Right now, this function is not looking very attractive because we are dealing with small tuples. But take the case in your mind when the tuple contains hundreds or thousands of elements and get the length of the tuple in just a few moments.
It is now time to go over some basic tuple-related code using TensorFlow. The steps for starting it are the same as those we always follow; have a look at these steps:
Open your Anaconda Navigator by browsing it from your window panel.
Search for the Jupyter lab in the environment section.
Wait for the PC to open the new tab in your browser.
Go to the new cell.
Start coding.
With the help of code, we will try to create a tuple, and then, by using the function on it we will try to retrieve the data from the tuple in different ways. So, have a look at the code given next and then guess the output in your mind.
print('Make a tuple with stationaries item')
myTuple=('pen', 'paper', 'eraser', 'pencil', 'sharpener', 'notebooks')
print('The tuple has following items: ',myTuple)
print()
print('print only the third item of the tuple:', myTuple[3])
print()
print('print the item by using the index: ',myTuple[1])
Now, have a look at the output. Is it exactly the same as you were expecting?
You can see how simple it is to get the item from the tuple when it is declared in the tuple. This is the same as what we did with the list. But what if we want more than one element from the tuple at the same time?
print('Make a tuple with fruits and prices in dollars')
myTuple=('strawberry','kiwi','banana','orange','apple', 2.5,3,12, 4,6)
print('The following items in the fruit shop are available: ',myTuple)
print()
print('print only the forth fruit of the fruit shop:', myTuple[-7])
print()
print('print the name of only fruits: ',myTuple[0:5])
Looking at the code, you will observe that the negative number is used in the index. This is the way to tell the compiler that we are counting from the end of the index. Moreover, the index on the end side starts at 1 instead of zero. Another point to notice is that the start and end limits are specified by separating them with the colon, and as a result, we get the whole tuple in the output.
A new concept that is to be shared is that when you are providing the limits to the tuple, you have to take care that the right numbers are being used because it will then not work properly. You must be thinking that the compiler will throw the error when you feed the wrong limits in the tuple, but instead, the output will be empty, and you will get the parentheses with nothing in them.
Another thing that must be mentioned here is that you can check whether the tuple has a specific element or not. For this, we get the help of a statement. We know that, until now, we have not learned about the statements, and therefore, we suggest just looking at the code and output to understand the concept.
print('Make a tuple with fruits and prices in dollars')
myTuple=('strawberry','kiwi','banana','orange','apple', 2.5,3,12, 4,6)
print('The following items in the fruit shop are available: ',myTuple)
if "apple" in myTuple:
print("Yes, 'apple' is in the present in the fruit shop")
This program searches for the elements specified in the “if condition” and then prints the result on the screen.
If you are reading this lecture from the beginning, you must be thinking we have mentioned again and again that a tuple is immutable and unchangeable. Yet, programmers have the logic and solutions to every problem, and if the programmers have specified the tuple and want some changes made to it, they can do so with the help of the list. It is one of the reasons why we have discussed the list before in this series. Have a look at the following code and output, and then we will discuss it in detail.
print('Make a tuple with fruits and prices in dollars')
myTuple=('strawberry','kiwi','banana','orange','apple', 2.5,3,12, 4,6)
print('The following items in the fruit shop are available: ',myTuple)
myList=list(myTuple)
print(myList)
myList[2]='pear'
print(myList)
myTuple=tuple(myList)
print(myTuple)
Copy this code and run it on your TensorFlow, you will get the following output:
First, look at the brackets carefully and check the output in a sequence with the points given next:
At the start, the string message is shown, which shows the main title of the tuple.
The name of the tuple here is “myTuple” and it contains the fruits’ names and prices.
To make the changes in the tuple, we have to use another approach, and we know the reason why. For this, we are using the list. It is possible to convert the list into a tuple and vice versa, and we are doing the same in this. We are just using the name of the data type as a function and inputting the name of the data type to be changed. So, we changed the tuple into a list and then made the changes according to our choice.
By using the index number and feeding the value into the list, the list is updated. This can be observed with the help of square brackets.
Once we have seen the updated list, we can now easily convert it into a tuple again.
The conversion is done with the same procedure, and we get the updated tuple.
This was a simple example, other operations such as the addition of the new element, deleting the elements from the tuple, removing the single elements from the tuple, etc. are done with the help of lists.
