In-person events provide a unique ambiance and social experience that will continue to serve a significant purpose to businesses. However, the innovation, convenience, and affordability of digital tools and resources have sparked an influx of virtual events, from team and client meetings to launch parties and webinars (and everything in between). Of course, not all virtual events aren’t created equally, as they require thorough planning and execution. Acquiring the appropriate technologies is at the heart of factors to consider.
Are you interested in hosting virtual meetings or corporate events? Check out this list of must-have technologies to make the experience more enjoyable for you and your audience.
Planning, organizing, marketing, and monitoring virtual events is much easier when you have a dedicated website or page. Much like a wedding website for engaged couples, a site or page containing program-related information is ideal. It’s a one-stop shop for your target audience to learn essential details such as the event's title, date, time, and cost. It’s also a lot easier to integrate into your marketing campaign.
Your event website or platform should provide essential instructions for attendees, tech requirements, the agenda, names and contact info for speakers, audience feedback, promotional videos , and images from your last event to give viewers more insight into why they should attend.
Traditional methods of managing registration and payments, like mailing invites or sending out emails, are time-consuming and costly. So, if your event website doesn’t include RSVP or registration options, you’ll want to create a link or page for your guests.
Create branded, attractive, and user-friendly forms for attendees to supply their necessary information. Include a payment process that includes various methods, including credit/debit cards and online payment processors like PayPal, Stripe, or Venmo, to ensure everyone can cover registration costs.
Lastly, ensure that whichever registration and payment platform you use is secure, as data breaches are becoming more prevalent. Encryption, password protection, and two-step authentications are all effective ways to keep your guests’ data safe.
Many virtual events are streamed live for audiences to view in real time. However, you’ll need a secure platform to stream videos. Some of the most popular service providers include YouTube, Twitch, Facebook, and Periscope.
When deciding which live streaming platform is best for your virtual event, keep factors like ease of use, security, content management tools, video editing capabilities, monetization, digital rights, and analytics. It is also worth considering the preferences of your target audience.
Keeping your audience engaged is crucial, whether showcasing your latest product features, training your team, pitching to prospective clients, or giving a speech on industry-related topics to colleagues. However, it’s more challenging when hosting a virtual event. Everyone is accessing your content from various environments and devices and has different needs.
One way to keep guests interested in your virtual event is to utilize audience engagement tools like live polling software
. The platform works with video conferencing and live-streaming sites, allowing hosts to generate questions and get instant feedback from their audience. The analytic findings can provide insight into which topics your audience is most interested in, so you can fine-tune your event to meet or exceed their needs.
Other audience engagement tools include surveys, Q&A sessions, games, ice-breaking activities, images, videos, and learning modules. They all require audience participation and provide virtual event hosts with invaluable data that can be used to further their agenda and accomplish their goals.
Virtual events offer conveniences and economic advantages that in-person experiences can’t. They save individuals, business owners, and audiences time and money while helping them to overcome barriers like distance. However, hosting a virtual event is more than sending out a few emails, creating a meeting link, and hoping for the best. It starts with selecting innovative technologies to simplify the planning process and enhance the audience experience. Investing in the above resources will undoubtedly make your next virtual event one to remember.
Small businesses rely on a specific set of technologies but optimizing the way they are used can be challenging. It is a task that requires ongoing assessment, and you will need to be able to evaluate the different options. Making these adjustments requires you to be proactive and look for potential issues before they come up. There are several ways you can optimize the tech you use.
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Technology can help your fleet be more efficient, especially as your business grows. One way you can improve safety is by implementing GPS systems. Many fleets use GPS to make their operations more efficient, and more applications are being discovered every day. Are you wondering how does GPS work? You can review a fleet manager’s guide to GPS online.
You may find it hard to get enough resources together to keep your technology working as it should, and it is also harder for organizations to reach talent, so you might not have enough people to manage your technology. This can lead to it becoming outdated and inefficient. Even if you have the ability to hire someone else, you may not feel it is worth hiring a full-time employee for this. A managed service provider can help you fill in the gaps because they will be able to support your devices, apps, and services. They perform the same function as an IT team, without the need to hire full-time employees and cover salaries or benefits.
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Having the best equipment can give your employees more to work with, but if they do not know how to fully utilize it, it will not be effective. If they make common mistakes, they could end up causing more issues that take longer to solve. Don't assume every employee is comfortable with learning new technology. Instead, take the time to train them regularly.
A common mistake is a few employees using too many resources on the network. This makes the network slow for everyone. This might be because of an employee doing personal things, like watching videos, on company time. The more people who do this, the more the strain on the network. Another common issue is emailing large attachments instead of putting them on the cloud for everyone to view. Consider blocking common streaming sites and educating employees on how to put files on the cloud.
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Today, work does not happen in only one location. Even if you have an office space, you may do some tasks in your home or while on the road. While your staff may work in the office for now, there may come a time when they have to do some tasks remotely as well. They will need mobile access so they can work while on the go. Large desktops are not the only thing you can rely on anymore. You will need to supply teams with gadgets that allow them to work from anywhere.
Consider replacing these large devices with tablets, phones, or laptops. One solution may be convertible laptops with detachable screens. These can be used as either laptops or tablets. They can be hooked up to a dock station for in-office employees so the information can be displayed on a monitor, and it is easily transportable if going from site to site. Smartphones can also help your workforce be more mobile. They can connect through VPN services, which provide a secure connection to your business’s resources.
No matter how much work goes into optimizing your current infrastructure, it will not stay optimized for all eternity. You will need to watch key metrics and performance to ensure things are going as they should. You may need to hire an expert to help you out, or you may need to use special tools. Still, having an optimized system in place is critical to ensuring business operations continue to run smoothly. In fact, it may be the difference between failure and success. Still, even if a mistake does not lead to failure, not optimizing things efficiently can negatively impact productivity.
Promoting yourself online and having a strong digital presence are almost required in today’s business plans. You can implement digital marketing strategies and tools to help you build relationships with customers online. You can send emails to your email list, maintain your website, and leverage your social media presence. While you may not need a tool with all the bells and whistles, you may want a tool that can help you manage relationships with customers. There are also customer relationship management tools that can help you email customers and build content on your website.
You may want to pick one that will help you keep your site updated. It can also help you promote services and products. Some offer dynamic features, such as the ability to create custom forms, which can help you drive sales through lead generation. Many have tutorials and templates, which means you can get professional looking results, even if you are not a web designer. The reporting section will allow you to gain insights into visitors and potential customers. The reporting dashboard can help you tell whether posts or ads are generating enough traffic. Plus, you can see if your visitors are taking the actions you desire.
Your internet connection is likely one of the most important parts of your operations. If you don’t have it, it would take a long time to process sales, and you could be deep in paperwork. Still, it’s often hard to find a good internet service plan. While it may be tempting to use a plan designed for consumers, it may not meet your needs, and plans designed for larger businesses may be too expensive or fancy for your small business. Spend some time shopping around and ask providers in your area if they would be willing to create a personalized package for you.
The cloud software industry has begun developing new working models that have been especially needed during the pandemic. However, even though the pandemic is almost neutralized, the cloud market continues to grow. According to a new Forrester report, global software spending will continue to rise, reaching a CAGR of 10.3% from 2021 to 2023.
Therefore, in this article, we will learn more about cloud technologies and their trends.
Let's start with the definition of saas development . This is a special software model that is provided on a subscription basis. And most often it is a cloud solution. Such a concept as software as a service is deciphered.
The cloud can be a very secure storage solution if implemented correctly, which is why there has been rapid growth in recent years. Today, more and more companies use cloud computing for data storage. This is an expanding technology market that has changed a lot over the years. For this reason, it is important to keep an eye on trends to keep up with the competition.
In 2022, the need for cloud cost management services is one of the main cloud initiatives, and further growth of these services is expected in the next year.
Cloud operations are typically decentralized, making costs difficult to predict or control. For this reason, although one of the goals of companies when implementing cloud services is to reduce costs, they are willing to invest in a service that can manage costs in the cloud.
