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The Future of AI and Machine Learning: What the Experts Say?

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The Future of AI and Machine Learning: What the Experts Say?

It’s hard to ignore the growing prowess of AI and machine learning.

Previously, Gartner predicted that AI will become one of the key priorities for more than 30% C-Suite professionals by 2020. Indeed, it’s true; software vendors across the globe are following this new gold rush. For them, data is like new oil. In this blog, we explore the future of this budding technology and gain some new insights and ideas. Let’s see what the heavyweights from the digital industry have to say:

Hyper-targeting and Personalization

Ben Wald, Co-Founder & VP of Solutions Implementation at Very

Though machine learning is a subset of data analysis, it’s rapidly influencing the IoT industry and its respective devices. In the last couple of years, nearly 90% of data was generated through an array of smartphones, watches and cars. These mountains of data help in forming better customer relationships.

How? Using Machine Learning Using Python of course! With this power tool, the corporate houses are trying to understand their target audience and extract crucial information regarding how well they receive their products and related after-sales services. Fine-tuning personalization on a wider scale is the key. Hopefully, soon, we will be able to achieve this goal. We are still in the nascent stage.

Improved Search Engine Experiences

Dorit Zilbershot, Chief Product Officer at Attivio

Did you know that AI algorithms have a massive impact on search engine results?

In the next few years, search engines are expected to enhance user and admin experience: courtesy breakthroughs in neural networks and deep learning technologies. These revolutionary technologies, especially deep learning for computer vision with Python will make sure users enjoy a fabulous searching experience and will deliver highly relevant answers. Currently, we are working on delivering results that are based on user’s query and profile. The process requires a lot of manual configurations and a fundamental understanding of how search engines work. Later, the results will be customized based on individuals’ past preferences, interactions and words used. It will be fun to see how machine learning algorithms transform the dynamo of content publishing and search engines.

Quantum Computing

Matt Reaney, Founder & CEO of Big Cloud

Real and revolutionary, the concept of quantum computing is wreaking havoc in the domain of science and technology. It is the future of machine learning triggering an array of innovations. Integrating quantum computing with machine learning is expected to transform the field triggering accelerated learning, quicker processing and better capabilities. This means the intricate challenges that we can’t solve now could be done in a fraction of time then.

The potential of quantum computing is huge in the future and is likely affect millions of lives, notably in medicine and healthcare industry.

Currently, there are no commercially-built quantum algorithms or hardware available in the market. However, several research facilities and government agencies have been investing in this new field of science of late.

Data Science Machine Learning Certification

End Notes

At DexLab Analytics, we love to craft and curate insights from industry pundits, especially when it comes to something as significant as technological innovations that transform lives altogether. Follow us and stay updated!

 


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Application of Mode using R and Python

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Application of Mode using R and Python

Mode, for a given set of observations, is that value of the variable, where the variable occurs with the maximum or the highest frequency.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will elucidate the Mode and its application using Python and R.

Mode is the most typical or prevalent value, and at times, represents the true characteristics of the distribution as a measure of central tendency.

Application:

The numbers of the telephone calls received in 245 successive one minute intervals at an exchange are shown in the following frequency distribution table:

 

No of Calls
Frequency
0
14
1
21
2
25
3
43
4
51
5
40
6
51
7
51
8
39
9
12
Total
245

 

 [Note: Here we assume total=245 when we calculate Mean from the same data]

Evaluate the Mode from the data.

Evaluate the Mode from the data

Calculate Mode in R:

Calculate mode in R from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Calculate Median in Python:

First, make a data frame for the data.

Now, calculate the mode from the data frame.

Calculate mode in Python from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Mode is used in business, because it is most likely to occur. Meteorological forecasts are, in fact, based on mode calculations.

The modal wage of a group of the workers is the wages which the largest numbers of workers receive, and as such, this wage may be considered as the representative wage of the group.

In this particular data set we use the mode function to know the occurrence of the highest number of phone calls.

It will thus, help the Telephone Exchange to analyze their data flawlessly.

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Note – As you have already gone through this post, now, if you are interested to know about the Harmonic Mean, you can check our post on the APPLICATION OF HARMONIC MEAN USING R AND PYTHON.

