You needn’t look further than the Consumer Electronics Show (CES) in Las Vegas to see just how many “solutions” miss the mark and are often left in the wastebin of innovation, usually due to an ineffective business model.

Where does artificial intelligence (AI) stand in this context? It’s clear that recent advances in machine learning have led to a great deal of optimism – and fear – around the technology, from bots that outplay human champions, to virtual writers that spin together articles in seconds, to algorithms that can detect diseases years in advance.

How much of this is making it to the mainstream and how much is merely short-lived hype? How is machine learning changing the way companies operate? Let’s begin by understanding what exactly machine learning is before exploring its current applications in the corporate landscape.

1. What is Machine Learning?

In short, machine learning is a branch of AI-driven by data analysis that automates specific processes.

It’s based on the notion that computers can learn from data, identify patterns, and use the information to make decisions – all with little to no human intervention. Put, the idea is that AI can learn without explicitly being programmed to.

We can create an example using the Photos app on Apple devices.

It uses artificial intelligence and machine learning to streamline the tagging process. When you tag someone’s face in one of your pictures, the program searches through the rest of your library to find the same person and automatically attach the associated tag.

The technologies in question essentially allowed the app to “learn” who your friend is and their name. Most machine learning algorithms operate in this manner.

They use mathematical models to predict outcomes, whether it’s figuring out who’s in your photo, what the value of your stocks will be in the future or the probability that your loan application will be accepted.

As these predictions are developed and made more precise, they can be implemented in previously considered extraordinarily challenging or even impossible scenarios. We’ve already seen this with realistic renderings of pictures of people that never existed and the development of cures for dangerous viruses.

2. How Will Machine Learning Change Business?

There are, namely, two ways that machine learning will change the way organisations operate.

At lower levels, the technology can take over predictive and menial tasks that employees initially performed, saving time and improving efficiency. This can manifest in countless ways.

For instance, radiologists may use artificial neural networks to review more x-ray slides, while customer support services can send quick responses.

But we already realise the potential to go far beyond this. Prediction machines have become so accurate and reliable that they can change how companies do things in more complex ways.

For instance, Amazon is using machine learning algorithms to recommend products to shoppers. The aim is to provide more relevant content while benefiting the customer with a more convenient experience.

Online streaming services use similar technologies to offer music, movies, and videos that users are more interested in.

You can read more about how popular streaming platforms are using machine learning here and how technology is being used to power autonomous vehicles.

Going back to Amazon, the precision of predictive technologies can reach another, even higher level. In doing so, it might change their entire business model. Currently, the online shopping giant uses a shop-then-ship method. You make a purchase, and they deliver your products to your door as quickly as possible.

Another approach is to switch the model around to ship-then-shop. Amazon uses machine learning to determine what you need and sends the products your way. If you need them, you pay, and if not, the products are returned at the company’s expense. Of course, this only works if the prediction model is accurate enough.

3. Who Uses Machine Learning Today?

In that day and time, the vast majority of large businesses in industries that work with enormous amounts of data have already recognised and leveraged the value of machine learning technology set’s take a look at six key sectors where machine learning is currently being used.

i. Financial Services

Banks, loan providers, and other financial industry organisations use machine learning technology in various ways. The two primary purposes are to identify insights from data and to prevent fraud. The former can reveal hidden investment opportunities, while the latter can identify high-risk clients and avoid cybersecurity threats before an attack occurs.

ii. Healthcare

Some of the various primary uses of machine learning can be found in the healthcare sector. This comes in the form of wearable devices and sensors capable of assessing patient data in real-time. Machine learning can also assist medical experts in analysing data to improve diagnosis and treatment.

EnergHere’sre’s another industry where the uses for machine learning are nearly endless and ever-expanding. It can help find and implement new energy sources, analyse minerals in the ground, predict equipment failure, and streamlining distribution to improve efficiency and reduce expenses.

iii. Government

Even among government agencies, which are often viewed as behind-the-times, machine learning is utilised in various areas. The technology makes particular sense here as governments have access to vast amounts of data, which can save citizens money, detect fraud, and prevent identity theft.

iv. Retail

As we touched on earlier, online retail is especially conducive to AI and machine learning. Websites can use it to make accurate recommendations on previous purchases by analysing your shopping history. Retailers also rely on machine learning to implement marketing campaigns, optimise prices, and gain customer data insights.
Transportation

The utility of machine learning in transportation ranges far and wide. The transportation industry relies on making routes more efficient and foreseeing potential needs to increase profitability. Data analysis is fundamental to delivery companies, which illustrates an intersection between the industry and retail.

