augmented intelligence certification, machine learning and deep learning may leave you perplexed. I hope that this simple guide will help sort out the confusion around deep learning and that the 8 practical examples will help to clarify the actual use of deep learning technology today.</p> ;<div id="attachment_3042" class="wp-caption alignnone"> ; <div class="article-body-image"> ; <progressive-image class="size-large wp-image-3042" src="https://blogs-images.forbes.com/bernardmarr/files/2018/10/AdobeStock_179912599-1-1200×797.jpg" alt="" data-height="797" data-width="1200"></progressive-image> ; </div> ; <div article-image-caption=""> ; <div class="caption-container" ng-class="caption_state"> ; <p class="wp-caption-text">Adobe Stock<small class="article-photo-credit">Adobe Stock</small></p> ; </div> ; </div> ;</div> ;<p><strong>What is deep learning?</strong></p> ;<p>The field of augmented intelligence certification is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.</p> ;<p>The amount of data we generate every day is staggering—currently estimated at<u><a href="https://web-assets.domo.com/blog/wp-content/uploads/2017/07/17_domo_data-never-sleeps-5-01.png" target="_blank" rel="nofollow noopener noreferrer" data-ga-track="ExternalLink:https://web-assets.domo.com/blog/wp-content/uploads/2017/07/17_domo_data-never-sleeps-5-01.png"> 2.6 quintillion bytes</a></u>—and it’s the resource that makes deep learning possible. Since deep-learning algorithms require a ton of data to learn from, this increase in data creation is one reason that deep learning capabilities have grown in recent years. In addition to more data creation, deep learning algorithms benefit from the stronger computing power that’s available today as well as the proliferation of augmented intelligence certification (AI) as a Service. AI as a Service has given smaller organizations access to augmented intelligence certification technology and specifically the AI algorithms required for deep learning without a large initial investment.</p> ;<p> ; </p> ;<p>Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The more deep learning algorithms learn, the better they perform.</p> ;<p><strong>8 practical examples of deep learning</strong></p> ;<p>Now that we’re in a time when machines can learn to solve complex problems without human intervention, what exactly are the problems they are tackling? Here are just a few of the tasks that deep learning supports today and the list will just continue to grow as the algorithms continue to learn via the infusion of data.</p> ;<div class="vestpocket" vest-pocket=""></div> ;<ol> ; <li> Virtual assistants</li> ;</ol> ;<p>Whether it’s Alexa or Siri or Cortana, the virtual assistants of online service providers use deep learning to help understand your speech and the language humans use when they interact with them.</p> ;<ol start="2"> ; <li> Translations</li> ;</ol> ;<p>In a similar way, deep learning algorithms can automatically translate between languages. This can be powerful for travelers, business people and those in government.</p> ;<ol start="3"> ; <li> Vision for driverless delivery trucks, drones and autonomous cars</li> ;</ol> ;<p>The way an autonomous vehicle understands the realities of the road and how to respond to them whether it’s a stop sign, a ball in the street or another vehicle is through deep learning algorithms. The more data the algorithms receive, the better they are able to act human-like in their information processing—knowing a stop sign covered with snow is still a stop sign.</p> ;<ol start="4"> ; <li> Chatbots and service bots</li> ;</ol> ;<p>Chatbots and service bots that provide customer service for a lot of companies are able to respond in an intelligent and helpful way to an increasing amount of auditory and text questions thanks to deep learning.</p> ;<ol start="5"> ; <li> Image colorization</li> ;</ol> ;<p>Transforming black-and-white images into color was formerly a task done meticulously by human hand. Today, deep learning algorithms are able to use the context and objects in the images to color them to basically recreate the black-and-white image in color. The results are impressive and accurate.</p> ;<ol start="6"> ; <li> Facial recognition</li> ;</ol> ;<p>Deep learning is being used for facial recognition not only for security purposes but for tagging people on Facebook posts and we might be able to<u><a href="https://www.technologyreview.com/s/603494/10-breakthrough-technologies-2017-paying-with-your-face/" target="_blank" rel="nofollow noopener noreferrer" data-ga-track="ExternalLink:https://www.technologyreview.com/s/603494/10-breakthrough-technologies-2017-paying-with-your-face/"> pay for items in a store just by using our faces</a></u> in the near future. The challenges for deep-learning algorithms for facial recognition is knowing it’s the same person even when they have changed hairstyles, grown or shaved off a beard or if the image taken is poor due to bad lighting or an obstruction.</p> ;<ol start="7"> ; <li> Medicine and pharmaceuticals</li> ;</ol> ;<p>From disease and tumor diagnoses to personalized medicines created specifically for an individual’s genome, deep learning in the medical field has the attention of many of the largest pharmaceutical and medical companies.</p> ;<ol start="8"> ; <li> Personalized shopping and entertainment</li> ;</ol> ;<p>Ever wonder how Netflix comes up with suggestions for what you should watch next? Or where Amazon comes up with ideas for what you should buy next and those suggestions are exactly what you need but just never knew it before? Yep, it’s deep-learning algorithms at work.</p> ;<p>The more experience deep-learning algorithms get, the better they become. It should be an extraordinary few years as the technology continues to mature.</p>”>
There’s a lot of discussion these days about all the choices of equipment mastering to do matters human beings at this time do in our factories, warehouses, workplaces and properties. Even though the engineering is evolving—quickly—along with fears and excitement, conditions this sort of as augmented intelligence certification, machine learning and deep learning may leave you perplexed. I hope that this simple guidebook will help form out the confusion close to deep learning and that the 8 functional examples will support to clarify the real use of deep learning technological know-how these days.