Dexlab Analytics is a formidable institute for Deep learning for computer vision with Python. Here, you would also find more information about courses in Python, Deep Learning, Machine Learning, and Neural Networks which will come with proper certification at the end.

We are there in the Social Media where you can follow us both in Facebook and Instagram.

 

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A Deeper Understanding of Deep Learning

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A Deeper Understanding of Deep Learning

To define Deep Learning, it can be summed up as a machine learning technique used to teach computers all those things which comes to humans quite instinctively. This is a sub-classification of the umbrella term Machine Learning and is based on artificial neural networks.

The technology of driver-less cars, of computers with the knowledge of lampposts and trees as non-living entities and with their discretion of differentiating between a pedestrian and a lamppost, all are being developed from Deep Learning. Besides, the voice assistant you find nowadays, that comes with the smartphones, tablets, TVs and hands-free electronic gadgets, everything is matured by Deep Learning.

Deep Learning is an immensely effective technique with huge prospective. Thus, Deep Learning is a highly regarded technology and more and more people are looking forward to finding their career in it.

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Deep Learning: The Path of Success

Among the ever-changing technologies, Deep Learning has its path paved to stand strong in the long run. Now, this is possible primarily because of the high accuracy levels that it has reached.

Pin-pointed Accuracy

With the convincing accuracy levels reached, Deep Learning is believed to be steadfast in situations which involves high risks and which calls for the least margin of errors. For example – driver-less cars.

Extensive Library

If you aim Deep Learning for computer vision with Python, you should be ready with enormous information that it can go through and process quite effortlessly, hence, putting forth an all-inclusive library to be used in real-time. For instance, millions of images, days of video and data should be fed to the system going forward to develop the technology of the driverless car.

Powerful Computing

If we talk about the power that Deep Learning needs, it is astonishingly unreal, the amount of power that this technology expects to perform in its optimum. None other than immensely powerful GPUs are used to get the best results.

As Deep Learning is quite a new thing, unknown in most of its dimensions, here are a few of the fields which have already absorbed or are trying to infuse Deep Learning in constructively.

  • Automobiles – As we have already mentioned that the automobile industry has already taken Deep Learning quite seriously and is effective moving in the direction, where, soon we would witness cars without any human drivers.
  • Defence and Aerospace – Deep learning is constantly taken into account when determining the objects that the satellites bring us. Via Deep Learning we can be sure of the areas/objects in the space. Furthermore, whether a particular zone is fit for the soldiers or not, can also be easily determined by Deep Learning.
  • Pharmacy – Deep Learning is highly significant even in modern medical science. For example, this technology is used to detect cancerous cells.

Deep Learning and AI using Python

With these being said, Deep Learning is simply superb in how it has performed still and the promise that it is showing to be on par with the age. Therefore, if you are seeking for the Deep learning for computer vision course, you can simply avail of Deep Learning for computer vision Training Center in Delhi NCR.

 

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A Beginner’s Guide to Deep Learning: Exploring the Basics

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A Beginner’s Guide to Deep Learning: Exploring the Basics

Over the last couple of years, no other part of data science has made progress like Deep Learning has. From self-driving cars to scientific research, deep learning has been the game-changer in almost every innovation made. Day by day, its influence on our way of life is getting stronger!

Deep learning is a vast and complex field having numerous sections. It takes several months of consistent effort to master the basics before delving deeper into the subject. And a thorough understanding of fundamental concepts of calculus and algebra is essential for getting started with deep learning education.

This article discusses the basics of deep learning for newbies.

Machine Leaning Basics

One must be thorough with the basics of machine learning, which includes reinforced, supervised and unsupervised learning, before starting off your deep learning education. Statistical techniques, like linear regression and logistic regression, are greatly needed in this field.

Deep Learning Introduction

First of all, you need to know the various deep learning frameworks. Deep Learning algorithms draw inspiration from artificial neural networks. While there are many free online courses, a professional course from a reputed deep learning training institute is the ideal starting point for beginners. Additionally, you can follow relevant eBooks, like the Neural Networks and Deep Learning PDF by Michael Nielson.