These are just a fraction of the uses for machine learning in some industries where the technologies are valued.

4. What are the Current Themes in Machine Learning

Let’set’s end off with a few key trends shaping the machine learning landscape today?

i. Processing Power

Artificial intelligence and machine learning have only started gaining mainstream popularity in recent years, mainly due to the need for many logic engines spread across a large amount of high-speed, dense flash memory. Only recently have the demands for neural net-based deep learning been met by the required computing power.

It was found that combining both CPUs (central processing units) and GPUs (graphics processing unit) can improve the speed of deep learning and similar analytics methods.

ii. Cloud

Another reason for the recent boom in AI is the widespread availability of capable cloud technology. Cloud computing is instrumental in democratising AI by enabling companies to access the technology and machine learning systems’ necessary computing capacity.

iii. Cybersecurity

Now more than ever before, organisations survive on the ability to adequately protect their private data and mitigate the risk of cyberattacks. The traditional prevention-based approach to this problem has been replaced with a more active detection of threats than machine learning.

iv. Behavioural Analytics

With an endless onslaught of security alerts, businesses might struggle to discern real threats from harmless anomaliIt’sIt’s not uncommon for systems to detect breaches days or even weeks if it’s too late. Behavioural analytics helps by using various techniques, namely machine learning, to detect threats in large volumes of data more reliably.

v. Online Fraud

Another security issue that affects consumers just as much as organisations is online fraud, which often remains under the radar for months before inevitably causing significant financial and reputational damage. Modern online fraud detection systems use a combination of machine learning and behavioural analytics and identity authentication.

While these technologies improve fraud detection systems’ efficacy, they also help cybercriminals develop more advanced tools. This has sparked a never-ending race to stay ahead of the enemy.

vi. Advertising | Machine Learning Business

Among the main challenges that marketers face is the tighter regulation of the digital advertising sector. Factors like data privacy and protection, along with copyright, fake news, and tax avoidance, are all prime for code. Machine learning-driven tools are becoming available to assist advertisers in creating effective campaigns.

The solutions include responsive search advertisements that use machine learning to distribute content and automatic adjustment of bids to optimise ad performance on video streaming platforThere’sre’s no clear end to the applications for AI and machine learning in the business world. Only time will tell what the future brings for these technologies and their impact on society as a whole.

The post Machine Learning: Why It Matters for Businesses appeared first on Tech India Today.

Technology is evolving at a fast rate, and lenders need to keep up with the growing trends as new start-ups and FinTech companies join the competition.

At the core of the disruption caused by recent technology trends in the mortgage industry are three main factors:

• New business models
• Economic pressure
• The convergence of mortgage and real estate business

These three factors are single-handedly responsible for the change in the whole outlook of the mortgage industry. Previously, a real estate agent was at the centre of all home transactions, but currently, the customers are the most important.

Why has tech caused such a severe disruption in the mortgage industry?

One of the main reasons why the mortgage industry has experienced a significant disruption is because lenders opted to seek ways to automate, speed up, and simplify steps of the mortgage origination process.

FinTech lenders, therefore, created a completely online mortgage application and approval process supported by centralized underwriting operations.

The Amazon effect is also taking root in the mortgage-lending sector. Today, customers are more tech-savvy and choosing their options carefully before taking up a home mortgage.

This has necessitated the need for mortgage lenders to employ tech in their processes to provide a better customer experience and services.

Trends that are responsible for the disruption in the mortgage industry

1. The digitalization of the mortgage ecosystem

Technology has revolutionized not only the mortgage process but also the mortgage ecosystem. Everything from creating a new listing to securing a homeowner’s title insurance has been digitized.

The digitalization of the entire mortgage ecosystem has led to the introduction of electronic/hybrid mortgage closing, which is slowly becoming a norm in the industry.

The incorporation of technology such as the eSignature has provided a customizable, branded signing solution that speeds up the process and provides a sleek customer experience.

2. Single source validation and automated data collection

Without any measures in place, customer validation is a complicated process, which is time-consuming. However, with the current technological trends, acquiring a borrower’s information can be quickly done in a few steps. This data is easily collected using 1003 applications that are available online.

