We are a remote devforce working towards a singular goal to have meaningful software products built based on first principle to improve human lives or to help businesses to grow

TechUp Labs

techuplabs

Photo by Edgar Castrejon on Unsplash As the NFT sector expands, entrepreneurs have a choice to make. Especially if they plan to enter the NFT space. If they want to offer their customers an NFT marketplace, they need to approach the right developer companies. Confused about which company to choose? This blog will guide you through choosing the best & most reliable NFT marketplace development company for your specific needs. Below are the top 10 NFT marketplace development companies of 2023. Read on! INORU INORU is an outstanding NFT marketplace development company.  It specializes in offering a top-of-the-line NFT marketplace. They have the experience of working in different kinds of marketplaces which makes them versatile. For example, they have NFT marketplace options which cater to music, arts, and many similar other choices. The marketplaces are also available at a reasonable price. Undoubtedly, anyone could deploy the marketplace within a shorter period of time. Furthermore, this company offers a 360-degree range of services. It includes the following. NFT Development Services NFT Marketing Services NFT Marketplace Development NFT Music Marketplace Development NFT Collectible Marketing Services, and many others. Blockchain App Factory The Blockchain App Factory is a leading NFT marketplace development enterprise that seems to be a pioneer in this sector. They aim to provide innovative solutions and unique ideas needed to implement the NFT marketplace, apt for the clients. Appdupe Appdupe is a firm specializing in providing the NFT marketplace development solutions at an affordable price. They offer the solution within a fast turnaround without compromising its quality. They meet unique client needs and lead the way for global reach. They work in several niches like arts, music, and various other domains in the NFT world. They also have a solid reputation. TurnkeyTown TurnkeyTown is a reliable NFT development firm. They are customer-oriented and are keen on providing the NFT marketplace that suits client requirements. Just like Inoru, TurnkeyTown focuses on offering a wide range of services. Diversified NFT marketplace development services include the following. NFT for arts NFT for music NFT for games Photography Digital collectibles Other domains Infinite Block Tech Infinite Block Tech, is a firm that helps entrepreneurs and business owners enter the NFT space by deploying the NFT marketplace. The company offers user-friendly interfaces along with solid security measures for transactions. The team focuses on delivering the solution suitable for the individuals and also comes with suggestions to make your marketplace more profitable. Leewayhertz Leewayhertz is an NFT marketplace development company. They have pre-packaged solutions with the incorporation of superior features. This ready-to-launch solution will empower entrepreneurs and business people to deploy their NFT marketplace instantly. Antier Solution Antier Solution is a provider of NFT marketplace solutions. The transparency in the development process and customer-centric approach is what makes the company more trustworthy and preferable for business owners. TokyoTechie TokyoTechie is an Indian-based company or provider providing solutions for Blockchain and cryptocurrency. They are renowned for their professional service with the deliverance of quality solutions. Their pre-made solutions assist aspiring entrepreneurs in leaping into the sphere of the NFT world with the deployment of their own NFT marketplace. Askgalore Askgalore aims to give the perfect blend of services and distinctive products to make clients stand out from others in the competitive & ever-changing environment. Lightrains Lightrains Technolabs is one of the leading Indian-based companies, bringing solutions for businessess. Final words The rising hype of NFTs is paving the way for the emergence of NFT marketplaces, a medium in which the trading of NFTs takes place. The present situation is that the NFT sector is filled with several NFT marketplaces. On a surprising note, the NFT sector also brings a wide-open space for business opportunities since entrepreneurs could come up with their own NFT marketplace.

Top 10 NFT Marketplace Development Companies for 2023

Remote work has become a common practice across the globe. Even 5 years ago, the scenario was entirely different. Organizations were worried that this trend could tarnish their employees’ performance. However, as technology evolved at a rapid pace, so did everything else revolving around it. Now people at the workplace communicate more over Slack or Zoom than they do in person. It’s not a trend anymore. It’s a necessity. Bigger enterprises like Github and Automatic employ huge remote workforces. However, what really has grabbed everybody’s eyeballs is Startups like GitLab, Buffer, Toptal, and Zapier have gone remote. Bill Gates, the co-founder of Microsoft backed up the idea as well. He said it results in reduced utility bills, fewer absences, and employee satisfaction. Also, it gives employers access to a larger pool of talent. Let’s have a quick look at why Startups across the world are encouraging Remote Work. Access to the global pool of talent When you don’t have any geographical limits, you have the entire world to look for the perfect candidate. That’s the primary reason why Startups have chosen to go remote. Remember, you may not get the exact candidate if you restrict yourself to where your office is. Reduced cost Remote Work reduces your expenses on equipment and rent. As reported by Product Hunt, Cisco managed to save $227 back in 2009 by allowing employees to work remotely. At the present time, a startup could save about $10,000 per year by going remote. housing an employee would cost you around $20,000 per year. A chance to expand your business When you recruit global talent, they bring a fresh perspective to your business. Also, it helps you expand your business. Startups across the globe have realized this fact and decided to go remote. Employees from different countries or regions would get you new connections. Keep your employees satisfied Studies depict that employees working remotely are way happier. It results in higher employee retention rate and a flexible work environment. 86 percent workers confirmed that working remotely gave them a chance to be more productive. Employee productivity is a Startup’s foremost objective. It is a proven fact that satisfied and productive employees can help your business grow further. Lack of clear accountability More often than not, employers fear that remote workers may not be well aware of their accountability. Green Apex considers it their foremost action to sign an with employees. It ensures employees are well-versed with every aspect of their employment. NDA (Non-Disclosure Agreement) Communication problem When your employees are remote, being in constant touch with them is a roadblock. Use of a two-way communication channel to chat with employees is a must in this case. Also, it is advisable to go with agencies that offer you a spokesperson. Freelancers lack this facility. Work/life balance When you do work remotely it offers a better work/life balance. Working from the home can make you feel in control of your life and it saves the time of traveling to an office. So that you get more time to plan activities.  It gives a sense of achievement so that employees be more productive towards their work. When working remotely: As I said, there is no rush to catch a train or sit in traffic You don´t require to carry food, files or your laptop You can take a break when you require one There are no stressful office politics You get more time to spend with your family Positivity Sometimes we have a question that working remotely is a good idea for a team? But we believe it is better for them as it will increase communication and integration among them. Team members can be in touch through modern technology apps. So it is like they are in an office anyway. Working together like this will promote positivity and motivation. It will lead to a healthy environment for the company. Employees try to stretch working hours if you need them to work late or require them to complete any task. It will be beneficial for employer branding too. Green Apex Technolabs lets you hire its top-notch developers with expertise across various platforms and technologies including Android App Development, iPhone App Development, Cross-Platform App Development, Java, PHP, AngularJS, NodeJS, ReactJS, UI/UX designing, WordPress Development, QA/Manual/Automation Testing & many more. Visit our website or get in touch to know more. Previously published at www.green-apex.com.