Every day, investments in SaaS services are increasing. This growth is driven by the standardization of remote work environments and the emergence of SaaS tools for hybrid and multi-cloud environments.
For the first time, the lack of knowledge of the company's staff has become the main obstacle to the implementation of a SaaS solution in the cloud. A large number of companies claim to train their employees at a basic and advanced level.
Cloud SaaS providers should take advantage of this context and offer their customers a higher level of training in the use of their tools so that the company can consolidate and use all the solutions offered by cloud software. This is an opportunity to stand out from the competition and provide special value to the SaaS product. Software demos can be a good option when it comes to communicating this advantage as a vendor.
With the recent consolidation of new technologies such as 5G, a new approach to analytics and artificial intelligence is becoming more accessible to companies. Because of this, the percentage of companies that intend to implement artificial intelligence, machine learning, and deep learning solutions between 2023 and 2024 has grown significantly.
More than half of companies currently store less than 25% of their data in the cloud. Although the data move is starting to gain momentum, companies are expressing a desire to move their data to the cloud. Cloud service providers will see their customers' budgets increase and their industry expands, but they will also face an increasingly regulated context and will need to do what they can to ease this transition.
Multicloud is currently the preferred option for customers. Companies prefer a platform that integrates multiple clouds, both public and private, and traditional on-premise infrastructures to get the most value for services and pricing. Therefore, companies want to move to a multi-cloud environment to maximize the potential of software and reduce dependence on a single vendor.
Globally, cybersecurity continues to be one of the biggest concerns of customers.
Concern about cybersecurity when implementing cloud solutions is in line with the global trend and has become one of the main concerns of companies. Software vendors should be aware of this SaaS market trend and make cybersecurity a top priority for customers.
Therefore, when using such technologies, pay attention to the main trends and analyze the cloud technology you choose for SaaS.
Milling is a machining process that can create detailed or complex shapes from metal, plastic, or other materials. The CNC milling process involves using a computer to control the motion of a rotary cutter that removes material from a workpiece. This article will discuss things you need to know about the CNC milling process.
CNC milling is a machining process that uses computer numerical control (CNC) to cut materials precisely. It can produce complex shapes with high accuracy and repeatability from metal, plastic, or other materials. It is used for many applications, including prototyping, manufacturing parts, making moulds and dies, creating fixtures for production lines, producing 3D sculptures and more.
The most significant benefit of CNC milling is its ability to produce exact parts with tight tolerances. Other advantages include speed and consistency in production and flexibility in design since it can be automated easily compared to manual operations. Additionally, because it supports multiple types of machines with different tooling, it is a versatile solution for many manufacturing needs.
There are many different types of CNC mills available today, including vertical and horizontal mills. Vertical mills are designed for operations that require cutting or drilling straight down into material and have a spindle axis perpendicular to the table. Horizontal mills are designed for operations in which the workpiece is fed parallel to its length along an x-y axis.
CNC machines can mill nearly any material, from aluminium alloys to plastics, composites, and even hardwoods. It is essential to consider the specific properties of your material when selecting the best tooling for your application. Generally speaking, more complex materials such as steel require more specialized tools, while softer materials can be machined using standard-issue tooling.
CNC milling is often carried out in multiple passes, depending on the complexity of the design and material being milled. The most common techniques used in CNC milling include rough cutting, finishing, slotting, drilling, and engraving. Each technique has its tools that must be chosen based on the workpiece material and desired finish.
When looking for a CNC milling service provider, one must consider their experience with CNC machines and the type of parts they specialize in creating. Additionally, it is vital to look for certified CNC milling service providers with an established quality management system. Doing your due diligence on a CNC milling service provider will help you find the best fit for your application.
Safety should always be the top priority when operating a CNC machine. Proper training and use of personal protective equipment, along with regular CNC machine maintenance, are necessary to ensure safe operation. It is also essential to be aware of any hazardous materials or processes used during CNC machining so that proper precautions can be taken.
CNC machinists typically use CNC software programs to design and create their components. Popular CNC software programs include CAMWorks, Mastercam, Fusion 360, and Autodesk Inventor. Each CNC program has its own tools and capabilities that allow CNC machinists to create the exact parts they need for a given application.
When selecting the suitable CNC mill for your needs, it is essential to consider how precise and repeatable the parts will need to be. You must also consider what type of material will be machined and any special features that would benefit your application. It is vital to work with a CNC milling service provider with experience in the type of part you are trying to manufacture so that they can advise on the best CNC machine for your needs.
When getting started with CNC milling, it is essential to clearly understand the project goals and machining requirements. You should also be familiar with CNC machine operations, such as setting up tools and measuring material properties. It is also essential to have a plan for managing data so that any changes made during CNC machining can be tracked. Finally, it is beneficial to practice CNC milling on scrap materials before attempting to machine a final product.
CNC milling is a versatile machining method that can be used to create a variety of components from soft and hard materials. It is vital to research CNC machines, CNC software programs, and CNC milling service providers to find the best fit for your application. Additionally, safety considerations, as well as tips for getting started, should also be taken into consideration when beginning CNC machining projects. With the proper knowledge and equipment, CNC milling can open up many possibilities regarding part design and production capabilities.
Hi Guys! Hope you’re well today. I welcome you on board. In this post, I’ll walk you through Electronics DIY Projects to Improve Work from Home.
Electronic devices are not cheap and rightly so. Since you require advanced setup and technical skills to build something sophisticated and delicate. The good news is that you don’t have to spend a fortune on such devices since DIY electronic projects are the solution. You can make similar electronic projects you find online at home and save a lot of money. Some people prefer working on a breadboard while others prefer building on printed circuit boards. However, if you’re new to this field, we’ll suggest you start from the breadboard, before building your projects on the PCBs. The good thing is that you don’t require a big setup or advanced tools to work on breadboards. Basic computer knowledge and a few tools and electronic components would suffice.
Know that nearly all of these electronics projects can be developed in less than a day if you have the required tools and components. It won't be difficult to test out these creative ideas for electronics projects because, fortunately, you can search hot electronics parts from kynix for a low price.
I suggest you read this article all the way through, as I’ll be covering in detail electronics DIY projects to improve work from home.
Let’s get started.
The long, warm months ahead can only mean one thing for DIY enthusiasts: polishing up the skills over the project after project. For some people, that can include finishing a picture or creating original dishes in the kitchen. And for tech nerds, it is learning new software or building electronic projects at home.
Looking for easy ways to spruce up your technical skills? These simple DIY Electronic Projects would help you to get your hands dirty in the electronic field without spending a lot of time and money.
This is a simple electronic project used to charge your lead acid battery. It comes with LM 317 which is the main component of the circuit that serves as an operational amplifier mainly employed to deliver the exact charging voltage to the battery.
Operational Amplifier LM 317
Transistor BC 548
Transistors
Capacitors
Potentiometer
LM317 provides the correct voltage for the circuit and the transistor BC548 is employed to control the charging current delivered to the battery.
It is worth noting that one-tenth of the Ah value of the battery must be charged - the basic idea behind charging this circuit. The charging current can be adjusted using the potentiometer R5. While Q1, R1, R4, and R5 regulate the battery's charging current. The current flowing through R1 rises as the battery charges and it changes how Q1 conducts. The voltage at the LM 317's output rises because Q1's collector is connected to the IC LM 317's adjustment pin.
The charging current is reduced by the charger circuit after the battery is fully charged, and this mode is known as trickle charging.
Signal transmission is crucial when you want people to hear someone from a distance. Especially in factory and college settings to allow people to hear programs and speeches within range. This is a low-cost, simple electronic project used to create an FM transmitter circuit that has a 2-kilometer range for signal transmission.
Matching Antenna
Transistors BC109, BC177, 2N2219
Capacitors
Resistors
Battery 9 to 24 V
This is a simple DIY electronic project that you can easily develop at home. It comes with a 2 km range for transmitting the signals.
In this setup, a 9 to 24 DC power supply battery is used to power the circuit which not only ensures the optimum performance but also helps in reducing the noise.