Understanding Neural Networks

Neural networks have a layered outlay and their functioning resembles the neurons of human brain. Neural networks are made up of an input layer, an output layer and a hidden layer, and produce output after receiving an input – just like human mind works. You need to be familiar with techniques of handling and pre-processing data, regularization methods, data augmentation, hyperparameter technique, etc. These functions of artificial neural network are widely employed in deep learning, helping tasks like image and speech recognition.

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Convolution Neural Network Basics

An important role in deep learning is played by Convolution Neural Network, which is profusely used in object detections, facial recognition, image recognition and classifications, etc. In deep learning, CNN models work by passing the input image through a string of convoluted layers before classifying it with probabilistic values.

Knowing Sequence Models

If you want to go deeper into deep learning, it is crucial to know how to develop models such as Recurrent Neural Networks (RNNs), and make use of alternatives, like Long Short Term Memory (LSTMs) and Gated Recurrent Unit (GRU). Working with audio applications and music synthesis becomes easier when you understand these models.

Unsupervised Deep Learning

A complex topic, but learning it helps crack otherwise unsolvable problems. Problems that remain unclear even after applying supervised learning methods like biasing can be explained with unsupervised deep learning. One popular algorithm of unsupervised deep learning is Autoencoder neural network.

Know Natural Language Processing

NPL deals with understanding human speech and has many benefits.  With the help of computational algorithms, NPL analyzes and represents human language. It can also be employed in dialogue generation, machine translation, etc.

Deep Reinforcement Learning

Deep reinforcement learning has immense potential in deep learning. Reinforcement learning algorithms united with deep learning created AlphaGo, which was successful in defeating the strongest Go players!

Theory isn’t enough; you must implement your deep learning knowledge. And to do that properly, you must be able to use Python.

There’s no need to panic if Python looks like Hebrew at the moment! DexLab Analytics is here to offer expert guidance by skilled industry experts. We offer comprehensive and industry-driven deep learning certification in Gurgaon. You can also check our popular Python certification courses.

 
Reference: https://www.analyticsindiamag.com/the-best-resources-for-learning-deep-learning-for-beginners/


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More than Statistics, Machine Learning Needs Semantics: Explained

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More than Statistics, Machine Learning Needs Semantics: Explained

Of late, machines have achieved somewhat human-like intelligence and accuracy. The deep learning revolution has ushered us into a new era of machine learning tools and systems that perfectly identifies the patterns and predicts future outcomes better than human domain experts. Yet, there exists a critical distinction between man and machines. The difference lies in the way we reason – we, humans like to reason through advanced semantic abstractions, while machines blindly depend on statistics.

The learning process of human beings is intense and in-depth. We prefer to connect the patterns we identify to high order semantic abstractions and our adequate knowledge base helps us evaluate the reason behind such patterns and determine the ones that are most likely to represent our actionable insights.

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On the other hand, machines blindly look for powerful signals in a pool of data. Lacking any background knowledge or real-life experiences, deep learning algorithms fail to distinguish between relevant and specious indicators. In fact, they purely encode the challenges according to statistics, instead of applying semantics.

This is why diverse data training is high on significance. It makes sure the machines witness an array of counterexamples so that the specious patterns get automatically cancelled out. Also, segmenting images into objects and practicing recognition at the object level is the order of the day. But of course, current deep learning systems are too easy to fool and exceedingly brittle, despite being powerful and highly efficient. They are always on a lookout for correlations in data instead of finding meaning.

Are you interested in deep learning? Delhi is home to a good number of decent deep learning training institutes. Just find a suitable and start learning!

How to Fix?

The best way is to design powerful machine learning systems that can tersely describe the patterns they examine so that a human domain expert can later review them and cast their approval for each pattern. This kind of approach would enhance the efficiency of pattern recognition of the machines. The substantial knowledge of humans coupled with the power of machines is a game changer.

Conversely, one of the key reasons that made machine learning so fetching as compared to human intelligence is its quaint ability to identify a range of weird patterns that would look spurious to human beings but which are actually genuine signals worth considering. This holds true especially in theory-driven domains, such as population-scale human behavior where observational data is very less or mostly unavailable. In situations like this, having humans analyze the patterns put together by machines would be of no use.