Why Startups Give The-Go-Ahead For Remote Employees

In this article, you’ll learn what velocity means in the context of an agile/scrum methodology and how you can improve it to allow your team to move faster and ship more stuff. Speed is a crucial ingredient to all companies, especially startups. The defining characteristic of startups that end up making it against the odds, is their ability to move fast. So what does speed mean for engineering teams? Velocity. In this article, you’ll learn what velocity means in the context of an agile/scrum methodology and how you can actually improve it to allow your team to move faster and ultimately ship more stuff. What does velocity mean in agile? Velocity is a metric that tracks how much work your team was successfully able to complete in their sprint. This is normally done by adding up all the effort estimates to see how many of these were completed in that time period. As velocity is based on your effort estimates, it requires these to be accurate (it generally takes around 4-5 sprints to find your average). According to , Martin Fowler A typical approach is to average the velocity the past three time periods to determine velocity for future time periods. How do you improve velocity? 1. Maintain a laser focus If your team is faced with new tasks and distractions once the sprint has already begun, your velocity is guaranteed to suffer. That’s why it’s so important to clearly define the tasks and prioritize them before you begin your sprint. Be consistent when it comes to accepting or rejecting changes from the rest of the company, everyone should be aware of the deadline for submitting ideas and features to the engineering team. 2. Fix your technical debt is the silent velocity killer. Technical debt Developers are constantly under pressure to deliver features faster (hell, that’s probably why you’re reading this article). We all know that this pressure results in shortcuts and assumptions, which are probably necessary at the time. However, it’s easy to forget these concessions and if they’re not dealt with, they turn into technical debt that will weigh your team down. To optimize for velocity, you need to ensure that your codebase is well maintained. For smaller pieces of tech debt, engineers should follow the , for medium to large pieces of debt you could consider using a technical debt management tool. boy scout rule ‍ 3. Always do retrospectives Retrospectives (or retros) are a key part of the agile methodology. It allows the team to identify what went right or wrong in the sprint and how to improve for next time. As mentions, Maruti Techlabs The most important benefit is that it cuts through hierarchy and gives equal power to the team members to open up and present their views effectively. Ensure that these retros happen in a trusted environment, without blame, so you get maximum transparency. The meetings will require time and participation (developer kryptonite combo), but it’s a tried and tested practice that will lead to extremely valuable insights and tweaks that will ultimately speed up your velocity. 4. Boost team morale A happy team is a productive team. Make sure the team is rallied around a common goal and that they have clear ways of expressing feedback if they’re not happy (like the aforementioned retros). The work that is done should also feel satisfying, try to bridge the gap between the business side of the company and the engineering team, by, for example, showing how many users started to use the feature that was built in the last sprint ( is a great tool for visualizing this). Hotjar Don't forget to cheer for the maintenance and tech debt heroes: the people who refactored this nasty function, fixed that bug, reviewed these pull requests, and ultimately saved the team hours of work by keeping the codebase healthy. Key takeaway So there you have it, 4 ways to improve your team’s velocity. It’s important to remember that your velocity cannot be constantly increased (you don’t want to end up with inflated estimates). Instead, try to evaluate it periodically and make the changes that you feel will have the greatest impact on the team. Previously published at https://www.stepsize.com/blog/the-engineers-complete-guide-to-improving-velocity

4 Tips to Improve Your Engineering Team's Velocity in Scrum

PHP web frameworks are the most popular sources for web application development. Laravel web development is one of the most important technologies for web application because of the MVC architecture. As a result, Laravel has enabled the development of superior personalized web applications for organizations around the globe. Because of its simplicity, the coding experience leads to flawless syntax with the least extent of being incorrect. Why Businesses Use Laravel? The development market is filled with several open-source PHP development frameworks. You’ll find Symfony, CakePHP, and CodeIgniter. The last one mentioned is Laravel’s closest competitor in terms of market share. Top Key Features of Laravel Framework: Modular Development Process Laravel is a repository of over 20 unique libraries that are categorized into distinct modules. These libraries are what Laravel is. Thanks to this, Laravel makes it easier for developers to take a modular approach to developing complex and responsive web applications that can still be user-friendly. Also, tons of complexities in enterprise could be taken care of easily with this modular approach. Laravel application development Artisan CLI One of the standout features of Laravel is its Artisan Command Line Interface. With the help of Artisan, developers can perform some of the most complex tasks in minutes. Besides, it also helps them develop database structures and seamlessly migrate them. Artisan also allows you to develop your commands and further simplify the development process. Unit Testing Facilities Application development doesn’t stop with coding. Testing is an inevitable process and Laravel allows you to run several unit testing, eliminating the need to run standalone tests to assess the performance of your application Routing System Laravel’s routing system allows developers to choose the route an application would trigger. Consistent Updates Laravel is constantly updated. So, this is ideal to build future-proof apps that would stand the test of time. Laravel Packages With the 5.5 update, developers don’t have to set up aliases to install new Laravel packages, the framework does that autonomously. Swift Time-To-Market It paves the way for quick development process, which in turn offers you swift time-to-market Coding Simplified It further simplifies coding with its Blade Template Engine. Laravel Voted as Best PHP Web Framework Laravel is one of the leading PHP frameworks in the market. Its popularity resounds in the Developer community because of its simplicity and conciseness. Also, the Laravel community is improving the framework by creating crafty documentations that allows others to join in the resource pool. One of the most sought-after things in the programming world, retains its marketing positioning as the leader in the web application frameworks. Laravel framework Laravel has created a reputation for itself that causes the IT community to pay the highest attention to the framework. As a result, most companies prefer getting their on this platform. IT project development Wrapping-up: Laravel has a lot of advantages for startups and businesses. And these benefits have a direct proportion to an increase in the business, customers & revenue. After understanding the reasons that Laravel can boost your business, what’s stopping you? Feel free to visit and start on the road to success. Shiv Technolabs

How Laravel Web Development Can Grow Your Business in 2020 and 2021

WordPress is one of the most popular CMS platforms and it powers nearly one-third of the world’s websites. You can use WordPress Development Services from small personal blogs to the complex sites of major corporations such as Sony, and TIME Magazine, and so on One of the major plus points is that WordPress site builders and CMS users can download and install for free, but it has unique features. Let’s dive into the top benefits of using WordPress: Ease of Use You don’t have to be a technical person while working with WordPress. Because it is very easy to use & has an intuitive interface. It’s very easy to add new pages, blog posts, images, and so on. In short, WordPress is so simple, time spent on formatting is greatly reduced. Manage Your Website from Any Computer It’s very easy to access WordPress, you can login from any internet-connected computer & manage your site. No HTML Editing or FTP Software Required WordPress does not require HTML editing software. It offers you several interesting features such as you can create a new page, blog post, format text, upload images, video files so on. All this without the need for additional HTML or FTP software. Search Engines Love WordPress Sites WordPress offers you a very clean and simple code, making it easy for search engines to read & index a site’s content. You provide Meta tag, keywords, description, and title to each page, post, and image optimized for specific keywords. You can also explore the tags to further enhance your SEO. You Have Control of Your Site No more waiting for your web designer to make simple updates to your site. Working with WordPress is fun because you have control of every aspect of your site & can easily make those simple updates yourself. The Design of Your Website is 100% Customizable WordPress provides your site with a good look and feel with 100% customization so your brand can shine through on your site & provide a unique experience to your visitors. A Blog is Built-in and Ready to Go WordPress was created as a blogging platform that is very easy to integrate. Setting up RSS / email subscriptions to your blog, commenting capabilities, and automatically adding the most recent blog posts to other pages of the site are also very simple to set-up, and help to extend your company’s reach and make your site more dynamic and interactive. Extend the Functionality of Your Site with Plugins WordPress offers you great plugins so that you can add an event calendar, Twitter feed, Facebook fan box. The interesting point is most of the plugins are free or affordable. Your Site Can Grow as Your Business Grows WordPress sites are very scalable. As we have discussed above that you can have several blog posts on your site yet the performance of the site will not be compromised in the least. Now that’s interesting, isn’t it? Have Multiple Users Being an administrator of a WordPress site, you can easily set-up multiple users for the website and assign access levels to each user as per requirements. Conclusion The best way to truly experience the power of WordPress is by using it. Give it a try, you need an interactive website that attracts maximum people. About Us ensures that right from the conceptualization to deployment of your project. The points mentioned above will certainly help you Hire WordPress Developers which in turn will be beneficial for your business. Shiv Technolabs Pvt.ltd