The traditional high-sensitive preamplifier stage is formed with the transistors Q1 and Q2. Know that the audio signal required to be transmitted is connected to the base of Q1 using capacitor C2.
The oscillator, mixer, and final power amplifier functions are all carried out by transistor Q3. And the biasing resistors for the Q1 and Q2 preamplifier stage are R1, R3, R4, R6, R5, and R9. The tank circuit, which is formed by C9 and L1, is crucial for producing oscillations.
The FM signal is coupled to the antenna by inductor L2. Recognize that, the circuit frequency can be adjusted by varying C9 and R9 is employed to adjust the gain.
Make sure you apply this circuit on good-quality PCBs, as poor-quality connections can hurt the overall performance of the circuit.
Tired of your speaker’s low noise? Don’t panic! Since this is another easy-to-design electronic circuit that provides 150W to the four Ohm speakers – enough to provide you with a lasting, ruthless buzz to rock and roll. The basic component of the project is the pair of Darlington transistors TIP 142 and 147.
Darlington transistors TIP 142 and 147
Transistor BC558
Resistors
Diode 1N4007
Electrolytic Capacitors rated at least 50V
This circuit is effective for those just starting in the electronic field. In this circuit, TIP 147 and 142 are complementary Darlington pair transistors known for their durability that can handle 5 A current and 100V.
Know that Q5 and Q4 of two BC 558 transistors are joined together as a pre-amplifier also called a differential amplifier. This is used for two main reasons: for providing negative feedback and for reducing the noise at the input stage, thus improving the overall productivity of the amplifier.
While TIP41 (Q1, Q2, Q3) and TIP 142, TIP 147 together are employed to drive the speaker. This circuit's construction is so robust that it can be put together by soldering directly to the pins. A dual power supply with a +/-45V, 5A output can power the circuit.
A siren is a device that produces a usual louder sound to alert and/or attract people or vehicles. Typically, ambulances, police cars, fire trucks, and VIP cars are among the vehicles that use the siren.
The basic component of the project is the 555 timer which is one of the most adaptable chips that can be applied in practically all applications because of its multi-functionality. It is an 8-pin chip with a 200 mA direct current drive output that comes in a DIP or SOP packaging. This IC is a mixed-signal semiconductor since it has both analog and digital components. The IC's primary uses include producing time, clock waveforms, square wave oscillators, and numerous more functions.
Using two 555 timers, speakers, and a basic circuit, this breadboard project creates a police siren sound. As indicated in the diagram above, an 8 Ohm speaker is connected to two 555 timers.
Note that, one 555 timer is attached in an astable mode (it carries no stable state, instead comes with two quasi-stable states which quickly change from one state to another and then back to the original state) and the second 555 timer is connected in the monostable mode (one of the two states is stable, and the other is nearly stable.
When a trigger input is applied, it changes from a stable state to a quasi-state and then returns to the stable state on its own after a certain amount of time) to achieve the appropriate frequency.
This setting creates a siren with a frequency of about 1 kHz. Using the knob in the circuit, the siren sound frequency can be changed to match the police siren sound. The siren is used not just in automobiles but also in many businesses, mills, and other establishments to notify workers of their shift times.
With the help of a few basic components, a cooling system to regulate a DC fan is designed in this simple breadboard project. The goal of this project is to build a cooling system by easily operating a DC fan without the need for microcontrollers or Arduino, but rather by using readily available and straightforward electronic components. Once the temperature hits a certain level, this fan will turn on.
5V DC Fan
5V battery
NTC thermistor-1 kilo-ohm
LM555 Timer
NTC thermistor-1 kilo-ohm
BC337 NPN Transistor
diode 1N4007
capacitors 0.1 uF & 200 uF
LEDs
Connecting Wires
Resistors like 10k ohm, 4.7k ohm, 5k ohm
Breadboard
In this circuit, the DC fan can be controlled using a thermistor. The resistance of the thermistor, a particular type of resistor, is largely dependent on temperature. Thermistors come in two main types including NTC (Negative Temperature Coefficient) and PTC (Positive Temperature Coefficient).
When an NTC is employed, the resistance decreases as the temperature rises. This is the opposite in the case of PTC where resistance and temperature are directly proportional to each other.
When the temperature reaches a certain threshold, the fan turns on. The first LED, "The green LED," which indicates rising temperature, will turn ON as the temperature rises.
The second LED will turn ON when the temperature reaches the second threshold, and the fan will run as long as the temperature is over the second threshold. The fan will continue to run for a set amount of time once the temperature returns to an acceptable level.
This simple LED chaser circuit is developed using a 555 timer and 4017 IC. Together, the two ICs in this project run the LEDs in a sequence to create the illusion that they are chasing each other.
555 timers
CD 4017 IC
Resistors 470R, 1K & 47K
1uF capacitor
Connecting Wires
5 to 15 V power supply
Breadboard
Before you start working on the project, you must visit the pin diagrams of both ICs. It will help you to identify the correct pins to be used in the project.
When an IC – a 555 timer – is used in an astable mode (that produces a square wave), its output fluctuates continually between high and low supply voltages. For instance, an LED will continuously blink if it is connected between 555 timers and the ground.
The CLK input of a decade counter is connected to the output of a 555-timer IC. This IC has ten output pins, each of which is wired to an LED. The remaining output pins will all be switched OFF once the first pin is turned ON.
This simple, low-cost traffic lights model circuit is designed using two 555 timers and some other basic components.
This circuit comprises three LEDs for the indication of RED, Yellow, and Green traffic light signals. First, it will turn ON a green LED, maintains it on for a while, then briefly turns ON a yellow LED before turning on a red LED that remains ON for almost the same amount of time as the green LED.
555 Timers
Resistors of 100K, 47K, 2 x 330R, 180R
LEDs – Yellow, Red, and Green
Connecting Wires
Power Supply 5-12 V
2 Capacitors of 100uF
Breadboard
The circuit comes with two astable circuits where the first astable circuit will power the other. Therefore, only if the first 555 timer IC's output is ON will the second 555 timer IC be powered.
When the output of the first timer remains at 0V, it will turn ON the red LED. The green LED turns ON anytime the output of the second 555 timer IC is at a positive voltage, and the yellow LED will turn ON when the second 555 IC is in discharge mode.
The yellow and green LEDs would turn on at the same time. However, even before the voltage across the capacitor of the first 555 timer IC reaches two-thirds of the supply voltage, the output of the first 555 IC goes off, which will allow the red LED to turn ON and the yellow LED to turn OFF.
Hope you’ve got a brief idea of how to get started with electronic projects.
Getting hands-on experience will not only improve your technical skills but also help you to develop critical thinking to get familiar with advanced electronics.
It’s okay to become acquainted with PCBs, but if you don’t know how to solder properly or how to design a good PCB layout, it’s preferred to start working on the breadboard to keep your project up and running.
That’s all for today. Hope you’ve enjoyed reading this article. If you’re unsure or have any questions, you are welcome to reach out in the section below. I’d love to help you the best way I can. Thank you for reading this post.
Hi Guys! Hope you’re well today. I welcome you on board. In this post, I’ll walk you through How a Hobbyist Can Work on Electronic Projects in America.
Working on electronic projects is a bit inundating.
From selecting the topic to research work and development to execution, you need to hustle, grind and drill to keep your final product up and running.
When you are new to the electronic field, you must not be afraid to get your hands dirty in diving deep into the nitty-gritty of the project. This means that no matter what kind of technical project you pick, you need to spend a significant amount of your time and money to reach your final goal. It's not just about making sure that whatever it is you're looking for is done well—it's also about making sure that your project is done right from the start.
I suggest you read this post all the way through as I’ll be covering everything you need to know to make electronic projects as a hobbyist.
Development of electronic projects is tricky especially when you lack direction or you’re overwhelmed by the options available online. You can pick from a range of projects but the main goal is execution. If you fail to produce something that you proposed initially, it’s not worth it. NOPE. It’s not a good idea to pick the most difficult project to impress your instructors. Choose what resonates well with your expertise and helps you grow and excel in your field.