In 2016, Fannie Mae launched the Day 1 Certainty program that automatically passes customer details from financial institutions, which in effect speeds up the mortgage processing time and cuts down costs.

3. Self-service channels for borrowers

In today’s world, the world is increasingly connected, and customers look for instant gratification. They always want to have access to products and portfolios and compare them against similar products and portfolios. Self-service is becoming increasingly common in the mortgage industry.

Better self-service channels help provide useful information and educational tools that facilitate consumer access and cut down costs. Borrower portals are assisting customers in checking their credit scores, generate pre-approval applications, and run their files through digital underwriting processes.

4. Mobility

Mortgage lenders are devising their operations with mobility in mind in this changing landscape. People move periodically handling their mobile devices, and lenders need to make their products and services more accessible.

Mortgage service providers are creating apps with different functionalities that keep consumers up to date with mortgage rates, mortgaging processes, and mortgage information. Web and mobile apps help consolidate and streamline service provision and ensure customers can easily manage their finances.

5. Big data

The availability of mass data has made it imperative for lenders to derive actionable conclusions that help them offer better quality and diverse services. Machine learning algorithms can potentially help lenders identify trends in the market. This helps lenders provide better products and support customer requirements efficiently.

The growth of digital technology, however, carries some risk for mortgage lenders. Data theft is a significant concern, and the customer’s sensitive information needs to be adequately secured.

In a nutshell, technological diffusion has helped speed up the mortgage origination process and reduced capacity constraints commonly experienced during peak demand.

This has strengthened the ability of the mortgage market to transmit mortgage policies to households. These tech trends have also influenced refinancing decisions making them more streamlined and efficient for most businesses and consumers.

The post Recent Tech Trends That Have Caused Serious Disruption in the Mortgage Industry appeared first on Tech India Today.

Cutting edge technology is the most advanced in information technology (IT). Cutting edge technologies are the future for a high level IT developers. The cutting edge is also called as leading-edge technology or state-of-the-art technology.

Cutting-edge equipment is the most advanced in a particular field. The innovation in cutting edge areas is connected to smart devices like IoT (Internet of Things), Smart homes, etc.

In information technology (IT) industries, cutting edge is frequently used to describe disruptive technologies such as the most recent technological improvements.

The IT leaders are pressured to incorporate cutting edge technologies and IT services. The option is to permit shadow IT to build the organization’s danger craving and maybe present operational and security risks to expand its consistent trouble.

The term “cutting-edge technology” is an ambiguous word of content and often used in marketing and technology.

The related terms related to cutting-edge technology are State-of-the-Art Technology, Leading-Edge Technology, and Bleeding Edge Technology.

1. Top 11 Examples of Cutting-edge Marketing Technologies

1. High Tech and High Fashion Technology
2. Augmented Reality (AR) Advertising
3. Virtual Reality (VR) Shopping Experience
4. NFC Technology
5. IoT and Wearable Technology

6. Facial Recognition Technology
7. Cloud Computing
8. Artificial Intelligence (AI) Technology
9. 3D Body Scanning Technology
10. Social Media Command Centres
11. Robotics and Automation

2. Video About Cutting-edge Technology

The post Cutting Edge Technology: The Future of IT Developments appeared first on Tech India Today.

The world RPA business size was valued at USD 846 million in 2018 and became the speed-growing part of the worldwide enterprise software market. Also, it is anticipated to register a CAGR of 31.1% in the next five years.

The world artificial intelligence business size was valued at USD 24.9 million in 2018, and it is anticipated to reach a CAGR of 46.2% from 2019 to 2025.

When you look for the trending technologies for 2020, you will find RPA and Artificial Intelligence on the top ten of the list. The trend of machine learning and bots is only going to escalate, which implies that RPA and ML are going to become an invaluable skill to have, and people who take a Machine Learning online course will be in demand.

When you read the above discussion, you find that RPA and AI or ML are quite similar terms and may be interrelated. It is thought that every aspect of automation is related to artificial intelligence. But this is not so. RPA and ML are horizontal techniques, both with different goals and interfaces.

Yes! You are thinking it right. Let’s dive deep into the two terms.

1. What is RPA or Robotic Process Automation?

Robotic Process Automation is an application of technology that is aimed at the automation of business processes, which is governed by business logic and structured inputs.

That means RPA tools can be used to configure software or a robot for manipulation of data, triggering responses, capture and translate applications for processing transactions, and communicating with other digital systems.