Benefits of Using WordPress to Power Your Company’s Website

Top 30 Ecommerce Development Companies in 2020

Top Ecommerce Development Companies

Streamlined customer experience, increased efficiency, and cost reduction are some of the many benefits insurers gain by integrating artificial intelligence (AI) in their internal and client-facing processes. Defying its traditional image, insurance companies today embrace innovative solutions that foster convenience, transforming conventional insurance practices. Whether underwriting, claims processing, or accurately evaluating risks by analyzing large sums of data, AI solutions are paving the way to all verticles of the insurance industry. Insurance can hugely benefit from AI-based technologies such as machine learning (ML), deep learning, and related techs such as neural networks to refine vast amounts of internal and external data. Let’s delve deeper to explore the profound role of AI in insurance, learning its applications and current trends. Benefits of Using AI for Insurance Companies Integrating AI for insurance can be tedious, with underlying concerns about cybersecurity risks. Companies need to establish ground rules to use this technology responsibly while learning the reliability and accuracy when choosing it for high-impact use cases. Yet, as the world discovers what AI is capable of, it has become hard to avoid the ease and automation it provides. Some of the evident benefits of AI include: Enhanced Efficiency The insurance sector must deal with a significant volume of digital data daily. It can be a blessing to find this data in a structured format and a curse when it’s not. AI holds the potential to refine and structure data at a rapid pace, allowing employees to focus on more high-value tasks. Reduced Costs When a task is concluded quicker, the cost to perform each task decreases. It also increases employee satisfaction, as they need not waste their time and energy on mundane tasks. Streamlined Customer Experience Applying AI to your customer-facing processes gives them an easy-to-access and streamlined insurance process. A happy customer is one of the most remarkable feats any service provider can achieve. It instills trust in your clients and adds more to repeat business. How Are AI Solutions Reshaping the Insurance Industry? The insurance landscape is fiercely competitive. Therefore, adopting automation technologies has become imperative to gain a competitive advantage. AI offers immense value to insurers by automating repetitive tasks such as policy administration, data entry, document processing, and scaling operations. Let’s explore how integrating advanced technologies such as AI can expedite laborious and time-consuming insurance procedures. 1. Efficient Claims Handling Insurers must manually assess policies in detail to determine the comprehension a claimant would receive for a particular claim. Introducing can reduce this time and issues faced with manual verification. Insurance companies have leveraged AI, RPA, and IoT to increase operational efficiency. claims processing automation 2. Risk Assessment Applicants carried out the risk assessment process, filling out insurance forms manually. Here, the possibility of inaccurate risk assessments increases due to a claimant’s mistakes or dishonesty. The rise of connectivity and IoT devices allows insurers to gather larger datasets with accurate information. Additionally, risk assessments can be enhanced using Natural Language Processing (NLP), which helps insurance companies find their desired information in text sources. 3. Detecting and Preventing Fraud Around is collected via premiums in the insurance industry. With such high stakes, the risk of fraud also increases exponentially. The cost of non-health insurance fraud is over $ 40 billion every year. This results in a premium amount rising from $ 400 to $ 700 annually. $ 1 trillion is a boon for detecting and preventing fraud. Analyzing vast amounts of data can learn suspicious patterns and caution insurers against potential fraud in real-time. AI assists insurers by reducing monetary losses, upholding operational integrity, and managing risks. It empowers the business and policyholders by shielding them against fraudulent activities. Machine learning fraud detection Reporting FNOLs 4. Leveraging AI in insurance, first notice of loss can be reported, routed, triaged, and assigned with minimal human intervention. Chatbots can be used to support this process, offering convenience to customers to register their incidents anytime and anywhere. Expedite Everyday Operations 5. Introducing AI in everyday operations can enhance customer experience. Chatbots are highly efficient when initiating a process and further circulating the information to the following aligned process, making the process smoother, quicker, and error-free. AI-enabled chatbots can cross-sell and upsell products by learning the customer’s profile and history. Using AI to its fullest potential in scaling operations, human resources can be used for other crucial and strategic roles. Conducting Loss Estimation 6. Assessing damages using images has become exceedingly easy using the latest advancements in AI technology, such as deep learning, machine learning, and OCR. By only uploading a picture of the damaged object, the extent of the damage can be determined within seconds. In addition to that, these techs can be further trained to predict potential losses and offer valuable recommendations. Conclusion In today’s dynamic business landscape, leveraging AI is crucial for gaining a competitive advantage. Insurance companies that adopt advanced technologies early, making their processes flexible for customers and employees, achieve unprecedented success. Industries heavily reliant on data-driven decision-making, such as insurance, are increasingly inclined towards integrating AI solutions. Automation enhances the entire process, especially in the insurance sector, from application to claims settlement. Although AI in insurance is still emerging, it holds the potential for substantial transformation. The insurance sector has numerous AI use cases, and how quickly companies adopt this technology will determine their competitive edge.

How AI Is Transforming the Insurance Industry

এআই কীভাবে বীমা শিল্পকে রূপান্তরিত করছে

Wie KI die Versicherungsbranche verändert

Cómo la IA está transformando la industria de los seguros

Comment l’IA transforme le secteur de l’assurance

एआई बीमा उद्योग को कैसे बदल रहा है?

AI が保険業界をどのように変革するか

AI가 보험 산업을 변화시키는 방법

Como a IA está transformando a indústria de seguros

Как ИИ меняет страховую отрасль

Yapay Zeka Sigorta Sektörünü Nasıl Dönüştürüyor?