Newcomers have so many questions when they are about to get hands-on experience on the project. They don’t know how to start without lacking enthusiasm throughout the entire process. Don’t panic! We’ve streamlined a few steps in this post that will help you to complete your electronic project from start to finish with a proper strategy in place.
Whether you’re working alone or in a group, it all starts with brainstorming a few ideas. If you’re working in a group, make sure you work on concepts with shared interests and common grounds. Having a fruitful conversation before picking up the topic will help you figure out everyone’s weaknesses and strengths. What you lack in one area may well be covered up by someone good in that field. And if you’re aiming to develop something amazing for your final year project, this is a great opportunity to leave some sort of legacy for your juniors.
The following are the key considerations while brainstorming ideas for your electronic project.
Must be doable. You must have abilities to turn your thoughts into reality.
Start with something new. With the recent advent of technologies, there is a scope for covering something that has not been discussed before. Try incorporating microcontrollers and Arduino boards into your projects with new peripherals.
The best idea could be where you address the problem and provide a solution.
Cover both hardware and software. This is important. Covering both aspects of the project will not only polish your skills but also leave room for improvement for your juniors to work on.
Within price range. Yep, it should be well within your budget. Though you can ask for sponsorships if you want to produce something from a commercial aspect, still it’s wise to pick something that you can easily afford.
Should be completed in due time. That’s true. Deadline is important. Of course, you wouldn’t want to spend your money, time, blood and sweat that you can’t submit within time.
Once you’ve finalized the project idea, it’s time to play… Yeahhhhhh! Yep, it’s time to plan your project.
Say, if you have six months to complete your project, then divide the whole duration into dedicating each aspect of the project to a specific time limit. For instance, spare two months for research purposes, the next two months for purchasing the components and development of the project, and the final two months for the testing and execution of the project.
It is observed most engineering don’t plan their project according to the time limit and in the last month, they will be scratching their heads and doing everything to run the project. Even I did this mistake in our final year project. And we had to ask for extra days from our instructor to complete our project. So, I suggest you… please don’t do this mistake and plan your project accordingly.
Until now, you’ve selected the topic and planned the project. Now comes the research part. This is the backbone of the entire project. Start your research with what’s required to be included in both hardware and software.
Your time and energy are wasted when you rely on an inaccurate source of information. To research a subject with confidence and to cite websites as support for your writing, you should streamline your research to gain a clear understanding of the subject.
Make sure the hardware components you select are available in the market. And even if you have to buy them from outside the country, you spare enough time to incorporate them into your project.
Thoroughly go through the datasheets and pin diagrams of the components and look for possible substitutes. Why use an expensive part when a cheap substitute would suffice?
Apart from finding the components from the local market, there are scores of places online where you can get the right products. Some are better than others. But how do you differentiate them when all claim to be the best? Don’t fret! You can use Utmel Electronic Online Store which gives you quality electronic parts and components at reasonable prices to support your electronic project. From batteries, audio products, and connectors to capacitors, transistors, and evaluation boards, this place is a haven for tech nerds.
Hardware development is not a linear process. Sometimes you’ll witness going two steps backward for every step ahead. Don’t fear when this occurs since it’s what is required to turn your imagination into reality. Making hardware includes both: creation of mechanical structures and electrical circuit development.
The first step in developing the mechanical structure is making the 3D model on the computer. You can use “SolidWorks” to create the overall exterior of the project. Once you design the 3D model, turn it into a physical prototype. You can only create the model in the software but most probably, you will require someone in the market adept in understating the complexities of injection molding. Since this process is a bit tricky with many rules and regulations to follow.
If you’re a beginner and are not familiar with the nuts and bolts of developing PCBs, it’s wise to first create your hardware on the breadboard. This will help you identify all the possible mistakes before installing all these components on the printed circuit boards. Moreover, breadboards are user-friendly and you don’t require advanced technical skills to run your project.
PCBs are the cornerstone of many electronic and electrical units that provide a pathway to reduce their technological size. A PCB is often made of laminated materials like epoxy, fiberglass, or a variety of other materials that provide an essential framework for organizing electronic circuits.
You need to design your PCB to create PCB layouts. Print out your PCB layout on the glossy paper and transfer that print onto the required size copper plate.
Make sure you place the main IC into the center of the board to allow even connections with all the electronic components.
You have developed the required hardware for your project. Now is the time to use your programming skills to run your hardware.
If you’re using a microcontroller or Arduino, you might need to learn C++ since Arduino code is written in C++ with the inclusion of special functions. The code you build on the software is called a ‘sketch’ that is compiled and processed to machine language to run your hardware by the instructions given by the human input.
Similarly, if you aim to create development boards, MATLAB software is used which is quite handy for Data analytics.
A PCB and electronic design automation software suite for printed circuit boards is Altium Designer.
All circuit design activities can be completed using the tools in Altium Designer, including schematic and HDL design capture, circuit simulation, signal integrity analysis, PCB design, and the design and development of embedded systems based on FPGAs. The Altium Designer environment can also be modified to suit the needs of a wide range of users.
I don’t highly recommend this trick but if you find yourself stuck in some part of the hardware or software development, you can outsource that part to freelancers. But this is highly risky since if your instructor finds out that you were not the one who did that part, you may get into hot waters. Make sure you get your instructors on board before outsourcing the most complex part of the project.
You might have done everything right from the start, but it’s unlikely that your project starts in one go. You might need to run your project through a series of test and trial methods to identify errors and glitches in both hardware and software.
Always create a backup plan. Make your hardware in such a way that if you require some modification in the process, you can do so.
For instance, you can make a plastic casing for your mechanical structure before going for the hard metal enclosure. Ensure that the end product resonates with what you initially proposed in your proposal.
Once your project is completed and carefully tested, next comes the writing process.
Anyone can write but good writing needs practice. Make sure you dedicate this part to someone good at jotting down ideas in a clear and meaningful way.
Since the audience will get to see the end product. They don’t care how many struggles you withstood and how many sleepless nights you went through, they care about how your project can be beneficial for them and how it can solve their problems.
Additionally, it’s all about presentation. If you don’t know how to skillfully present your project, you fail to convince the audience that your project is worth spending time and money on.
It will be helpful to use data visualization in your presentation to present your project clearly and concisely. Throughout your presentation, be ready to respond to the panel's queries with care and attention.
And finally, don’t forget to make a video of your running project. Sometimes, even though the project runs smoothly, it doesn’t execute well in front of the instructors. So it’s wise to be on the safe side and record the video of your project.
You can make a home automation IoT project to remotely control the appliances of your home.
You can build an automatic security system that informs you whenever someone trespasses your home boundaries.
Develop an advanced light system that can be used to turn on the light loads whenever they detect human presence within range.
You may also create a robotic metal detector system that can find metals in the ground, and inside food products with the help of radiofrequency technology.
Build automatic solar tracker. To make sure your panel receives the most radiation possible throughout the day, you can construct trackers that follow the sun's path from sunrise to sunset.
Make Wireless Lock System Through OTP that provides a smart security solution.
Still reading? Perfect.
It means you’ve learned some valuable insights into how to develop your electronic project from start to execution.
Just follow these steps and you’ll be well ahead in turning your idea from ideation to execution.
Start with a simple doable idea. Some ideas may look best initially, but when you start working on them they become unrealistic.
Don’t forget to ask for help if you get stuck in the process. Since when you never ask for it, the answer is always NO.
That’s all for today. Hope you’ve enjoyed reading about how a hobbyist can work on electronic projects in America. If you are unsure or have any questions, you can ask me in the section below. I’d love to help you the best way I can. Thank you for reading the article.
Hi pals! Welcome to the next deep learning tutorial, where we are at the exciting stage of TensorFlow. In the last tutorial, we just installed the TensorFlow library with the help of Anaconda, and we saw all the procedures step by step. We saw all the prerequisites and understood how you can follow the best procedure to download and install TensorFlow successfully without any trouble. If you have done all the steps, then you might be interested in knowing the basics of TensorFlow. No matter if you are a beginner or have knowledge about TensorFlow, this lecture will be equally beneficial for all of you because there is some important and interesting information that not all people know. So, have a look at the topics that will be discussed with you in just a bit.