What is deep learning?
The subject of augmented intelligence certification is essentially when equipment can do duties that usually have to have human intelligence. It encompasses machine learning, where machines can find out by practical experience and purchase skills devoid of human involvement. Deep learning is a subset of machine learning exactly where artificial neural networks, algorithms encouraged by the human brain, understand from large amounts of facts. Equally to how we find out from expertise, the deep learning algorithm would perform a endeavor consistently, every single time tweaking it a little to enhance the final result. We refer to ‘deep learning’ simply because the neural networks have several (deep) levels that enable studying. Just about any challenge that necessitates “thought” to figure out is a trouble deep learning can discover to solve.
The volume of data we create just about every day is staggering—currently approximated at 2.6 quintillion bytes—and it’s the resource that would make deep learning possible. Due to the fact deep-learning algorithms call for a ton of facts to learn from, this boost in info creation is one particular reason that deep learning capabilities have grown in modern a long time. In addition to far more info development, deep learning algorithms profit from the more powerful computing electricity that’s available nowadays as properly as the proliferation of augmented intelligence certification (AI) as a Assistance. AI as a Provider has specified smaller sized corporations entry to augmented intelligence certification technologies and exclusively the AI algorithms demanded for deep learning without the need of a big original expense.
Deep learning lets machines to address advanced difficulties even when utilizing a info established that is pretty assorted, unstructured and inter-connected. The additional deep learning algorithms learn, the improved they accomplish.
8 practical examples of deep learning
Now that we’re in a time when devices can master to solve sophisticated issues without the need of human intervention, what exactly are the difficulties they are tackling? Right here are just a couple of the duties that deep learning supports currently and the record will just keep on to expand as the algorithms continue to find out by way of the infusion of info.
- Virtual assistants
No matter if it is Alexa or Siri or Cortana, the virtual assistants of on the web support suppliers use deep learning to aid recognize your speech and the language human beings use when they interact with them.
In a comparable way, deep learning algorithms can mechanically translate between languages. This can be highly effective for vacationers, business people and all those in governing administration.
- Vision for driverless delivery trucks, drones and autonomous vehicles
The way an autonomous vehicle understands the realities of the street and how to respond to them irrespective of whether it’s a end sign, a ball in the road or a different automobile is by means of deep learning algorithms. The a lot more details the algorithms receive, the improved they are capable to act human-like in their details processing—knowing a cease sign protected with snow is still a stop signal.
- Chatbots and assistance bots
Chatbots and services bots that give buyer services for a whole lot of corporations are capable to reply in an smart and valuable way to an raising volume of auditory and text issues thanks to deep learning.
- Image colorization
Reworking black-and-white pictures into shade was formerly a task accomplished meticulously by human hand. These days, deep learning algorithms are ready to use the context and objects in the images to shade them to in essence recreate the black-and-white graphic in coloration. The effects are extraordinary and precise.
- Facial recognition
Deep learning is remaining applied for facial recognition not only for security purposes but for tagging folks on Fb posts and we could be able to spend for products in a retail store just by applying our faces in the around foreseeable future. The difficulties for deep-finding out algorithms for facial recognition is figuring out it is the exact human being even when they have transformed hairstyles, grown or shaved off a beard or if the picture taken is very poor thanks to poor lighting or an obstruction.
- Medicine and prescription drugs
From sickness and tumor diagnoses to individualized medicines established exclusively for an individual’s genome, deep learning in the health-related field has the attention of several of the most significant pharmaceutical and healthcare corporations.
- Personalized shopping and entertainment
At any time ponder how Netflix will come up with strategies for what you should observe following?…