AI đang biến đổi ngành bảo hiểm như thế nào

人工智能如何改变保险业

You will undoubtedly be surprised by the overall influence of quantum computing on software development. However, in order to provide you with a deeper understanding of every facet of this technology, I'll deal with the fundamentals before outlining the ways that quantum computing affects software development. So, what precisely is this " " that is so widely discussed? quantum computing Quantum computing, to put it simply, is the idea of using a supercomputer with an abstract number system to tackle complicated issues. Although several engineers have developed their own quantum computers, the world is still waiting for the perfect model to appear. After all, what good are experimental results if they can't perform better than conventional computers? A careful examination of quantum computing's potential reveals that it has the power to revamp machine learning and artificial intelligence, two of the most hotly debated subjects among tech enthusiasts. This technology has the potential to fundamentally alter how we live and work, so the buzz is entirely justified. Quantum computing is the future for everything from data analysis to more productive software development outcomes. The long and error-prone process of debugging code will be eliminated with the help of processing power that is beyond comprehension. And believe me when I say that this is only the beginning! The entire process of software creation will be redesigned once the fabled quantum computer enters the realm of programming. Forget about waiting for mistakes to happen before fixing them. You can save money and the stress that comes with the software development process by reducing the number of manual tasks. What Areas of Software Development Would Quantum Computing Influence? Quantum computing has the ability to have an impact across many sectors by providing countless opportunities in various fields. Data analysis, the foundation of , is one of the key elements. software development Quantum computing can aid in the development of efficient data analysis procedures by overcoming the constraints of current data analysis systems, giving firms the tools they need to create accurate models and make wiser business decisions. Keep an eye out for the most potent data analysis tools to arrive if you want to improve your growth by understanding your consumers' needs. That was a summary of what will be possible with quantum computers. Refer to this incredibly informative content to learn more about quantum computing: Understanding the Power of Quantum Computing The Advantages of Applying Quantum Computing to Software Development Giant companies like Google and IBM are interested in constructing their own quantum computers, while experts turn to leverage technologies like superconductivity and ion traps to build quantum computers to fit a variety of requirements. Quantum computing has been embraced by an increasing number of businesses for a range of inventions and computations, proving its superiority to conventional computers. It has been saving humanity from the worst of disasters, from assisting you in making wiser financial decisions to changing transportation to speeding up the discovery of vaccinations in the midst of a pandemic. To discover more about the wave of changes that quantum computing could bring about in our lives, read the following article: How the Quantum Computer will Change our Lives Thanks to the advent of the quantum computer, errors no longer limit the practical application of hardware. Despite the fact that there are a number of competing technologies, developers' preferred quantum software is the one that makes it possible to create applications for quantum computing. Problems that generally need resources that increase exponentially with the size of the challenge can be fully eliminated by quantum computers. In these circumstances, it has become vital for scientists to come up with a hybrid type of execution that would incorporate classical code running on the CPU with quantum code. The Potential Impact of Quantum Computing on Software Development A lot of interest and funding has been generated by individuals who are interested in the topic of the ability of quantum physics to power computers. Given that this technology can defeat several encryption schemes, the rising interest of national agencies is suggestive of the potential for further development. Although it is anticipated that processing would be faster and more complex than with the available resources, none of this will be achievable without significant software advancements. By incorporating domain experts, quantum algorithm design software also makes it possible to implement more complex algorithms on more advanced machines, expanding the pool of labor that is readily available. One of the computing areas that software-programmable quantum computers are expected to have a positive influence on is scientific computing. Quantum Computing: The Future of Software Development Despite the fact that quantum computing is capable in and of itself, the fusion of other technologies can lead to outcomes that are unfathomable. If company tycoons want to stay up with the rising expectations of the era, they must have a good understanding of modern technology. The promise of quantum computers to revolutionize the computing industry can be achieved by using them to perform advanced research in hitherto untapped fields and solve issues that are currently intractable. This technology will soon permeate numerous other disciplines, including physics, chemistry, medicine, genetics, and geography. Despite all of its advantages, I must draw your attention to quantum computing's negative impacts on society. Future post-quantum encryption may be required as a result of improper use of the quantum computer capability, which could lead to the decryption of all current data encryption. The Other Side of the Coin Unquestionably, even for the most capable and esteemed software architects, building the highly coveted quantum computer is a challenge. This is primarily caused by how challenging it is to simulate elementary particles on a large scale. Once this obstacle has been cleared, experimenters' next task will be to use a sophisticated computer system to solve difficult issues. It is not necessary for me to underline the significance of a quantum computing system when it comes to decreasing costs and boosting speeds, but this necessitates major advances in quantum computing before it can be effective in various contexts outside of scientific study. An Overview of What you May Witness in the Field of Quantum Computing Using quantum algorithms to perform artificial intelligence and machine learning tasks Quantum calculation validation and verification A potential VHDL-like quantum computing strategy Quantum computing units (QPUs) are being studied to provide interactive proof systems for quantum physics. Qubits and gates are electrical designs created by complex computer algorithms. The use of quantum computing in traditional quantum networks for cryptography purposes such as data protection from hackers. Conclusion Although the power of technology to influence our daily lives is well known, quantum computing will bring something completely new to the table. Researchers are always working to create a quantum computer that comes the closest to being "perfect." Quantum computing will soon permeate a wide range of academic fields, from genetics and geography to chemistry and physics, as I've highlighted above and across this site. But the most important thing to keep in mind is that post-quantum encryption will be required since it is possible that quantum computing will be misapplied.