What is a tensor?
What are some important types of tensors that we use all the time while using TensorFlow?
How can we start programming in TensorFlow?
What are the different operations on the TensorFlow?
How can you print the multi-dimensional array int he TensorFlow?
Moreover, you will see some important notes related to the practice that we are going to perform so, you can say, no point will be missing and we will recall our previous concepts all the time when we need them so that the beginners may also get the clear concepts that what is going on in the course.
There are different meanings of tensors in different fields of study, but we are ignoring others and focusing on the field with which we are most concerned: the mathematical definition of the tensor. This term is used most of the time when dealing with the data structure. We believe you have a background in programming languages and know the basics, such as what a data structure is, so I will just discuss the basic definition of tensors.
"The term "tensor" is the generalization of the metrics of nth dimensions and is the mathematical representation of a scaler, vector, dyad, triad, and other dimensions."
Keep in mind, in tensor, all values are identical in the data type with a known shape. Moreover, this shape can also be unknown. There are different ways to introduce the new tensor in TensorFlow while you are programming in it. If it is not clear at the moment, leave that because you will see the practical implementation in the next section. By the same token, you will see more of the additional concepts in just a bit, but before this, let me remind you of something:
Types of Data Structure |
No. of Rank |
Description |
No. of Components |
Vector |
1 |
There is only the magnitude but no direction. |
1 |
Scaler |
2 |
It has both magnitude and direction both. |
3 |
Dyad |
3 |
It has both magnitude and direction both. If x, y, and z are the components of the directions, then the overall direction is expressed by applying the sum operation of all these components. |
9 |
Triad |
4 |
It has the magnitude and also have the direction that is obtained by multiplying the 3 x 3 x 3. |
27 |
Before going deep into the practical work, I want to clarify some important points in the learning of TensorFlow. Moreover, in this tutorial, we will try to divide the lecture into the practical implementation and the theoretical part of the tutorial. We will see some terms often in this tutorial, and I have not discussed them before. So, have a look at the following descriptions.
The length of the tensor is called its shape, and we can understand the term "shape" in a way that it is defined with the help of the total number of rows and columns. While declaring the tensor, we have to provide its shape.
The rank defines the dimensions of the tensor. So, in other words, rank defines the order of the dimensions that starts at 1 and ends on the nth dimension.
When talking about the tensor, the “type” means the data type of that particular tensor. Just as we consider the data type in other programming languages, when talking about the language of TensorFlow, the type provides the same information about the tensor.
Moreover, in order to learn more about the type of the tensors, we examine them with respect to different operations. In this way, we found the following types of tensors:
tf.Variable
tf.constant
tf.placeholder
tf.SparseTensor
Before we get into the practical application of the information we discussed above, we'll go over the fundamentals of programming with TensorFlow. These are not the only concepts, but how you apply them in TensorFlow may be unfamiliar to you. So, have a look at the information given below:
When you want your compiler to ignore some lines that you have written as notes, you use the “sign of hash” before those lines. In other words, the compiler ignores every line that you start with this sign. In other programming languages, we use different types of signs for the same purpose, such as, // is used in C++ to start the comment line when we are using compilers such as Visual Studio and Dev C++.
When we want to print the results or the input to show on the screen, we use the following command:
print(message)
Where the message may be the input, output, or any other value that we want to print. Yet, we have to follow the proper pattern for this.
To apply the operations that we have discussed above, we have to first launch TensorFlow. We covered this in our previous session. Yet, every time, you have to follow some specific steps. Have a look at the details of these steps:
Fire up your Anaconda navigator from the search bar.
Go to the Home tab, where you can find the “Jupyter notebook."
Click on the launch button.
Select “Python” from the drop-down menu on the right side of the screen.
A new localhost will appear on your Google browser, where you have to write the following command:
import tensorflow as tf
from tensorflow import keras
Make sure you are using the same pattern and take care of the case of the alphabets in each word.
Here, you can see, we have provided that information about the type of tensor we want, and as an output, it has provided us with the information about the output. There is no need to provide you with the meaning of the int16 here as we all know that there are different types of integers and we have used the one that occupies the int16 space in the memory. You can change the data type for the practice. It is obvious that you are just feeding the input value, and the compiler is showing the output of the same kind as you were expecting. Here, the shape was empty because we have not input its value. So, we have understood that there is no need to provide the shape all the time. But, in the next programs, we will surely use the shape to tell you the importance of this type.
Before the practical implementation, you have seen the information about the dimensions of the Tensors. Now, we are moving forward with these types, and here is the practical way to produce these tensors in TensorFlow.
Here, you can print the two-dimensional array with the help of some additional commands. I'll go over everything in detail one by one. In the case discussed before, we have provided information about the tensor without any shape value. Now, have a look at the case given next;
Copy the following code and insert it into your TensorFlow compiler:
a=tf.constant([[3,5,7],[3,9,1]])
print(a)
Here comes the interesting point. In this case, we are just declaring the array with two dimensions, and TensorFlow will provide you with the shape (information of the dimension) and the memory this tensor name “a" is occupying by itself. As a result, you now know that this array has two rows and three columns.
By the same token, you can use any number of dimensions, and the information will be provided to you without any issues. Let us add the other dimensions.
Let’s have another example to initialize the matrix in TensorFlow, and you will see the shortcut form of the declaration of a unit matrix of your own choice. We know that a unit matrix has all the elements equal to one. So, you can have it by writing the following code:
a=tf.ones((3,3)
print(a)
The result should look like this:
Other examples of matrices that can be generated in such a way are the identity matrix and zero matrices. The identity matrix and the zero matrices are two other matrices that can be generated in this manner. For a zero and identity matrix, we will use “zero” and “eye” respectively in place of “ones” in the code given above.
The next step is to practice creating a matrix containing random numbers between the ranges that are specified by us. For this, we are using the random operation, and the code for this is given in the next line:
a=tf.random.uniform((1,5),minval=1, maxval=3)
print(a)
When we observe these lines, we will understand that first of all, we are using the random number where the numbers of
Rows and columns are given by us. I have given the single-row and five-column matrix in which I am providing the compiler with the maximum and minimum values. Now, the compiler will generate random values between these numbers and display the matrix in the order you specify.
So, from the programs above, we have learned some important points:
Just like in the formation of matrices in MATLAB, you have to put the values of rows in square brackets.
Each element is separated from the others with the help of a comma.
Each row is separated by applying the comma between the rows and enclosing the rows in the brackets.
All the rows are enclosed in the additional square bracket.
You have to use the commas properly, or even if you have a single additional space, you can get an error from the compiler while running it.
It is convenient to give the name of the matrix you are declaring so that you can feed the name into the print operation.
If you do not name the matrix, you can also use the whole matrix in the print operation, but it is confusing and can cause errors.
For the special kinds of matrices that we have learned in our early concepts of matrices, we do not have to use the square brackets, but instead, we will use the parentheses along with the number of elements so that compiler may have the information about the numbers of rows and columns, and by reading the name of the specific type of the matrix, it will automatically generate the special matrices such as the unit matrix, the null matrix, etc.
The special kind of matrices can also be performed in the TensorFlow but you have to follow the syntax and have to get the clear concept for the performance.
So, in this tutorial, we have started using the TensorFlow that we had installed in the previous lecture. Some of the steps to launch TensorFlow are the same and you will practice them every day in this course. We have seen how can we apply the basic functions in the TensorFlow related to the matrices. We have seen the types of tensors that are, more or less, similar to the matrices. You will see the advanced information about the same concepts in the tutorials given next as we are moving from the basics to the advance so stay with us for more tutorials.