Now that you know all the ways to initiate, use, and update the tuple in detail, a last method for the tuple is ready for you. In some cases, when you do not want to use a particular tuple for any reason, such as if you are no longer using it, you can do so in just a simple step. This is used in the programs when long calculations and collections of data types are needed for a particular time and then there is no need for them anymore. So for this, we use the delete operation. The programmers have to simply use the del keyword before the name of the tuple to be, and the compiler has to do its job well. Have a look at the example given next:
Tuple = ("Artificial Intelligence", "Machine Learning", "Deep Learning")
print(Tuple)
del(Tuple)
print(Tuple)
So, when the compiler was on the second line, it showed us the results, but on the next line, when we deleted the declared tuple, the compiler was not able to show us the result because it had been removed from its memory.
So, it was an informative data types lecture in which we clarified many concepts about tuples. These are the data types that belong to the class of sequences. We read a lot about it and started the discussion with the introduction. The tuple characteristics were thoroughly discussed, and then, with these in mind, we tested the functions of the tuple using TensorFlow examples. We attempted similar examples and carefully observed the operation of tuples, which also involved the list. In the next lecture, you will know more about the data types in Python, so stay connected with us.
Hey, peep! This is a connected tutorial from the previous one where we saw the detail of numeric data types. This time, we are moving forward with the other data types in Python. We are understanding all these concepts with the help of examples and practising the little but understandable codes in TensorFlow. Different types of operations are also performed on these data types so that you may have an idea of why we are differentiating all these data types and how we can categorize all of them into different groups. Keep in mind that all of these concepts are for deep learning, and we want to make sure that we will not face any problems in the complex work of deep learning; therefore, we are moving slowly and steadily in Python. So, have a look at the content you are learning in this tutorial, and after that, we’ll start the introduction.
What are the strings in Python?
How do you declare the string in different ways while working in Python?
What are escape sequences in Python?
How can you use the triple quotation in Python and why it is useful?
Each concept will be discussed with the help of simple and easy codes and the description of each of them is discussed in detail in this lecture. This is a connected part of the lecture that was discussed in the last lecture and other data types will be mentioned in the next lecture.
A string is nothing but the combination of different alphabets in a specific sequence. In programming, the concepts are a little bit different from those in the real world. The way we speak in English is said to be "string" in the programming language, and it is an important data type in any programming language for non-programmers, anything that comes on the screen must be easy to understand, and string is the best way to print the message on the screen. Let us define the string in simple words.
"The string is the sequence of characters or alphabets that specify the message on the screen and it is denoted by 'str' in Python."
We always say that python is simpler and easier than other programming languages, and it is true for the concept of string as well. In Python, the string can be denoted by using single or double quotation marks and the usage of commas is according to the choice of the programmer. In other words, you can use the single or double inverted commas around the alphabet to represent the string. Moreover, you must know that string has no limited length same as in the case of integers. The only thing that limits the length of the string is the memory space of the system you are using. Have a look at the syntax of the string while you are working on Python.
First of all, you have to look at the syntax of the string. The syntax of all the data types is the same; therefore, we are mentioning just this one, and after that, you will have an idea of how to implement the other data types. We have mentioned in the previous lectures that you just need the name of the variable and then the value of the variable in the form of any data type you want, so the syntax is given as
string = "I am learning Python for Deep learning."
The name may be anything, but it is important to use inverted commas (either single or double). TensorFlow will make this clearer, but there is a short procedure to get TensorFlow up and running.
Search for the “Anaconda Navigator” on your PC.
In the environment section, click on the Jupyter lab and launch TensorFlow.
The new tab will open in your browser with the name “Local host.”
Go to the new cell and start coding there.
Now, you have to write the following code in the new cell and run the program to get the output.
string="I am learning at TheEngineeringProjects"
print(string)
a=" "
print(a)
b='Python is best for Deep learning'
print(b)
The output of this code is given as:
From the code and output, we can conclude with the following point:
The name of the variable may be anything.
We can use single or double inverted commas for the string and the result will be the same.
A string may be an empty space so we can say that length of the string is zero to positive infinity.
The output of each print function is always shown in the next line in normal conditions.
So, the best way to show any message to non-programmers is in the form of a string.