End Notes

As closing thoughts, we would like to share that machine learning initiated a renaissance in which deep learning technologies have tapped into unconventional tasks like computer vision and leveraged superhuman precision in an increasing number of fields. And surely we are happy about this.

However, on a wider scale, we have to accept the brittleness of the technology in question. The main problem of today’s machine learning algorithms is that they merely learn the statistical patterns within data without putting brains into them. Once, deep learning solutions start stressing on semantics rather than statistics and incorporate external background knowledge to boost decision making – we can finally chop off the failures of the present generation AI.

Artificial Intelligence is the new kid on the block. Get enrolled in an artificial intelligence course in Delhi and kickstart a career of dreams! For help, reach us at DexLab Analytics.

 

The blog has been sourced from ― www.forbes.com/sites/kalevleetaru/2019/01/15/why-machine-learning-needs-semantics-not-just-statistics/#789ffe277b5c

 

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How Deep Learning is Solving Forecasting Challenges in Retail Industry

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How Deep Learning is Solving Forecasting Challenges in Retail Industry

Known to all, the present-day retail industry is obsessed with all-things-data. With Amazon leading the show, many retailers are found implementing a data-driven mindset throughout the organization. Accurate predictions are significant for retailers, and AI is good in churning out value from retail datasets. Better accuracy in forecasts has resulted in widespread positive impacts.

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Below, we’ve chalked down how deep learning, a subset of machine learning addresses retail forecasting issues. It is a prime key to solve most common retail prediction challenges – and here is how:

  • Deep learning helps in developing advanced, customized forecasting models that are based on unstructured retail data sets. Relying on Graphic Processing Units, it helps process complex tasks – though GPUs area applied only twice during the process; once during training the model and then at the time of inference when the model is applied to new data sets.

  • Deep learning-inspired solutions help discover complex patterns in data sets. In case of big retailers, the impressive technology of Deep Learning supports multiple SKUs all at the same time, which proves productive on the part of models as they get to learn from the similarities and differences to seek correlations for promotion or competition. For example, winter gloves sell well when puffer jackets are already winning the market, indicating sales. On top of that, deep learning can also ascertain whether an item was not sold or was simply out of stock. It also possesses the ability to determine the larger problem as to why the product was not being sold or marketed.

  • For a ‘cold start’, historical data is limited but deep learning has the power to leverage other attributes and boost the forecasting. The technology works by picking similar SKUs and implement that information to bootstrap forecasting process.

Nonetheless, there exists an array of challenges associated with Deep Learning technology. The very development of high-end AI applications is at a nascent stage; it is yet to become a fully functional engineering practice.

A larger chunk of successful AI implementation depends on the expertise and experience of the breed of data scientists involved. Handpicking a qualified data scientist in today’s world is the real ordeal. Being fluent in the nuances of deep learning imposes extra challenges. Moreover, apart from being labor intensive in terms of feature engineering and data cleaning, the entire methodology of developing neural network models all manually is difficult and downright challenging. It may even take a substantial amount of time to learn the tricks and scrounge through numerous computational resources and experiments performed by data scientists. All this makes the hunt down for skilled data scientists even more difficult.

Fortunately, DexLab Analytics is here with its top of the line data science courses in Gurgaon. The courses offered by the prominent institute are intensive, well-crafted and entirely industry-relevant. For more information on data analyst course in Delhi NCR, visit our homepage.

 
The blog has been sourced from ―
www.forbes.com/sites/nvidia/2018/11/21/how-deep-learning-solves-retail-forecasting-challenges/#6cf36740db18
 

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Amazon Launches DeepRacer, an Autonomous Machine Learning Car

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Amazon Launches DeepRacer, an Autonomous Machine Learning Car

Amazon leverages machine learning technology and develops an entirely remote-controlled autonomous car, DeepRacer. It joins the bandwagon of blockchain, processor chips and advanced data storage in the recently held global event.

Amazon is the latest tech bigwig that’s found experimenting with the genre: self-driving cars. However, there is a subtle point of distinction between Amazon and its tailing rivals and that is the former’s car is about the size of a shoebox, while the others are busy trying to replace already existing passenger cars.