Impact of Quantum Computing on Software Development

In simple terms, computer vision trains the computer to visualize the world just like we humans do. Computer vision techniques are developed to enable computers to “see” and draw analysis from digital images or streaming videos. The main goal of computer vision problems is to use the analysis from the digital source data to convert it into something about the world. “What is computer vision?” Computer networks are one of the most popular and well-researched automation topics over the last many years. But along with advantages and uses, computer vision has its challenges in the department of modern applications, which deep neural networks can address quickly and efficiently. Here are the top 8 Challenges in Computer Vision. Network Compression With the soaring demand for computing power and storage, it is challenging to deploy deep neural network applications. Consequently, while implementing the neural network model for computer vision, a lot of effort and work is put in to increase its precision and decrease the complexity of the model. For example, to reduce the complexity of networks and increase the result accuracy, we can use a singular value decomposition matrix to obtain the low-rank approximation. Pruning After the model training for computer vision, it is crucial to eliminate the irrelevant neuron connections by performing several filtrations of fine-tuning. Therefore, as a result, it will increase the difficulty of the system to access the memory and cache. Sometimes, we also have to design a unique collaborative database as a backup. In comparison to that, filter-level pruning helps to directly refine the current database and determine the filter’s importance in the process. Reduce the Scope of Data Values The data outcome of the system consists of 32 bits floating point precision. But the engineers have discovered that using the half-precision floating points, taking up to 16 bits, does not affect the model’s performance. As the final solution, the range of data is either two or three values as 0/1 or 0/1/-1, respectively. The computation of the model was effectively increased using this reduction of bits, but the challenge remained of training the model for two or three network value core issues. As we can use two or three floating-point values, the researcher suggested using three floating-point scales to increase the representation of the network. Fine-Grained Image Classification It is difficult for the system to identify the image’s class precisely when it comes to image classification. For example, if we want to determine the exact type of a bird, it generally classifies it into a minimal class. It cannot precisely identify the exact difference between two bird species with a slight difference. But, with fine-grained image classification, the accuracy of image processing increases. Fine-grained image classification uses the step-by-step approach and understanding the different areas of the image, for example, features of the bird, and then analyzing those features to classify the image completely. Using this, the precision of the system increases but the challenge of handling the huge database increases. Also, it is difficult to tag the location information of the image pixels manually. But in comparison to the standard image classification process, the advantage of using fine-grained classification is that the model is supervised by using image notes without additional training. Bilinear CNN Bilinear CNN helps compute the final output of the complex descriptors and find the relation between their dimensions as dimensions of all descriptors analyze different semantic features for various convolution channels. However, using bilinear operation enables us to find the link between different semantic elements of the input image. Texture Synthesis and Style Transform When the system is given a typical image and an image with a fixed style, the style transformation will retain the original contents of the image along with transforming the image into that fixed style. The texture synthesis process creates a large image consisting of the same texture. Neural Network Applications in Texture Synthesis and Style Transform a. Feature Inversion The fundamentals behind texture synthesis and style transformation are feature inversion. As studied, the style transformation will transform the image into a specific style similar to the image given using user iteration with a middle layer feature. Using feature inversion, we can get the idea of the information of an image in the middle layer feature. b. Concepts Behind Texture Generation The feature inversion is performed over the texture image, and using it, the gram matrix of each layer of the texture image is created just like the gram matrix of each feature in the image. The low-layer features will be used to analyze the detailed information of the image. In contrast, the high layer features will examine the features across the larger background of the image. c. Concept Behind Style Transformation We can process the style transformation by creating an image that resembles the original image or changing the style of the image that matches the specified style. Therefore, during the process, the image’s content is taken care of by activating the value of neurons in the neural network model of computer vision. At the same time, the gram matrix superimposes the style of the image. d. Directly Generate a Style Transform Image The challenge faced by the traditional style transformation process is that it takes multiple iterations to create the style-transformed image, as suggested. But using the algorithm which trains the neural network to generate the style transformed image directly is the best solution to the above problem. The direct style transformation requires only one iteration after the training of the model ends. Also, calculating instance normalization and batch normalization is carried out on the batch to identify the mean and variance in the sample normalization. e. Conditional Instance Normalization The problem faced with generating the direct style transformation process is that the model has to be trained manually for each style. We can improve this process by sharing the style transformation network with different styles containing some similarities. It changes the normalization of the style transformation network. So, there are numerous groups with the translation parameter, each corresponding to different styles, enabling us to get multiple styles transformed images from a single iteration process. Face Verification/Recognition There is a vast increase in the use cases of face verification/ all over the globe. The face verification system takes two images as input. It analyzes whether the images are the same or not, whereas the face recognition system helps to identify who the person is in the given image. Generally, for the face verification/recognition system, carry out three basic steps: recognition systems Analyzing the face in the image Locating and identifying the features of the image Lastly, verifying/recognizing the face in the image The major challenge for carrying out face verification/recognition is that learning is executed on small samples. Therefore, as default settings, the system’s database will contain only one image of each person, known as one-shot learning. a. DeepFace It is the first face verification/recognition model to apply deep neural networks in the system. DeepFace verification/recognition model uses the non-shared parameter of networks because, as we all know, human faces have different features like nose, eyes, etc. Therefore, the use of shared parameters will be inapplicable to verify or identify human faces. Hence, the DeepFace model uses non-shared parameters, especially to identify similar features of two images in the face verification process. b. FaceNet FaceNet is a face recognition model developed by Google to extract the high-resolution features from human faces, called face embeddings, which can be widely used to train a face verification system. FaceNet models automatically learn by mapping from face images to compact Euclidean space where the distance is directly proportional to a measure of face similarity. Here the three-factor input is assumed where the distance between the positive sample is smaller than the distance between the negative sample by a certain amount where the inputs are not random; otherwise, the network model would be incapable of learning itself. Therefore, selecting three elements that specify the given property in the network for an optimal solution is challenging. c. Liveness Detection Liveness detection helps determine whether the facial verification/recognition image has come from the real/live person or a photograph. Any facial verification/recognition system must take measures to avoid crimes and misuse of the given authority. Currently, there are some popular methods in the industry to prevent such security challenges as facial expressions, texture information, blinking eye, etc., to complete the facial verification/recognition system. Image Search and Retrieval When the system is provided with an image with specific features, searching that image in the system database is called Image Searching and Retrieval. But it is challenging to create an image searching algorithm that can ignore the slight difference between angles, lightning, and background of two images. Classic Image Search Process a. Classic Image Search Process As studied earlier, image search is the process of fetching the image from the system’s database. The classic image searching process follows three steps for retrieval of the image from the database, which are: ~Analyzing appropriate representative vectors from the image ~Applying the cosine distance or Euclidean distance formula to search the nearest result and find the most similar image representative ~Use special processing techniques to get the search result. The challenge faced by the classic image search process is that the performance and representation of the image after the search engine algorithm are reduced. b. Unsupervised Image Search The image retrieval process without any supervised outside information is called an unsupervised image search process. Here we use the pre-trained model ImageNet, which has the set of features to analyze the representation of the image. c. Supervised Image Search Here, the pre-trained model ImageNet connects it with the system database, which is already trained, unlike the unsupervised image search. Therefore, the process analyzes the image using the connection, and the system dataset is used to optimize the model for better results. d. Object Tracking The process of analyzing the movement of the target in the video is called object tracking. Generally, the process begins in the first frame of the video, where a box around it marks the initial target. Then the object tracking model assumes where the target will get in the next frame of the video. The limitation to object tracking is that we don’t know where the target will be ahead of time. Hence, enough training is to be provided to the data before the task. e. Health Network The usage of health networks is just similar to a face verification system. The health network consists of two input images where the first image is within the target box, and the other is the candidate image region. As an output, the degree of similarity between the images is analyzed. In the health network, it is not necessary to visit all the candidates in the different frames. Instead, we can use a convolution network and traverse each image only once. The most important advantage of the model is that the methods based on this network are high-speed and can process any image irrespective of its size. f. CFNet CFNet is used to elevate the tracking performance of the weighted network along with the health network training model and some online filter templates. It uses Fourier transformation after the filters train the model to identify the difference between the image regions and the background regions. Apart from these, other significant problems are not covered in detail as they are self-explanatory. Some of those problems are: : Process of generating short description for an image a] Image Captioning : The process of answering the question related to the given image : The process to provide the visualization methods to understand the convolution and neural networks b] Visual Question Answering c] Network Visualizing and Network Understanding : The model use to analyze the distribution of the image d] Generative Models A modern computer vision enables the system to visualize the data and analyze patterns and insights from the data. This data plays its importance in translating the raw pixels, which computer systems can interpret.

