What is Deep Learning?
Deep Learning can be a subset of machine learning, and it is a field based on learning and improving on its own by using computerized algorithms. Deep learning also uses a variety of computer hardware and software systems to help it achieve its goals.
Many people believe that deep learning is an excellent approach to Artificial Intelligence (AI) development. Another interesting fact is that deep learning algorithms can be trained; the more training data a system has, the better it understands.
It taught on large data sets to help them learn more about their environment. The whole idea of deep learning is to get machines to perform complex tasks that they were never designed it.
How does it work?
Deep learning requires a massive amount of data to revert accurate results. However, when processing data, artificial neural networks can classify data with the answers received from a series of binary true or false questions involving highly complex mathematical calculations.
Let’s take an example- the goal is to have a neural network to recognize photos that contain a dog. All dogs don’t look exactly alike – consider a Rottweiler and a Poodle, for instance. photos show dogs at different angles and with varying amounts of light and shadow.
So, a training set of images must be compiled, including many examples of dog faces which any person would label as “dog,” and pictures of objects that aren’t dogs labelled as “not dog.
”The images, fed into the neural network, are converted into data. These data move through the network, and various nodes assign weights to different elements. The final output layer compiles the seemingly disconnected information – furry, has a snout, has four legs, etc. – and delivers the output: dog.
Increasing of deep learning
Machine learning is said to have occurred in the 1950s when Alan Turing, a British mathematician, proposed his artificially intelligent “learning machine.” Arthur Samuel wrote the first computer learning program. Various machine learning techniques came in and out of fashion in the decades that followed.
Neural networks were ignored mainly by machine learning researchers, as they were afflicting by the ‘local minima’ problem in which weightings incorrectly appeared to give the fewest errors. However, some machine learning techniques like computer vision and facial recognition moved forward.
In 2001, a machine learning algorithm called Ad boost was developed to detect faces within an image in real-time. It filtered images through decision sets such as “does the image have a bright spot between dark patches, possibly denoting the bridge of a nose?” When the data moved further down the decision tree, the probability of selecting the right face from an image grew.
Deep Learning in Action
Deep learning is finding its way into applications of all sizes. Anyone using Facebook cannot help but notice that the social platform commonly identifies and tags your friends when you upload new photos.
Digital assistants like Siri, Cortana, Alexa, and Google Now use deep learning for natural language processing and speech recognition, and Skype translates spoken conversations in real-time.
Apps like CamFind allow users to take a picture of any object and discover what the object is using mobile visual search technology. Google is leveraging deep learning to deliver solutions.
Deep understanding is only in its infancy and will transform society in the decades to come. Self-driving cars are being tested worldwide; the complex layer of neural networks is being trained to determine objects to avoid, recognize traffic lights, and know when to adjust speed. Neural networks are becoming adept at forecasting everything from stock prices to the weather.
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