Last week, Amazon Web Services launched DeepRacer implementing reinforced learning at its annual cloud computing conference in Las Vegas – it’s a one-18th scale model car that developers can drive using ML models and joins the rivalry against newly developed autonomous racing car range. This toy car is completely autonomous and they are selling it for $399.

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Developers can now experiment and learn more about reinforcement learning – it’s basically a process that uses trial and error method and trains the software to solve complicated and difficult tasks. Even customers can train it well, thanks to AWS’ reinforcement-learning models. In this way, DeepRacer can be used in real world solving difficult tasks in the easiest and cheapest manner.

“If you really want machine learning to be expansive across companies, you have to find a way to let everyday developers build machine-learning models and put them in production,” said Andy Jassy, chief executive of Amazon Web Services. “We wanted to make that easy for developers to take advantage of because that’s where all the innovation is going to happen… We said, how are they going to get hands-on experience and actually try it?”

Amazon’s DR is built on a monster truck chassis, contains a battery system, operates using Intel Atom processor and is mobile phone-monitored. The car’s AI module is constructed on AWS SageMaker and its 3D simulation environment is inspired by AWS RoboMaker.  It features a deep lens camera, which lets it maneuver through its surroundings – this too explains its weird shape.

Talking about deep lens camera, just a year ago, AWS released a cutting-edge image recognition camera, known as DeepLens. It helped a large number of developers to design a wide array of applications using image recognition and aided companies in solving challenges regarding autonomous driving. Soon, the company also marked its footsteps in the domain of self-driving cars and built this autonomous car to simulate driving and tackle issues regarding autonomous driving.

Interestingly, AWS is gearing up to introduce the world’s very first autonomous racing league – AWS DeepRacer League – in 2019. It will include 20 races and the winners will have to showcase their autonomous cars during the Championship cup.

Currently, DeepRacer is available only in the US but will soon be on sale for developers attending AWS hackathons. Surely, Amazon has big plans to take it global and for that, they are allowing you to pre-order yours on Amazon at a discounted price of $250. The original price appears to be more than $399.

DexLab Analytics is offering Deep Learning Training Courses in sync with current industry demands. Their deep learning certification in Gurgaon is fetching good marks – all thanks to an intensive knowledge-oriented curriculum, practical assistance and student-friendly approach.

 

The blog has been sourced from ― www.ft.com/content/934b73d2-f479-11e8-ae55-df4bf40f9d0d

 

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Deep Learning AI Is Not a Magic Potion but Machine Learning

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Deep Learning AI Is Not a Magic Potion but Machine Learning

Frankly, today’s AI technology is nothing less than magic. Algorithms deciphering images, videos, speeches and texts, translating languages in between, driving cars, identifying cancer, playing games have unleashed a new era of digital transformation that’s creating awe-inspiring milestones each day. It seems that AI is ravaging every part our industry verticals, and we can’t be more excited!

Apparently, AI algorithms are similar to conventional machine learning algorithms. Even the most robust systems feature more artistry than science, calls for a wide array of carefully curated data, does generalize beyond their own training area and contains several unknown glitches that even their developers have little knowledge about.

In the last half decade, Deep Learning AI renaissance has reached new heights. With groundbreaking innovations and phenomenal feats in ML technology, AI has come to hold almost a mythical significance. Quite frequently, academicians are conducting ‘intelligent systems’ conferences, analysts are etching an AI solution for every problem, media personalities are conjecturing AI’s keenness to replace human superiority – all of these instances are happening each day. Not a week passes by when a research publication or academic editorial doesn’t document another application or algorithmic achievement.

Quite obviously, the ‘future of machines’ is knocking at our doors…

The field of Machine Learning and AI is expanding. Going beyond the paradigms of human intelligence, artificial intelligence is making machines perform tasks, which were previously impossible. But, how do machines work? After all, there exists no spell that would cast its enigma and make machines perform like humans.

Interestingly, the machines learn and imbibe skills by experience and through inspirations from the human brain. Deep leaning is a fraction of machine learning wherein artificial neural networks and algorithms learn from huge volumes of data. Similarly, like how we learn from experience, the advanced algorithms follow our footsteps and perform a task repeatedly unless they succeed. Just, like us, they too believe in learning from experiences and mistakes. What’s more, the term ‘deep learning’ originates from the notion that networks have numerous deep layers and they boost learning.