Hello Peeps! Welcome to the next lecture on deep learning, where we are discussing TensorFlow in detail. You have seen why we have chosen TensorFlow for this course, and we have read a lot about the working mechanism, programming languages, and advantages of using TensorFlow instead of other libraries. Instead of using the other options for the same purpose, we have seen several reasons to use TensorFlow. Because of the latest work on the library for more improvement and better results, it's now time to learn the specifics of TensorFlow installation. But before this, you have to check the list of the concepts that will be cleared today:
The simple and to-the-point answer to this question is, the installation is easy and usually does not require any practice. If you are new to the technical world or have not experienced the installation of any software, do not worry because we will not skip any steps. Moreover, we have chosen the perfect way to install TensorFlow by telling you the all necessary information about the installation process so you start only if all the parameters are completed. So, first of all, let us share the prerequisites with you.
What are the minimum requirements for Tensorflow to be installed on your PC?
How can we choose the best method for the installation of TensorFlow?
How can we install TensorFlow with Jupyter?
What is the process for the Keras to be installed?
How can you launch the TensorFlow and Keras with the help of Jupyter Notebook?
What is the significance to use Jupyter, Keras, and TensorFlow?
To install TensorFlow without difficulty, you must keep all types of requirements in mind. We have categorised each type of requirement and you just have to check whether you are ready to download it or not.
System Requirements |
||
Ubuntu |
16.04 or higher |
64 bits |
macOS |
10.12.6 (Sierra) or higher, GPU is not supported. |
N/A |
Window Native |
Window 7 (higher is recommended) |
64 bits |
Window WSL |
Window 10 |
64 bits |
By the same token, there are some hardware requirements and below these values, the hardware does not support the TensorFlow.
Hardware Requirement |
|
GPU |
NVIDIA® GPU card with CUDA® architectures 3.5, 5.0, 6.0, 7.0, 7.5, 8.0 |
Here, it is important to notice that the requirements given in all the tables are the minimum requirement s and you can go for the higher versions of all of these for the better results and quality work.
Software Requirement |
|
Python version |
3.7-3.10 |
Pip version |
19.0 (for window and Linux), 20.3 (for macOS) |
NVIDIA® for GPU support |
|
NVIDIA® GPU driver |
version 450.80.02 |
CUDA® Toolkit |
11.2 |
cuDNN SDK |
8.1.0 |
Moreover, to enhance the latency and performance, TesnorFlowRT is recommended. All the requirements given above are authentic and you must never skip any of these if you want to get 100 efficiencies. Moreover, for the course we are working on, there is no need for any GPU as we are moving towards the basic course and we can install GPU in future if required.
Now it's time to make a decision about the type of installation you want. This is the step that makes TensorFlow different from the other simple installations. If you're going to install it on your PC, you have to get help from another software. There is certain software through which you can install the library software with the help of Anaconda software. For this, we are going to the official website and installing the anaconda software.
As soon as you will click on the download option, the loading will start and the software of Anaconda with the size 600+MBs will start downloading. It will take a few moments to be downloaded.
Once the process discussed above is completed, you have to click on the installation button and the window will be pop-up on your screen where the installation steps have to be completed by you.
The installation process is so simple that many of you have known them before but the purpose to tell you about each and every step is, some people do not know much about the installation or have the habit to match the steps with the tutorials so they may know that they are at the right path.
In the next step, you have to provide the path for the installation place of the anaconda. By default, as you expect, the C drive is set but I am going to change the directory. You can choose the path according to your choice.
Now, you will see that it is asking for the settings according to your choice. By default, the second option is ticked and I am installing the Anaconda as it is and click on the installation process.
Now, the installation process is starting and it will take some time.
While this step is taking a little time, you can read about the documentation of the TensorFlow.
Once the installation is complete, the next button will direct you towards this window:
In this way, Anaconda will be installed on your PC. You must know that it has multiple libraries, functions, and software within it and there is no need to check them all. For our practice, we just have to know about the Jupyter notebook. It will be cleared in just a bit How can we start and work with this notebook?
It seems that you have successfully installed Anaconda and are now moving towards the installation of your required library. It is a simple and interesting process that requires no technical skills. You just have to follow the simple steps given next:
Go to the start menu of your window.
Search for the “Anaconda command prompt."
Click on it, and a command prompt window will appear on your screen.
You just have to write the following command and the Anaconda will automatically install this amazing library for you.
As you can see, it mentions that the download of TensorFlow requires 266.3 MBs. Once this command is entered, the installation of the TensorFlow will carry out and you have to wait for some moments.
To confirm the installation process, I am providing you with some important commands. You just have to type “python” in the command prompt, and Anaconda will confirm the presence of the python information on your PC.
In the next step, to ensure that you have installed Tensorflow successfully, you can write the following command:
Import TensorFlow as tf
If nothing happens in the command prompt, it means your library was successfully installed; otherwise, it throws an error.
Hence, the TensorFlow library is successfully installed on our PCs. The same purpose is also completed with the help of Jupyter Navigator, and you will see it in detail.
Finally, for the perfect installation, we will search, follow the path Home>Search>Anaconda Navigator, and press enter. The following screen will appear here.
You have to choose the “Environment” button and click on the “Create” button to create a new environment. A small window will appear, and you have to name your environment. I am going to name it "TensorFlow.”
There is a possibility that it recommends the updated version if it is available. We recommend you have the latest version, but it is not necessary. As soon as you click on the "Create" button, in the lower right corner, you will see that your project is being loaded.
This step takes some time; in the meantime, you can check the other packages in the Anaconda software.
There is a need for Keras API as you have seen in our previous lectures. But as a reminder, we must tell you that Keras is a high-level application programming interface that is specially designed for deep learning and machine learning by Google, and with the help of this PAI, TensorFlow provides you with perfect performance and efficient work. So, here are the steps to install Keras on your PC.
Open the Jupyter navigator.
Click on the "create" button.
Write the name of your new environment, I am giving it the name "Keras,” as you were expecting.
The next step is to load the environment, as you have seen in the case of TensorFlow as well.
These steps are identical to the creation of an environment for TensorFlow. It is not necessary to discuss why we are doing this and what the alternatives are. For now, you have to understand the straightforward procedure and follow it for practice.
Keep in mind that, till now, you have just installed the library and API, but for the working of both of these, you have to run them, and we will earn this in just a bit.
The installation process does not end here. After the installation process, you have to check if everything is working properly or not. For this, go to the home page and then search for “Jupyter notebook." You must notice that there is a launch button at the bottom of this notebook’s section. If you found something else here, such as "Install,” then you have to first install the notebook by simply clicking on it, and then launch the notebook.
As soon as you launch the Jupyter notebook, you will be directed to your browser, where the notebook is launched on the local host of your computer. Here, it's time to write the commands to check the presence and working of the TensorFlow. You have to go to the upper right side of the screen and choose the Python3 (ipykernel) mode.
Now, as you can see, you are directed towards the screen where a code may be run. So you have to write the following command here:
Import tensorflow as tf
From tensorflow import keras
This may look the same as the previous way to install tensorflow, but it is a little bit different because now, at Jupyter, you can easily run your code and work more and more on it. It is more user-friendly and has the perfect working environment for the students and learners because of the efficient results all the time.
Keras is imported along with TensorFlow, and it is so easy to deal with deep learning with the help of this library and API.
If you do not remember these steps, do not worry because you will practice them again and again in this course, and after that, you will become an expert in TensorFlow. Another thing to mention is that you can easily launch Keras and TensorFlow together; you do not have to do them one after the other. But sometimes, it shows an error because of the difference in the Python version or other related issues. So it is a good practice to install them one after the other because, for both, the procedure is identical and is not long.
So, it was an informative and interesting lecture today. We have utilized the information from the previous lectures and tried to install and understand TensorFlow in detail. Not only this, but we also discussed the installation process of Keras, which has a helpful API, and understood the importance of using them together. Once you have started TensorFlow, you are now ready to use and work with it within Jupyter. Obviously, there are also other ways to do the same work as we have done here, but all of them are a little bit complex, and I found these procedures to be the best. If you have better options, let us know in the comment section or contact us directly through the website.
In the next session, we will work on TensorFlow and learn the basics of this amazing library. We will start from the basics and understand the workings and other procedures of this library. Till then, stay with us.