Put, mimicking human actions to perform a series of steps that lead to meaningful activity and doesn’t require human intervention is referred to as robotic process automation.

Application of RPA can be made for basic tasks like replying to an email as well as for very complex tasks like deployment of thousands of bots, each meant to automate jobs in an ERP system.

Today RPA is being used in almost every sector that includes supply chain management, human resources, customer service, healthcare, financial services, accounting, and more.

RPA benefits include the accuracy and consistency of the tasks. It reduces manual labor, and the best part is that it requires no or minimal coding. It increases the productivity of a company and reduces costs by eliminating human intervention.

2. What is Machine Learning?

As per the definition given by Arthur Samuel, a pioneer in the field of Artificial Intelligence and the one who coined the term Machine Learning, ‘Machine Learning is a range of study that proffers computers the ability to learn without being explicitly programmed.

Machine learning (ML) is a sub-part of Artificial Intelligence (AI). The two terms can be generally used interchangeably.

Machine learning involves improving the learning process of computers based on their experiences, without any human assistance or without being programmed.

First of all, sound quality or relevant data is fed into the system. Then the computer is trained with the help of machine learning models that are based on the data provided and different algorithms.

The type of data and the task to be done form the basis of selecting an algorithm to train the machine or automate the required task.

Let us see the most common example of machine learning.

When you look for some watches online, below comes a recommendation, ‘you may also like,’ and there are pictures of some more eyes that are similar to the one you were looking at.

This standard application of Machine Learning is called ‘Recommendation Engine.’

Another example of Machine Learning is Google, Alexa, and Siri.

Yourself can request them to tell you of your tasks and anything on a smartphone.

It is based on the learning pattern of humans. We see, and we learn from our experiences. Likewise, machines are fed with inputs and related algorithms.

3. Machine Learning vs RPA

When you read the basic introduction of machine learning and RPA, you find them similar; both are involved in the automation of tasks. But if you fall more bass, you find that there are differences in their working and execution.

The fundamental difference between RPA and ML is based on Doing and Thinking. While RPA is associated with ‘doing,’ ML is related to ‘thinking’ and ‘learning’ and acts accordingly.

RPA is used for automating repetitive tasks like sending emails or downloading the attachments, retrieving the subject.

On the other hand, ML can manage your mails, pick out useful insights from them, and can also convert unstructured data to structured data for your ease.

The image below shows you how ML and RPA are different.

Robotic Process Automation (RPA) is a software robot that can mimic human actions, whereas ML expresses its artificial intelligence by exhibiting ‘adaptation,’ which is one of its biggest characteristics.

Image Source

Put, RPA acts more like an essential resource that executes actions based on its configuration and can’t think out of the box.

On the other hand, Machine Learning autonomously improves its performance as the system is fed with observational data and real-time problems, just like humans improve their actions with experience.

The two technologies are also compared by some people as brains over brawn, with RPA being the latter and ML being the former.

Another difference between ML and RPA rests on their area of focus.

RPA, as the term itself implies, is highly process-driven. It is all about automating iterative tasks that are rule-based and typically require communication with multiple and disparate IT systems.
For implementing RPA, the major prerequisite is the process discovery workshop to map the existing process.

On the other hand, ML is data-centric. It is fed with high-quality data and machine learning algorithms. There are no repetitive tasks, but it works on the given input and acts as it has been learned to do so. For example, you can ask your smartphone to set the alarm for you. Then you can ask it to type a message and send it to some recipient. So there is no repetition.

In short, ML is data-centric, and RPA is process-centric.

4. Bottom Line

We now know that RPA and ML, both the technologies are trending these days, both have their different use cases. They have their implications and are applied in almost every sector. Also, both have their benefits too. So you can choose any of them as your career and reach new heights.

RPA and ML are both invaluable solutions that have the potential of enhancing business performance for any organization. They are applied according to the most critical business requirements that can be improved through automation.

The post Robotic Process Automation (RPA) vs Machine Learning (ML) appeared first on Tech India Today.

Natural language processing is a branch of AI that enables computers to learn and interpret human language. It is being used today by digital marketers to analyze customer intent and improve customer experience in ways that weren’t possible in the past.

1. How natural language processing works

Vast amounts of data exist today that can be mined for useful information. A large number of this data, consisting of emails, images, audio, social media posts, text messages, etc. is unstructured data.