At present, the world is churning out data at a phenomenal speed. A state of data explosion is not too far. Every day, a staggering – 2.6 quintillion bytes of data is being generated. And this is the fuel for deep learning. As deep learning algorithms need a humongous amount of data, the increase in the levels of data creation is one of the key reasons for which deep learning capabilities and resources have grown manifold in recent years.

In a nutshell, Deep Learning, AI and ML in conjunction are rapidly advancing. Cutting edge algorithms are making life and death decisions, but yes once again we would like to say, they are not some magic potion that casts its spell around just like that. Instead, they are advanced, well-built models that identify underlying patterns and implement those patterns to relevant data. The more experience they gather, the more productive they become.

Want to secure your career for good? Opt for a comprehensive deep learning training in Gurgaon. DexLab Analytics is a powerful online community that excels in providing deep learning training courses and more.

 

The blog has been sourced from ― www.forbes.com/sites/kalevleetaru/2018/11/14/todays-deep-learning-ai-is-machine-learning-not-magic/#142cc4276875

 

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AI in Cyber Security: Knowing the Difference between Machine Learning and Deep Learning

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AI in Cyber Security: Knowing the Difference between Machine Learning and Deep Learning

The need of the hour in business world is continuous innovation in the field of cyber security. Security vendors constantly brainstorm ideas and methods that’ll keep them ahead of cybercriminals. The gravity of the problem can be understood from a report by Sophos which mentions that almost 50% of Australian businesses were affected by ransomware attacks in 2017.

To keep functioning amidst such threats, businesses require innovative technologies, and artificial intelligence is one such tool that has become vital for cyber security.

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Artificial Intelligence

AI is a trendy term now, thanks to blockbuster Bollywood movies made on AI!

AI is an all-embracing principle that includes a number of technologies─ machine learning and deep learning being important ones among them. Basically, artificial intelligence enables machines to learn on their own from experience, modify techniques when fed with new data sets and carry out tasks that are human-like. When the principles of AI are applied to cyber security, we call it predictive security. AI helps to identify and check if files contain malware, which is carried out with the help of machine learning as well as deep learning. Although these two branches use similar AI principles, the two fields are fundamentally very different.

Moving on, let’s explore their basic differences.

Machine Learning

Machine learning is an artificial system that learns from examples and generates knowledge from past experiences. ML technology doesn’t simply memorize examples; rather it picks up laws and patterns and applies it later where relevant.

Considering today’s advanced threat landscape, conventional approaches fail to offer strong protection to a system. Malware programs are sometimes designed to make slight changes and breach traditional systems. In such situations, machine learning can be a better security option as it can detect these unknown and modified malwares too.

An important advantage of machine learning is that it keeps evolving and improving as it is used more and fed with more data. Machine learning algorithms scrutinize file elements in order to comprehend the nature of attacks, which includes simple things like file size as well as complex things like part of codes.

Deep Learning

The benefits of employing machine learning techniques in cyber security are numerous. However, it has some drawbacks too, which can be overcome with deep learning. The main limitations of ML are its inability to handle many variables at once, requirement of huge computing powers and using up a lot of space. In deep learning, unstructured data is stored in neural networks and decisions are made using predictive reasoning, which is modeled on the workings of human brain. This structure has potential to manage numerous points of information without hampering speed of the system.

Deep learning can form better idea of the big picture because it doesn’t include programs designed to solve a particular problem, rather it includes mathematical models that learn over time. A model is developed such that it can explain well what it ‘’sees’’. For this, large amount of data is used, such as trends, malicious URLs and other modes of attacks.

Cyber attackers need to be correct in their methods only once in order to breach an enterprise. On top of that, security threats are becoming more innovative each day. Hence, technologies like deep learning and machine learning need to be the founding stones of modern security systems. Understandably, these skills are also very high in demand. Artificial Intelligence certification courses are hugely popular. If this subject interests you, then don’t delay in enrolling for deep learning courses in Delhi or machine learning courses in Gurgaon from leading institute DexLab Analytics.

 
Reference: www.cso.com.au/article/648861/artificial-intelligence-vs-machine-learning-vs-deep-learning-what-difference
 

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