Hey learners! Welcome to the new tutorial on deep learning, where we are going deep into the learning of the best platform for deep learning, which is TensorFlow. Let me give you a reminder that we have studied the need for libraries of deep learning. There are several that work well when we want to work with amazing deep-learning procedures. In today’s lecture, you are going to know the exact reasons why we chose TensorFlow for our tutorial. Yet, first of all, it is better to present the list of topics that you will learn today:
Why do we use TensorFlow with deep learning?
What are some helpful features of this library?
How can you understand the mechanism of TensorFlow?
Show the light towards the architecture, and components of the TensorFlow.
In how many phases you can complete the work in the TensorFlow and what are the details of each step?
How the data is represented in the TensorFlow?
In this era of technology, where artificial intelligence has taken charge of many industries, there is a high demand for platforms that, with the help of their fantastic features, can make deep learning easier and more effective. We have seen many libraries for deep learning and tested them personally. As a result of our research, we found TensorFlow the best among them according to the requirements of this course.
There are many reasons behind this choice that we have already discussed in our previous sessions but here, for a reminder, here is a small summary of the features of TensorFlow
Flexibility
Easy to train
Ability to train the parallel neural network training
Modular nature
Best match with the Python programming language
As we have chosen Python for the training process, therefore, we are comfortable with the TensorFlow. It also works with traditional machine learning and has the specialty of solving complex numerical computations easily without the requirement of minor details. TensorFlow proved itself one of the best ways to learn deep learning, therefore, google open-sourced it for all types of users, especially for students and learners.
The features that we have discussed before we very generalized and you must know more about the specific features that are important to know before getting started with TensorFlow.
Before you start to take an interest in any software or library, you must have knowledge about the specific programming languages in which you operate that library. Not all programmers are experts in all coding languages; therefore, they go with the specific libraries of their matching APIs. TensorFlow can be operated in two programming languages via APIs:
C++
Python
Java (Integration)
R (Integration)
The reason we love TensorFlow is that the coding mechanism for deep learning is much more complicated. It is a difficult job to learn and then work with these coding mechanisms.
TensorFlow provides the APIs in comparatively simple and easy-to-understand programming languages. So, with the help of C++ or Python, you can do the following jobs in TensorFlow:
To configure the neuron
Work with the neuron
Prepare the neural network
As we have said multiple times, deep learning is a complex field with applications in several forms. The training process of the neural network with deep learning is not a piece of the cake. The training process of neural networks requires a lot of patience. The computations, multiplication of the matrices, complex calculations of mathematical functions, and much more require a lot of time to be consumed, even after experience and perfect preparations. At this point, you must know clearly about two types of processing units:
Central processing unit
Graphical processing unit
The central processing units are the normal computer units that we use in our daily lives. We've all heard of them. There are several types of CPUs, but we'll start with the most basic to highlight the differences between the other types of processing units. The GPUs, on the other hand, are better than the CPUs. Here is a comparison between these two:
CPU |
GPU |
Consume less memory |
Consume more memory |
They work at a slow speed |
They work at a higher speed |
The cores are more powerful.powerful |
They have contained relatively less powerful cores |
It has a specialty in serial instruction processing |
They are specialized to work in a parallel processing |
Lower latency |
Higher latency |
The good thing about TensorFlow is, it can work with both of them, and the main purpose behind mentioning the difference between CPU and GPU was to tell you about the perfect match for the type of neural network you are using. TensorFlow can work with both of these to make deep learning algorithms. The feature of working with the GPU makes it better for compilation than other libraries such as Torch and Keras.
It is interesting to note that Python has made the workings of TensorFlow easier and more efficient. This easy-to-learn programming language has made high-level abstraction easier. It makes the working relationship between the nodes and tensors more efficient.
The versatility of TensorFlow makes the work easy and effective. TensorFlow modules can be used in a variety of applications, including
Android apps
iOS
Cluster
Local machines
Hence, you can run the modules on different types of devices, and there is no need to design or develop the same application for different devices.
The history of deep learning is not unknown to us. We have seen the relationship between artificial intelligence and machine learning. Usually, the libraries are limited to specific fields, and for all of them, you have to install and learn different types of software. But TensorFlow makes your work easy, and in this way, you can run conventional neural networks and the fields of AI, ML, and deep learning on the same library if you want.
The architecture of the TensorFlow depends upon the working of the library. You can divide the whole architecture into the three main parts given next:
Data Processing
Model Building
Training of the data
The data processing involves structuring the data in a uniform manner to perform different operations on it. In this way, it becomes easy to group the data under one limiting value. The data is then fed into different levels of models to make the work clear and clean.
In the third part, you will see that the models created are now ready to be trained, and this training process is done in different phases depending on the complexity of the project.
While you are running your project on TensorFlow, you will be required to pass it through different phases. The details of each phase will be discussed in the coming lectures, but for now, you must have an overview of each phase to understand the information shared with you.
The development phase is done on the PC or other types of a computer when the models are trained in different ways. The neural networks vary in the number of layers, and in return, the development phase also depends upon the complexity of the model.
The run phase is also sometimes referred to as the inference phase. In this phase, you will test the training results or the models by running them on different machines. There are multiple options for a user to run the model for this purpose. One of them is the desktop, which may contain any operating system, whether it is Windows, macOS, or Linux. No matter which of the options you choose, it does not affect the running procedure.
Moreover, the ability of TensorFlow to be run on the CPU and GPU helps you test your model according to your resources. People usually prefer GPU because it produces better results in less time; however, if you don't have a GPU, you can do the same task with a CPU, which is obviously slower; however, people who are just getting started with deep learning training prefer CPU because it avoids complexities and is less expensive.
Finally, we are at the part where we can learn a lot about the TensorFlow components. In this part, you are going to learn some very basic but important definitions of the components that work magically in the TensorFlow library.
Have you ever considered the significance of this library's name? If not, then think again, because the process of performance is hidden in the name of this library. The tensor is defined as:
"A tensor in TensorFlow is the vector or the n-dimensional data matrix that is used to transfer data from one place to another during TensorFlow procedures."
The tensor may be formed as a result of the computation during these procedures. You must also know that these tensors contain identical datatypes, and the number of dimensions in these matrices is known as the shape.
During the process of training, the operations taking place in the network are called graphs. These operations are connected with each other, and individually, you can call them "ops nodes." The point to notice here is that the graphs do not show the value of the data fed into them; they just show the connections between the nodes. There are certain reasons why I found the graphs useful. Some of them are written next:
These can be run or tested on any type of device or operating system. You have the versatility to run them on the GPU, OS, or mobile devices according to your resources.
The graphs can be saved for future use if you do not want to use them at the current time or want to reuse them in the future for other projects or for the same project at any other time, just like a simple file or folder. This portable nature allows different people sharing the same project to use the same graph without having any issues.
TensorFlow works differently than other programming languages because the flow of data is in the form of nodes. In traditional programming languages, code is executed in the form of a sequence, but we have observed that in TensorFlow, the data is executed in the form of different sessions. When the graph is created, no code is executed; it is just saved in its place. The only way to execute the data is to create the session. You will see this in action in our coming lectures.
Each node in the TensorFlow graph, the mathematical operation such as addition, subtraction, multiplication, etc, is represented as the node. By the same token, the multidimensional arrays (or tensors) are shown by the nodes.
In the memory of TensorFlow, the graph of programming languages is known as a "computational graph."
With the help of CPUs and GPUs, large-scale neural networks are easy to create and use in TensorFlow.
By default, a graph is made when you start the Tensorflow object. When you move forward, you can create your own graphs that work according to your requirements. These external data sets are fed into the graph in the form of placeholders, variables, and constants. Once these graphs are made and you want to run your project, the CPUs and GPUs of TensorFlow make it easy to run and execute efficiently.
Hence, the discussion of TensorFlow is ended here. We have read a lot about TensorFlow today and we hope it is enough for you to understand the importance of TensorFlow and to know why we have chosen TensorFlow among the several options. In the beginning, we have read what is TensorFlow and what are some helpful features of TensorFlow. In addition to this, we have seen some important APIs and programming languages of this library. Moreover, the working mechanism and the architecture of TensorFlow were discussed here in addition to the phases and components. We hope you found this article useful, stay with us for more tutorials.