Computers can go through data and analyze it to find patterns, but the problem is that machines find it difficult to understand human language. It is bound together by almost arbitrary rules – intonation, context, grammar, syntax, etc.

NLP uses algorithms to teach a machine to identify the intent of a speaker. The algorithm is trained based on examples. Historically, the algorithms were pretty bad at interpreting human language, but they have improved considerably. Now, when you open a website, you will often find a chatbot that works based on natural language processing and can understand and answer your queries.

As conversations can now take place between humans and computers, many things have benefited, and some examples of natural language processing include automatic text summarization, entity recognition, speech tagging, and topic extraction.

2. Applying NLP in digital marketing

One of the first requirements of using NLP is to have systems in place that can take advantage of the data as well as systems that can pass it on to yet other methods that can take action using it.

Coming together, NLP might run behind the scenes as a spam filter, a spell-checking app, a translation tool, or a chatbot. An NLP application that is probably most useful to marketers is sentiment analysis, which can provide them with actionable customer insights.

3. Sentiment analysis

Assume you are speaking to a friend about a product you bought. Sentiment analysis has advanced enough that it can give insight not only into what you are saying about the product but how you feel about it.

Most use of NLP in marketing revolves around social media. Social listening is a mainstream feature enabled by NLP. The technology is used to sift through millions of mentions about a given topic, pull out the most important ones and identify the overall ‘feeling’ about the subject, i.e., whether it is positive, neutral, or negative.

Marketers know that not all mentions are positive ones, and NLP can help to find negative remarks. Marketers can then address these to mitigate any negative consequences. Likewise, sentiment analysis can help marketers to identify people with a clear intention to purchase so they can take the necessary actions to make them aware of their brand.

Some NLP-enabled apps focus on specific social media platforms, and others are built into social media management apps, such as Hootsuite.

4. Search engine optimization (SEO)

Google BERT is the newest Google algorithm update that leverages natural language processing (NLP) and machine learning to improve searches. How does this affect brands and the content they produce going forward?

Any content that’s precise, well-written, and relevant will rank well, and brands that have already been creating high-quality content may see a boost. In creating content, it is essential to ask the questions an audience would ask and then proceed to answer them.

The popularity of voice shopping continues to expand, and when people search using voice, they use longer sentences than they may use when doing a text-based Google search. This means that varying keywords and long-tail key phrases become important in written content.

For a while now, writers have been able to use NLP in real-time to examine content as it is being written and get suggestions for improving it. It is possible to optimize average writing in this way highly. MarketMuse is one AI content intelligence and strategy platform that claims to be able to transform how you research, plan, and craft content.

5. Customer experience

Marketing and customer experience are not the same, but they’re closely related. Stress-free customer interactions are vitally important for overall company success.

Improving the performance of chatbots using NLP can improve the customer experience. Chatbots can respond to queries around the clock; they are objective and never in a bad mood. They can handle simple questions, and those they can’t deal with are passed on to humans who can answer them.

Customers must be able to access the information they need quickly and interact naturally with these tools that can help them. Automatic categorizing and tagging of customer support tickets based on sentiment analysis, for example, is a way for companies to ensure that the most critical queries are handled first.

Email marketing is still beneficial, and using NLP can help its ROI to improve even further. For example, NLP can measure how often users respond to specific keywords, which content attracts new users, and which headlines work better for individual users.

Chatbots can even offer significant marketing benefits in terms of conversions and sales when combined with targeting and marketing psychology. Retailer Asos found that their orders increased when they started using a Facebook Messenger chatbot, Enki, instead of a traditional ‘boring’ gift bot. They reached more people and saw a 250 percent return on spend.

Conclusion

Many new NLP-enable apps use actionable data to achieve a particular purpose. The degree to which companies move into using them will influence how NLP affects digital marketing in future years.

NLP-powered tools are continually evolving, and it’s essential to keep an eye on those that are being made available. No matter whether you are a small or large business or what you’re marketing, they offer some of the most practical and exciting uses of big data available to marketers today.

The post The Impact of Natural Language Processing on Digital Marketing appeared first on Tech India Today.

You’ll also come across some lesser-known options that make quite a lot of sense, at least financially. That doing answered, there are some distinct drawbacks to going for one of those less popular options, and they may be very far from the best option for you.

To make things a bit easier for you folks who are looking to invest in such a system, let’s take a look at two things. First, we’ll see those big brands, who they are, and what they offer.