Deep Neural Networks Are Addressing Challenges in Computer Vision

There’s no doubt that the finance industry is undergoing a transformational change. To provide superior service to their customers and outdo the competition, financial institutions are using the latest technologies to understand customer needs, identify opportunities, stay competitive, solve business problems and streamline back-office operations. The recent years have seen a rapid acceleration in the pace of disruptive technologies such as AI and Machine Learning in Finance due to improved software and hardware. The finance sector, specifically, has seen a steep rise in the use cases of machine learning applications to advance better outcomes for both consumers and businesses. Why Use Machine Learning in Finance? Here are some of the reasons why banking and financial services firms should consider using Machine Learning despite having the above-said challenges Enhanced revenues owing to better productivity and improved user experience Low operational costs due to process automation Reinforced security and better compliance Here are a few use cases where machine learning algorithms can be/are being used in the finance sector – Financial Monitoring Machine learning algorithms can be used to enhance network security significantly. Data scientists are always working on training systems to detect flags such as money laundering techniques, which can be prevented by financial monitoring. The future holds a high possibility of machine learning technologies powering the most advanced cybersecurity networks. Making Investment Predictions The fact that machine learning-enabled technologies give advanced market insights allows the fund managers to identify specific market changes much earlier as compared to the traditional investment models. With renowned firms such as Bank of America, JPMorgan, and Morgan Stanley investing heavily in ML technologies to develop automated investment advisors, the disruption in the investment banking industry is quite evident. Process Automation Machine Learning-powered solutions allow finance companies to completely replace manual work by automating repetitive tasks through intelligent process automation for enhanced business productivity. Chatbots, paperwork automation, and employee training gamification are some of the examples of process automation in finance using machine learning. This enables finance companies to improve their customer experience, reduce costs, and scale up their services. Further, Machine Learning technology can easily access the data, interpret behaviors, follow and recognize the patterns. This could be readily used for customer support systems that can work similarly to a real human and solve all of the customers’ unique queries. An example of this is Wells Fargo using ML-driven chatbot through Facebook Messenger to communicate with its users effectively. The chatbot helps customers get all the information they need regarding their accounts and passwords. Secure Transactions Machine Learning algorithms are excellent at detecting transactional frauds by analyzing millions of data points that tend to go unnoticed by humans. Further, ML also reduces the number of false rejections and helps improve the precision of real-time approvals. These models are generally built on the client’s behavior on the internet and transaction history. Apart from spotting fraudulent behavior with high accuracy, ML-powered technology is also equipped to identify suspicious account behavior and prevent fraud in real-time instead of detecting them after the crime has already been committed. According to research, for almost every $1 lost to fraud, the recovery costs borne by financial institutions are close to $2.92. One of the most successful applications of ML is credit card fraud detection. Banks are generally equipped with monitoring systems that are trained on historical payments data. Algorithm training, validation, and backtesting are based on vast datasets of credit card transaction data. ML-powered classification algorithms can easily label events as fraud versus non-fraud to stop fraudulent transactions in real-time. Risk Management Using machine learning techniques, banks and financial institutions can significantly lower the risk levels by analyzing a massive volume of data sources. Unlike the traditional methods which are usually limited to essential information such as credit score, ML can analyze significant volumes of personal information to reduce their risk. Various insights gathered by machine learning technology also provide banking and financial services organizations with actionable intelligence to help them make subsequent decisions. An example of this could be machine learning programs tapping into different data sources for customers applying for loans and assigning risk scores to them. ML algorithms could then easily predict the customers who are at risk for defaulting on their loans to help companies rethink or adjust terms for each customer. Algorithmic Trading Machine Learning in trading is another excellent example of an effective use case in the finance industry. Algorithmic Trading (AT) has, in fact, become a dominant force in global financial markets. ML-based solutions and models allow trading companies to make better trading decisions by closely monitoring the trade results and news in real-time to detect patterns that can enable stock prices to go up or down. Machine learning algorithms can also analyze hundreds of data sources simultaneously, giving the traders a distinct advantage over the market average. Some of the other benefits of Algorithm Trading include – a]Increased accuracy and reduced chances of mistakes b]AT allows trades to be executed at the best possible prices c]Human errors are likely to be reduced substantially d]Enables the automatic and simultaneous checking of multiple market conditions Financial Advisory There are various budget management apps powered by machine learning, which can offer customers the benefit of highly specialized and targeted financial advice and guidance. Machine Learning algorithms not only allow customers to track their spending on a daily basis using these apps but also help them analyze this data to identify their spending patterns, followed by identifying the areas where they can save. One of the other rapidly emerging trends in this context is Robo-advisors. Working like regular advisors, they specifically target investors with limited resources (individuals and small to medium-sized businesses) who wish to manage their funds. These ML-based Robo-advisors can apply traditional data processing techniques to create financial portfolios and solutions such as trading, investments, retirement plans, etc. for their users. Customer Data Management When it comes to banks and financial institutions, data is the most crucial resource, making efficient data management central to the growth and success of the business. The massive volume and structural diversity of financial data from mobile communications, social media activity to transactional details, and market data make it a big challenge even for financial specialists to process it manually. Integrating machine learning techniques to manage such large volumes of data can bring both process efficiency and the benefit of extracting real intelligence from data. AI and ML tools such as data analytics, data mining, and natural language processing, help to get valuable insights from data for better business profitability. An excellent example of this could be machine learning algorithms used for analyzing the influence of market developments and specific financial trends from the financial data of the customers. Decision-Making Banking and financial institutions can use Machine Learning algorithms to analyze both structured and unstructured data. E.g., customer requests, social media interactions, and various business processes internal to the company, and discover trends (both useful and potentially dangerous) to assess risk and help customers make informed decisions accurately. Customer Service Level Improvement Using an , customers can get all their queries resolved in terms of finding out their monthly expenses, loan eligibility, affordable insurance plan, and much more. intelligent chatbot Further, there are several ML-based applications that, when connected to a payment system, can analyze accounts and let customers save and grow their money. Sophisticated ML algorithms can be used to analyze user behavior and develop customized offers. For example, a customer looking to invest in a financial plan can be benefitted from a personalized investment offer after the ML algorithm analyses his/her existing financial situation. Customer Retention Program Credit card companies can use ML technology to predict at-risk customers and specifically retain selected ones out of these. Based on user demographic data and transaction activity, they can easily predict user behavior and design offers specifically for these customers. The application here includes a predictive, binary classification model to find out the customers at risk, followed by utilizing a recommender model to determine best-suited card offers that can help to retain these customers. Marketing The ability of AI and Machine Learning models to make accurate predictions based on past behavior makes them a great marketing tool. From analyzing the mobile app usage, web activity, and responses to previous ad campaigns, machine learning algorithms can help to create a robust marketing strategy for finance companies.