Hey buddies! Welcome to the next tutorial on deep learning, in which you are about to acquire knowledge related to Python. This is going to be very interesting because the connection between these two is easy and useful. In the last lecture, we had an eye on the latest and trendiest deep learning algorithms, and therefore, I think you are ready to take the next step towards the implementation of the information that I shared with you. To help you make up your mind about the topics of today, I have made a list for you that will surely be useful for you to understand what we are going to do today.
How do you introduce the Python programming language to a deep learning developer?
How is Python useful for deep learning training in different ways?
Do Python provide the useful frameworks for the depe learning?
What are some libraries of Python that are useful for the deep learning?
Why do programmers prefer Python over other options when working with deep learning?
What are some other options besides Python to be used with deep learning?
Over the years, the hot topic in the world of programming languages has been Python because of many reasons that you will learn soon. It is critical to understand that when selecting a coding language, you must always be confident in its efficiency and functionality. Python is the most popular because of its fantastic performance, and therefore, I have chosen it for this course. From 2017 to the present, calculations and estimations of popularity show that Python is in the top ten in the interests of both common users and professionals due to its ease of installation and unrivaled efficiency.
Now, recall that deep learning is a popular topic in the industry of science and technology, and people are working hard to achieve their goals with the help of deep learning because of its jaw-dropping results. When talking about complexity, you will find that deep learning is a difficult yet useful field, and therefore, to minimize the complexity, experts recommend python as the programming language. All the points discussed below are an extraction of my personal experience, and I chose the best points that every developer must know. The following is a list of the points that will be discussed next:
I am discussing this point at the start of the discussion because I think it is one of the most important points that make programming better and more effective. If the code is clean and easy to read, you will definitely be able to pay attention to the programming in a better way. Usually, the programming is done in the grouping phase, and for the testing and other phases of successful programming, it is important to understand the code written by the others. Hence, coding in Python is easy to read and understand, and by the same token, you will be able to share and practice more and more with this interesting coding language.
The syntax and rules of the Python programming language allow you to present your code without mentioning many details. People realize that it is very close to the human language, and therefore, there is no need to have a lot of practice or knowledge to start practising. These are the important points that prove the importance of the Python language for writing more useful code. As a result, you can conclude that for complex and time taking processes such as deep learning, Python is one of the ideal languages because you do not have to spend a lot of time coding but will be able to use this energy to understand the concepts of deep learning and its applications.
Python, like other modern programming languages, supports a variety of programming paradigms. It fully supports:
Object-oriented Programming
Structured programming
Furthermore, its language features support a wide range of concepts in functional and aspect-oriented programming. Another point that is important to notice is that Python also includes a dynamic type system and automatic memory management.
Python's programming paradigms and language features enable you to create large and complex software applications. Therefore, it is a great language to use with deep learning.
If you are a programmer, you will have the idea that for different programming languages, you have to download and install other platforms for proper working. It becomes hectic to learn, buy, and use other platforms for the working of a single language. But when talking about Python, the flexibility can be proven by looking at the following points:
It supports multiple operating systems.
It is an interpreted programming language. That means you can run the Python code on several platforms without the headache of recompilation for the other platforms.
The testing of the Python code is easier than in some other programming languages.
All these points are enough to understand the best combination of deep learning with the Python programming language because deep learning requires the training and testing process, and there may be a need to test the same code or the network on different platforms.
Want to know why Python is better than other programming languages? One of the major reasons is the fantastic and gigantic library of the Python language. It is a programming tip that programmers should always check the programming language's library if they want to know its efficiency and ability to work instantly. One thing to notice is that you will get a large number of modules, and it allows you to choose the modules of your choice. So, you can ignore the unnecessary modules. This feature is also present in other popular programming languages. Moreover, you can also add more code according to your needs. For the experts, it is a blessing because they can use their creativity by using the already-saved modules.
Deep Elarnigna only contains algorithms, and it requires a programming language that allows for simple and quick module creation. Python is therefore ideal for deep embedding in context.
In the past lectures, we have seen the frameworks of deep learning, and therefore, for the best compatibility, the programming language in which the deep learning is being processed must also have open-source frameworks; otherwise, this advantage of deep learning will not be useful. Most of the time, the tools and frameworks are not only open source but also easily accessible, which makes your work easier. I believe that having more coding options makes your work easier because coding is a time-consuming process that requires you to have as much ease as possible for better practice. Here is the list of some frameworks that are used with the Python programming language:
Django
Flask
Pyramid
Bottle
Cherrypy.
Another reason why experts recommend Python for deep learning is the Python frameworks related to graphical user interfaces. In the previous lectures, you have seen that deep learning has a major application in image and video processing, and therefore, it is a good match for deep learning with Python coding. The GUI frameworks of Python include:
PyQT
PyJs
PyGUI
Kivy
PyGTK
WxPython
Observe that the keyword "Py" with all these frameworks indicates the specification of the Python programming language with these frameworks. At this point, it is not important to understand all of them. But as an example, I want to tell you that Kivy is used for the front end of Android apps with the help of Python.
This category makes it important to notice the connection between the Python programming language and deep learning because, when working with deep learning, a greater variety of frameworks results in an easier working and better training process.
If you are following our previous tutorials, you will be aware of the importance of testing in deep learning. But allow me to tell you the connection between Python and the test-driven approach. In deep learning, all efficiency depends upon the testing process. More and more training and testing means better performance, which the network can recognize better. Python provides for the rapid creation of prototype applications, and similarly, it also provides the best test driven approach when connected to networks.
The first rule to learning programming languages is to have consistency in your nature. Yet, for the more difficult programming languages, where the absence of a single semicolon can be confusing for the compiler, consistency is difficult to attain. On the contrary, an easier and more readable programming language, such as Python, helps to pay more attention to the code, and thus the user is more drawn to its work. Deep learning can only be performed in such an environment. So, for peace of mind, always choose Python.
Have you ever been stuck in a problem while coding and could not find the help you needed? I've seen this many times, and it's a miserable situation because the code contains your hard work from hours or even days, but you still have to leave it. Yet, because of the popularity and saturation of this field, Python developers are not alone. Python is a comparatively easy language, and normally people do not face any major issues. Yet, for the help of the developers, there is a large community related to Python where you can find the solution of your problems, check the trends, have a chit chat with other developers, etc.
When working on deep learning projects, it's fun to be a part of a community with other people who are working on similar projects. It is the perfect way to learn from the seniors and grow in a productive environment. Moreover, while you are solving the problems of the juniors, you will cultivate creativity in your mind, and deep learning will become interesting for you.
At this point, where I am discussing a lot about Python, it must be clarified that it is not the only option for deep learning. Deep learning subjects are always wasteful, and users always have more than one option. However, we prefer Python for a variety of reasons, and now I'd like to tell you about some other options that appear useful but are, in fact, less useful than Python. The other programming languages are:
JavaScript
Swift
Ruby
R.
C
C++
Julia
PHP
No doubt, people are showing amazing results when they combine one or more of these programming languages with deep learning, but usually, I prefer to work more with Python. It totally depends on the type of project you have or other parameters such as the algorithm, frameworks, hardware the user has, etc. to effectively choose the best programming language for deep learning. An expert always has an eye on all the parameters and then chooses the perfect way to solve the deep learning problems, no matter what the difficulty level of the language is.
Hence, we have discussed a lot about the Python today. Before all this discussion, our focus was on the deep learning and its working so you amy have the idea what actually si going on. In this article, we have seen the compatibility of the Python programming language with deep learning. We knew about the parameters of the deep learning and therefore were able to understand the reason of choosing the Python for our work. Throughout this article, we have seen different reasons why we have chosen TensorFlow and related libraries for our work. It is important to notice that Python works best with the TensorFlow and Keras APIs, and therefore, from day one, we have focused on both of these. In the next lecture, you will see some more important information about deep learning, and we are moving towards the practical implementation of this information. Once we have performed the experiment, all the points will be crystal clear in your mind. So until then, learn with us and grow your knowledge.