And then we’ll take a look at some of the lesser-known options and see if it would be a smart choice to save a bit of money.

What Are Those Big Brands? – Less Popular Voice Assistants and Popular Voice Assistants

If you take a look at the industry, you’ll see it’s mostly dominated by Amazon, Google, and recently Apple. The companies have a massive market share, and their respective voice assistants are arguably the best on the market.

Amazon’s Alexa is currently the most popular option.

It comes with any of Amazon’s smart speakers and has support for the most significant number of original products, such as smart cameras, door locks, video doorbells, and lights. It’s also relatively reasonably priced, but you might not get all the user-friendliness you expected from it.

Then you’ve got Apple’s HomeKit. Even though this is a pretty new player in the industry, you’ll find that many people who have invested in Apple’s ecosystem find that this is the best option for them.

Apple has made sure the system is incredibly easy to set up and use, and when you get compatible devices, it works like a charm. The problem with it, however, is finding compatible devices – since they’re new to the game, not a lot of brands have proper support for them.

Last but not least, we’ve got Google Assistant. You don’t have to have a smart speaker for it, and you can use it from your Android phone, provided it has the Google Assistant. When you look at user-friendliness and device compatibility, it’s somewhere between Amazon and Apple, but it’s closer to Alexa because it supports a lot of devices.

What About the Less Popular Options? – Less Popular Voice Assistants and Popular Voice Assistants

AI by Xiaomi, Bixby by Samsung, and Cortana by Microsoft Less Popular Voice Assistants” />

Even among the “less popular” options, there are three that you’ll come across very often. We’re talking about Xiao AI by Xiaomi, Bixby by Samsung, and Cortana by Microsoft.

You might be familiar with them from other places, too – Bixby has been preloaded on many Samsung smartphones in the past period, and Cortana comes with Microsoft’s Windows 10 operating system.

The first difference between these three and the more common options we mentioned above is the compatibility with devices.

When you look at most security cameras or smart locks, you’ll see “Works with Alexa,” or “Google Home compatible.” Still, not many of them say they’ll work with Xiao AI, especially if Xiaomi doesn’t make them.

Of course, how much this impacts you depends on the devices you’re using, so if you’ve already invested in a security system, check if it’s compatible.

The second thing is reliability. The more popular options have a proven track record, and they’ll work well, without any problems.

This is a critical aspect of home security because your home isn’t all that secure if your system doesn’t work well. You’ll want everything to be reliable, and you’ll want it to work 24/7.

Last but not least, we must mention price, as this is where the less popular options tend to win, especially if you go for Xiaomi’s products.

If you take a look at the entire ecosystem as a whole, both the hardware and the software, you’ll find that Xiaomi’s products, for example, are a lot cheaper than competitive products that are made for Alexa, Google Assistant or Siri. And if you’re going to be investing in an entire system, this will make a significant difference.

Are They Worth It?

You can’t deny that you’ll be saving a lot of money by getting some less popular options. However, it would better if yourself kept in mind that your home’s security is in question here, and saving might not be the top priority – reliability should be.

If you want to play things smart, you should go with an option that has a proven track record and is known to work well, in a variety of situations – it’s your home’s security and your peace of mind we’re talking about, after all.

The post Less Popular Voice Assistants , Popular Voice Assistants – Are They Worth It for Home Security and Automation appeared first on Tech India Today.

Developers often find difficulty in combining writing and mathematics in a uniform platform, to help them out with such a problematic situation, TensorFlow is here to solve all their worries.

TensorFlow is a python based open framework of Google that enables us to create models on Deep learning.

Deep Learning is a subsection of Machine Learning computational algorithms that utilize neural networks of multiple layers to build complex models and train them with unstructured data to produce suitable output.

1. What are the main features of TensorFlow?

TensorFlow incorporates the models and algorithms of Machine Learning with that of Deep Learning to form what we commonly call as Neural Networking. Neural Networking is a common juncture that is very useful for developing models based on AI.

TensorFlow utilizes Python to produce a suitable front-end API for all the building applications in the framework that happens to execute the developed applications in higher-level C++.

TensorFlow is capable of training complex networks of neurons with the specific inputs to classify among many handwritten digits and recognize images.

TensorFlow also enables us to build models based on proper sequences that can be successfully translated into machines.

Models developed through the TensorFlow computational framework are capable of processing Natural Languages.