12 Use Cases of AI and Machine Learning In Finance

Robotic Process Automation (RPA) is a relatively recent technology that has fundamentally changed the way we think about routine and repetitive operations. Deloitte’s results confirmed that more than 74% of respondents are open to engaging with upcoming technologies in the near future, with almost 22% of them have already tried or implemented Robotic Process Automation. recent survey Human resources automation using RPA will allow organizations to automate transactional processes so that the HR team can focus on more strategic and meaningful responsibilities. It will enable redirection of time and resources on more productive activities such as giving face time to your employees or conflict resolution. Human Resources Automation using RPA RPA in HR operations focuses on micro-tasks, allowing it to integrate all of the processes and programs that the old systems were unable to handle effectively. This is done by running a software code that performs repetitive and high-volume operational activities on behalf of the HR work staff. The operations that it covers range from hiring new employees, processing payroll, and evaluating benefits to creating compliance reports that are all very time-consuming and labor-intensive. RPA adds accuracy and speed to data processing and can be instrumental in cutting down HR-related costs. The benefits of RPA in HR can revolutionize the industry by enhancing work efficiency and improving ROIs. 9 Real World Use Cases of Human Resources Automation RPA provides firms and their HR departments with a slew of distinct and quantifiable advantages. Some prominent use cases are discussed below: 1. CV Screening and Shortlisting One significant HR activity constitutes screening resumes and applications received for open positions. The RPA technology makes this process considerably easier by quickly consolidating applications and comparing all of the data to a list of predefined job criteria. These requirements may be viewed as predetermined guidelines that influence the whole selection process. The technology also helps with eliminating the need for redundant paperwork by using a database that stores all the potential employees’ profiles. It also automates the communication process by notifying candidates of their interview results. 2. Simplifying Onboarding Employee onboarding is another tedious task requiring simultaneous efforts of multiple people for approvals, sign-off, access rights, account creation, etc. Here, RPA for human resources automation can be used to activate a specific template for a user account’s onboarding workflow. RPA may be utilized to expedite the whole onboarding operation. Software robots may then make rule-based decisions like which onboarding materials to transmit, which credentials to issue to the new employee, and so on. HR automation best practices help in: Reducing onboarding process cost Increasing onboarding speed and efficiency Eliminating manual errors in the onboarding process, thereby increasing accuracy. 3. Employee Data Management One of the critical areas under HR job functionality is employee data management. Maintaining this database requires organized and consistent activities across databases and data formats, ranging from payroll and employee perks and benefits to company rules and regulations. Human resources automation can be leveraged to make jobs like personnel administration much easier to perform, lowering the risk of inaccurate data entry to a bare minimum. RPA additionally performs data cleaning operations regularly to maintain data consistency across numerous databases. 4. Payroll Processing It is no surprise that processing payments are among the most crucial tasks undertaken by the HR department. Unfortunately, it is a very mundane and repetitive task requiring a large amount of data entry regularly and is highly prone to manual errors. Human resources automation can streamline the payroll process by collecting and linking data from numerous systems and databases such as employee management, general ledger, accounts payable, time tracker, etc. They help in employee work-hours verification and create automated reports on the number of working hours registered, overtime, leave, timeouts, missing hours, etc. 5. Tracking Attendance By cross-checking self-reports against time documented in the business record and reporting any inconsistencies to HR management, RPA-based software robots can make attendance monitoring easier. Furthermore, bots might suggest reallocating various labor resources in the event of significant absenteeism, assisting the HR department in avoiding workflow interruptions. 6. Expense Management One of the most tedious and time-consuming tasks of HR managers is to keep track of travel, food, and other such expenditures. The task becomes even more grueling when dealing with missing receipts, late expense submissions, unforeseen expenses, unauthorized spending, etc. Using an RPA system for expenditure management helps businesses extract all of the necessary information from cost receipts automatically, thereby saving time. This also eliminates the need for them to carry around expenditure receipts because they can take photographs of them, and RPA will automatically retrieve all essential data. 7. Calculation of Shift Allowance Shift allowances are calculated through employee in-and-out swipe data stored in various HR backend systems. This data is used to generate shift allowance. Because of the vast datasets, this is an entirely manual procedure that takes a long time and has a significant risk of mistakes. RPA bots read and validate data from several backend systems automatically. Furthermore, the bots can perform this regularly, resulting in on-time allowance clearance and reduced manual efforts, and average handling time with zero mistakes. 8. Performance Management RPA can effectively handle employee performance management by reviewing and analyzing employee’s goal settings, expectations, incentives, and reward calculations. It helps the HR department keep the organization’s productivity consistently high, removes extra manual effort, and reduces employee evaluation chances. 9. Employee Exit Management While exiting an organization, several HR duties must be completed, including the preparation of leave documentation, the revocation of system access, and the full and final settlement. Any scope of error left here can result in several audit flags. RPA in HR may be pretty valuable in implementing a more efficient and well-organized off-boarding process. This is done by pre-determination of all the tasks involved in the exit process. The software extracts all the essential details from the various backend systems and sends emails to all the relevant departments for the next steps. Concluding Thoughts The scope and are continuously growing, and it is no longer restricted to certain aspects of a business. Human resources automation leads to increased accuracy, considerable labor savings, and faster processing times, resulting in happier workers and customers. RoI of RPA