In most high-level programming languages, there are certain words that are chosen to be used for a special task in Python with the help of their special sequence. These are known as the escape sequence. These are defined as:
"The escape sequence in Python is the combination of characters that, when used inside a string or character, does not show itself but does the specific task according to its functionality."
It is important to notice that these are important concepts in Python because it is very difficult and in some cases, impossible to do the same task without using the escape sequence. Now, have a look at some of these in the next section and you will get the detail of some important escape sequences.
As you can see, in the previous code, we made a space between two lines with the help of an empty string. But, what if we want to print a new line in the message? For this, we use a special operator in the string message and it is used in places when the output is long or there is the need for more than one line to represent the message more clearly. The operator is a backslash with an “n” that can be used at any place in the text. Have a look at one example to do so.
print('The backslash with "n" is used to \nprint a new line')
The output of this single-line program is interesting.
It is important to notice that if you want to work with this operator, you have to use it properly. You can not use any additional space between the text and this new line operator; otherwise, you will get an error message from the compiler.
We all use the tab on the keyboard but what if you want a space between the text? You can do so by applying the space between the text while you are printing the message but it is not the professional way. To do this with convenience, just like some other programming languages, Python has a special operator. Same as we use the n in the new line operator, if you use the t with the backslash, you can print the space between the text that is equal to eight space bars.
print('The backslash with "t" is used to \tprint a tab space in the line')
Let’s see what we have in the output of this code.
Same as these operators, we also have some other commands that do the work similar to this and the syntax of all of these is the same. For the convenience of the reader, we have made a table that contains all the information about these operators. Have a look at the table and after that, we will run all of these at once in our code in TensorFlow.
Name |
Representation |
Description |
New line |
\n |
It creates a new line in the text even if you are using it in the middle of the line. |
Tab space |
\t |
It is used for the space tab between the text. |
Bullet |
\a |
For the bullets in the text, we use this operator. |
Delete space |
\b |
To delete the space in the text, we use this operator. In this way, the text obtained only removes the space from the place where this operator is being used and the other text remains the same. |
Ignore the line |
\r |
By using the working of this operator, the text before the operator is deleted, and you will get the text after this operator only in the output. |
Arrow |
\v |
If you want to show a small arrow in the text, you will use this operator. |
To test each of the commands discussed before, we are rushing towards the TensorFlow, where we are using all of these in a similar ways to show the difference between all of these. Keep in mind, the syntax of each of them is the same. These are not the proper functions but are the pre-defined commands that are used less commonly in Python. Here is the homework task for you. You have to look at the code we are giving below and without cheating will guess the output.
print('We use \ncups for tea')
print('The tab create \teight spaces in the text')
print('\aBullets can be made using this operator in Python')
print('If you want to delete the \bspace, you can use the operator in Python')
print('This part will be ignore \ronly this will be printed on the screen')
print('\vThis small arrow looks cute')
Once you have guessed the output, now check this in your compiler, and then match the output with the one that is given next in the image.
The last arrow can also be used as the bullet in the text you want to show on the screen. Another task for you is use the different text and practice the code and string with your own message. It is the simplest and interesting task that you must try on your TensorFlow.
Here is another way to indicate that you want to declare the string. This is a less common way to represent the string, but if you are studying the string, you must know this. You can use double and single inverted commas around the text at the same time, and this will not cause any errors. Let me tell you about the workings of this kind of string. After that, I’ll show you how you can use it for different purposes.
print('''Deep learning is the subclass of the artificial intelligance''')
So, we are using three quotation marks around our text and getting the same output as for the single and double inverted commas. Here is the output:
The advantage of using this method is, you do not need any new line operators to start the text on a new line, but you will write the code as you want the output.
This is a more interesting and convenient way to write your text. Right now, you must be wondering why we are highlighting such uses and creating this hype. Yet, you must know, the aforementioned ways of writing the codes will help you a lot when you will go to the complex and long codes for the deep learning. One of the application of this way to declare the string is in this lecture. We can use this way to declare the string and can perform all the escape sequence command by declaring a single string in different lines so that we may not have to write “print command” every time when we are working with a new escape sequence.
Hence, we have read a lot about strings today. It was an interesting lecture where we saw what strings are and how we can use them in our coding in different ways. We say the representation, working, and the escape sequence between the string. The syntax of each case was clarified with the help of examples in TensorFlow. You will get information about more data types in the next lecture.