Through TensorFlow, the deep learning neural networks can be trained to run on simulations that are based on Partial Differential Equations.

Developers can use the individual training models for predicting products on a scale via TensorFlow open Library support.

2. How does TensorFlow work?

AI developers can create dataflow structures using TensorFlow. Dataflow structures describe the movement of particular data through a series of nodes that are capable of processing products.

Each node represents individual mathematical operations in the graph with the unique nodal intersections representing a tensor, a multidimensional array of data.

A programmer can get access to all these provisions from TensorFlow via Python programming language, which is comparatively easy to learn.

Programmers can use a couple of abstractions of higher level by convenient ways that can be availed through Python.

Individual nodes or tensors represent Python objects in TensorFlow.

Programmers using TensorFlow get to avail of the transformational libraries that are written in high-level C++ binary notations to produce better performance output.

In Python, the actual algorithm and mathematical calculations are not performed. Python directs the pieces and provides programming abstractions of higher levels for the better compilation of the data pieces together.

3. On which platform does TensorFlow run?

TensorFlow can run on any target platform. You can run TensorFlow on any device as per your convenience. Below listed are some of the standard target platforms for TensorFlow.

Programmers can run TensorFlow on any local computer device.

• TensorFlow can also run on a cloud cluster.
• Developers can also run TensorFlow on any Android-based device and iOS operating devices.
• Standard CPUs, as well as GPUs, can serve as the right TensorFlow platforms.
• Developers who have access to Google’s cloud may run TensorFlow on the silicon named TensorFlow Processing Unit customized by Google, to experience better acceleration in the computational process.
• The models that are developed on TensorFlow are implemented on mechanical devices that are subjected to react to the predictions.

4. Benefits of using TensorFlow.

The revolutionary benefit provided by TensorFlow in the field of Machine Learning is Abstraction. This has enabled developers to focus more on consistent application rather than dealing with the fundamentals of the algorithms and curbing out means to connect between the input and output of different functions.

All the details regarding the development of machines based on deep learning that could learn from unstructured data sources are dealt with great care in TensorFlow.

Developers using TensorFlow can get additional access to TensorFlow Apps.

Extra conveniences are offered to all developers from TensorFlow, who tend to debug.

Developers can transparently evaluate and modulate individual operations on the graph using the eager execution mode available at TensorFlow.

In eager execution mode, developers do not have to construct the entire chart in a single go and evaluate the same as a bulk at the end.

Each operation can be assessed and checked step by step, releasing the pressure from the developers to review the entire job after its completion.

Developers can inspect and manage the execution of the graphs using an online interactive dashboard using the TensorBoard visualization option.

In addition to the above benefits, TensorFlow also possesses many other advantages. Google has made several significant features on TensorFlow, which has made it easier to avail and utilize as per convenience. The TPU silicon feature has enabled developers to accelerate their performance in Google’s cloud.

TensorFlow even features an online-based hub that enables Deep Learning model developers to share their created models within a framework. TensorFlow is mobile-friendly; as such, developers find it easy to modulate their input data whenever any spark clicks in their mind.

It can be accessed through any browser which solves more than a dozen difficulties related to access issues. Developers find it extremely user-friendly to structure their Deep Learning models putting all the mathematical inputs in a standard programming language.

5. Why should you choose TensorFlow?

For all new developers out there, TensorFlow appears to be the best platform for machine learning that serves as an end-to-end open source to provide all sorts of solutions to the issues related to model developments for AI-infused machines.

It features compact and structurally organized flexible tools with access to libraries and several community resources, enabling AI developers to enhance their skills in Machine Learning. Using TensorFlow, all new developers can build their innovations and deploy their applications through Advanced Machine Learning.

TensorFlow is extensively used by a large number of Deep Learning machine developers, researchers, and enterprises based on Machine Learning to solve several problems that are practically impossible to execute in real life. Being a complex discipline, implementation of Machine Learning has always been difficult.

The introduction of a python friendly open framework from Google has made it easier for all developers to train models with the acquired data and produce future outputs. Using TensorFlow developers can train all their self-created ML models using high-functioning APIs that help to iterate and debug models in almost no time.

Developers can now quickly deploy their models in the Google cloud with access to any browser and any language. For a developer, TensorFlow is a vast platform to use your models and integrate them with machines, thus advancing a step ahead in the world of AI.

The post Deep Learning with TensorFlow | open-source software | DataFlow appeared first on Tech India Today.