Top 9 Use Cases for Human Resources Automation

Open sourced by Google Research team, achieved wide popularity amongst NLP enthusiasts for all the right reasons! It is one of the best Natural Language Processing pre-trained models with superior NLP capabilities. It can be used for language classification, question & answering, next word prediction, tokenization, etc. pre-trained models of BERT Our case study Question Answering System in Python using BERT NLP and BERT based Question and Answering system demo , developed in Python + Flask, got hugely popular garnering hundreds of visitors per day. We got a lot of appreciative and lauding emails praising our QnA demo. Along with that, we also got number of people asking about how we created this QnA demo. And till the day, we keep getting requests on how to develop such a QnA system using BERT pre-trained model open-sourced by Google. [1] [2] To start with, the readme file on the official GitHub repository of BERT provides a good amount of information about how to fine-tune the model on SQuAD 2.0 but we could see that developers are still facing issues. So, we decided to publish a step-by-step tutorial to fine-tune the BERT pre-trained model and generate inference of answers from the given paragraph and questions on using TPU. Colab In this tutorial, we are not going to cover how to create web-based interface using Python + Flask. We’ll just cover the fine-tuning and inference on Colab using TPU. You can create your own interface using Flask or Django. Overview In this tutorial we will see how to perform a fine-tuning task on SQuAD using Google Colab, for that we will use BERT GitHub Repository, BERT Repository includes: TensorFlow code for the BERT model architecture. Pre-trained models for both the lowercase and cased version of BERT-Base and BERT-Large. You can also refer or copy to follow the steps. our colab file Steps to perform BERT Fine-tuning on Google Colab 1) Change Runtime to TPU On the main menu, click on and select . Set “ “ as the hardware accelerator. Below screeenshot will help you understand how you can change the runtime to TPU. Runtime Change runtime type TPU After Clicking on “Change runtime type”, Select from the dropdown option as given in the below figure. TPU 2) Clone the BERT github repository , or idirectional mbedding epresentations from ransformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. You can find the academic paper here: . BERT B E R T https://arxiv.org/abs/1810.04805 BERT has two stages: and . Pre-training fine-tuning is fairly expensive (four days on 4 to 16 Cloud TPUs), but is a one-time procedure. BERT has released a number of pre-trained models. Most NLP researchers will never need to pre-train their own model from scratch. Pre-training is inexpensive. One can replicate all the results given in the paper, in at most 1 hour on a single Cloud TPU, or a few hours on a GPU. For example, SQuAD can be trained in around 30 minutes on a single Cloud TPU to achieve a Dev F1 score of 91.0%. Fine-tuning So our first step is to Clone the BERT github repository, below is the way by which you can clone the repo from github. Now get inside the Bert repo using “ “ command cd !git clone https: //github.com/google-research/bert.git cd bert 3) Download the BERT PRETRAINED MODEL BERT Pretrained Model List : BERT-Large, Uncased (Whole Word Masking): 24-layer, 1024-hidden, 16-heads, 340M parameters BERT-Large, Cased (Whole Word Masking): 24-layer, 1024-hidden, 16-heads, 340M parameters BERT-Base, Uncased: 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Large, Uncased: 24-layer, 1024-hidden, 16-heads, 340M parameters BERT-Base, Cased: 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Large, Cased: 24-layer, 1024-hidden, 16-heads, 340M parameters BERT-Base, Multilingual Cased (New, recommended): 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Base, Multilingual Uncased (Orig, not recommended): 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Base, Chinese: Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters BERT has released and models, that have uncased and cased version. Uncased means that the text is converted to lowercase before performing Workpiece tokenization, e.g., John Smith becomes john smith, on the other hand, cased means that the true case and accent markers are preserved. BERT-Base BERT-Large When using a cased model, make sure to pass -do_lower=False at the time of training. You can download any model of your choice. We have used the BERT-Large-Uncased Model. !wget https: //storage.googleapis.com/bert_models/2018_10_18/uncased_L-24_H-1024_A-16.zip # Unzip the pretrained model
!unzip uncased_L _H _A zip -24 -1024 -16. 4) Download the SQUAD2.0 Dataset For the Question Answering task, we will be using SQuAD2.0 Dataset. (Stanford Question Answering Dataset) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD SQuAD2.0 combines the 100,000+ questions in SQuAD1.1 with over 50,000 new, unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. You can download the dataset from SQUAD site https://rajpurkar.github.io/SQuAD-explorer/ #Download the SQUAD train and dev dataset
!wget https: !wget https: //rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json //rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json 5) Set up your TPU environment Verify that you are connected to a TPU device You will get know your TPU Address which is used at the time of fine-tuning Perform Google Authentication to access your bucket Upload your credentials to TPU to access your GCS bucket Using code below you can do the above mentioned 4 points: datetime json os pprint random string sys tensorflow tf os.environ, TPU_ADDRESS = + os.environ[ ]
print( , TPU_ADDRESS) google.colab auth
auth.authenticate_user() tf.Session(TPU_ADDRESS) session:
  print( )
  pprint.pprint(session.list_devices()) open( , ) f:
    auth_info = json.load(f)
  tf.contrib.cloud.configure_gcs(session, credentials=auth_info) import import import import import import import import as assert 'COLAB_TPU_ADDR' in 'ERROR: Not connected to a TPU runtime; please see the first cell in this notebook for instructions!' 'grpc://' 'COLAB_TPU_ADDR' 'TPU address is => ' from import with as 'TPU devices:' # Upload credentials to TPU. with '/content/adc.json' 'r' as # Now credentials are set for all future sessions on this TPU. 6) Create an output directory : You will need a GCP (Google Compute Engine) account and a GCS (Google Cloud Storage) bucket to run the colab file. Please follow the Google Cloud for how to create a GCP account and GCS bucket. You have $300 free credit to start with any GCP product, learn more about it at . Prerequisite https://cloud.google.com/tpu/docs/setup-gcp-account You can create your GCS bucket from here . http://console.cloud.google.com/storage As we are using the Cloud TPU, we need to store the pre-trained model and the output directory in the Google Cloud Storage. If you are not storing it on Bucket you may face the following error : ERROR:tensorflow: recorded training_loop: From /job:worker/replica: /task: :
Unsuccessful TensorSliceReader : Failed to get matching files on uncased_L-24_H-1024_A-16/bert_model.ckpt: Unimplemented: File system scheme '[local]' not implemented (file: 'uncased_L-24_H-1024_A-16/bert_model.ckpt')
[[node checkpoint_initializer_14 (defined at /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py:1748) ]] Error from 0 0 constructor You will get your fine_tuned model in the Google cloud storage bucket after completion of training. For that, you need to provide your BUCKET name and OUPUT DIRECTORY name. BUCKET = BUCKET, output_dir_name = BUCKET_NAME = .format(BUCKET)
OUTPUT_DIR = .format(BUCKET,output_dir_name)
tf.gfile.MakeDirs(OUTPUT_DIR)
print( .format(OUTPUT_DIR)) 'bertnlpdemo' #@param {type:"string"} assert '*** Must specify an existing GCS bucket name ***' 'bert_output' #@param {type:"string"} 'gs://{}' 'gs://{}/{}' '***** Model output directory: {} *****' 7) Move Pretrained Model to GCS Bucket Need to move Pre-trained Model at GCS (Google Cloud Storage) bucket, as Local File System is not Supported on TPU. If you don’t move your pre-trained model to TPU you may face the error. The command allows you to move data between your local file system and the cloud, move data within the cloud, and move data between cloud storage providers. gsutil mv !gsutil mv /content/bert/uncased_L _H _A $BUCKET_NAME -24 -1024 -16 8) Training Below is the command to run the training. To run the training on TPU you need to make sure about below Hyperparameter, that is tpu must be true and provide the tpu_address that we have found above. -use_tpu=True -tpu_name=YOUR_TPU_ADDRESS !python run_squad.py \
  --vocab_file=$BUCKET_NAME/uncased_L _H _A /vocab.txt \
  --bert_config_file=$BUCKET_NAME/uncased_L _H _A /bert_config.json \
  --init_checkpoint=$BUCKET_NAME/uncased_L _H _A /bert_model.ckpt \
  --do_train= \
  --train_file=train-v2 .json \
  --do_predict= \
  --predict_file=dev-v2 .json \
  --train_batch_size= \
  --learning_rate= \
  --num_train_epochs= \
  --use_tpu= \
  --tpu_name=grpc:// : \
  --max_seq_length= \
  --doc_stride= \
  --version_2_with_negative= \
  --output_dir=$OUTPUT_DIR -24 -1024 -16 -24 -1024 -16 -24 -1024 -16 True .0 True .0 24 3e-5 2.0 True 10.1 .118 .82 8470 384 128 True Create Testing File We are creating input_file.json as a blank JSON file and then add the data in the file in the SQuAD dataset format. is used to create a file - is used to write a file in the colab - touch %%writefile You can pass your own questions and context in the below file. !touch input_file.json %%writefile input_file.json
{ : , : [
        { : , : [
                { : [
                        { : , : , : },
                        { : , : , : },
                        { : , : , : }
                    ], : }
            ]
        }
    ]
} "version" "v2.0" "data" "title" "your_title" "paragraphs" "qas" "question" "Who is current CEO?" "id" "56ddde6b9a695914005b9628" "is_impossible" "" "question" "Who founded google?" "id" "56ddde6b9a695914005b9629" "is_impossible" "" "question" "when did IPO take place?" "id" "56ddde6b9a695914005b962a" "is_impossible" "" "context" "Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University in California. Together they own about 14 percent of its shares and control 56 percent of the stockholder voting power through supervoting stock. They incorporated Google as a privately held company on September 4, 1998. An initial public offering (IPO) took place on August 19, 2004, and Google moved to its headquarters in Mountain View, California, nicknamed the Googleplex. In August 2015, Google announced plans to reorganize its various interests as a conglomerate called Alphabet Inc. Google is Alphabet's leading subsidiary and will continue to be the umbrella company for Alphabet's Internet interests. Sundar Pichai was appointed CEO of Google, replacing Larry Page who became the CEO of Alphabet." Prediction Below is the command to perform your own custom prediction, that is you can change the input_file.json by providing your paragraph and questions after then execute the below command. !python run_squad.py \
  --vocab_file=$BUCKET_NAME/uncased_L _H _A /vocab.txt \
  --bert_config_file=$BUCKET_NAME/uncased_L _H _A /bert_config.json \
  --init_checkpoint=$OUTPUT_DIR/model.ckpt \
  --do_train= \
  --max_query_length= \
  --do_predict= \
  --predict_file=input_file.json \
  --predict_batch_size= \
  --n_best_size= \
  --max_seq_length= \
  --doc_stride= \
  --output_dir=output/ -24 -1024 -16 -24 -1024 -16 -10859 False 30 True 8 3 384 128 To make it easier for you, we have already created a Colab file which you can copy in your Google Drive and execute the commands. You can access the colab file at: . Question Answering System using BERT + SQuAD on Colab TPU If you have any further questions or doubt then please feel free to post them in comments. We’ll get back to you. Previously published at https://www.pragnakalp.com/nlp-tutorial-setup-question-answering-system-bert-squad-colab-tpu/ References Our case study Question Answering System in Python using BERT NLP BERT based Question and Answering system demo

Stories, News & Mentions about TECHUPLABS

Techuplabs’s Stories on HackerNoon