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Deep Learning Is So Famous, But Why?

Deep Learning is one of the approaches of Machine Learning strategy concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning speaks out as a key technology behind driverless cars, which allows them to analyze a stop sign, or to distinguish a pedestrian from a lamp post.

The leaders and experts in this field have ideas of what deep learning is and these specific and nuanced perspectives shed a lot of light on what deep learning is all about.

In this post, you will discover exactly what deep learning is by hearing from a range of mavens and leaders in the field.

Let’s dive in.

Why it matters?

In short, accurate. Deep Learning renders recognition accuracy and precision at higher levels. This in sequence helps the prospects to fulfill their expectations. And indeed it is extremely crucial for safety-critical applications such as Driverless cars. Over the last few years, Deep Learning has drastically moved to the next phase by performing human tasks like differentiating images and objects respectively.

Deep Learning has been useful mainly due to two key factors:

Deep Learning requires for ample amount of labeled data. For example, driverless car development entails millions of images and thousands of hours of video.

Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is conducive for deep learning. When coupled with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less.

How Deep Learning works?

Deep learning makes use of Neural Networks technology. This is the reason why it is referred to as Deep Neural Networks!

The term “deep” basically refers to the number of hidden layers in the neural network. Traditional neural networks only include 2-3 hidden layers, while deep networks can have as many as 150.

Deep learning models are equipped by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.

The well known Deep neural network is Convolutional Neural Network. A CNN convolves learned features with input data, and makes use of 2D convolutional layers, making this architecture well suited to processing 2D data, such as images.

CNN’s eliminate the need for manual feature extraction, so you do not need to recognize features which are used to classify images. The CNN works by obtaining features directly from images. The relevant features are not trained. They are learned while the network trains on a bunch of images. This automated feature extraction makes deep learning models extremely accurate for computer vision tasks such as object classification.

Know the Difference between Deep Learning and Machine Learning

Deep learning is a technoscientific form of machine learning. A machine learning workflow starts with appropriate features being manually extracted from images. The features are then used to build a model that classifies the objects in the image. With a deep learning workflow, the appropriate features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” where a network is given raw data and a task to accomplish, such as classification, and it learns how to do this automatically.

Another vital difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a specific level of performance when you add more examples and training data to the network.

The chief advantage of deep learning networks is that they often continue to improve as the size of your data increases.

Applications of Deep Learning

Deep learning is currently utilized in most common image recognition tools, NLP processing, and speech recognition software. These tools are initiated to appear in applications as diverse as self-driving cars and language translation services.

• Automatic Colorization of Black and White Images

Image colorization is the work of adding color to black and white photographs.

Traditionally this was done by hand with human effort because it is such a tedious task.

Deep learning can be used to use the objects and their context within the photograph to color the image, much like a human operator might address the problem.

A visual and highly impressive feat.

This ability leverages of the high quality and very large convolutional neural networks trained for ImageNet and co-opted for the problem of image colorization.

Basically, the approach involves the use of very large convolutional neural networks and supervised layers that recreate the image with the addition of color.

• Automated Machine Translation

This is an activity where given words, phrase or sentence in one language, automatically translated it into another language.

Automatic machine translation has been around for a long time, but deep learning is achieving top results in two distinct areas:

• Automatic Translation of Text.
• Automatic Translation of Images.

Text translation can be performed without any preprocessing of the sequence, allowing the algorithm to determine the dependencies between words and their mapping to a new language.

As you would expect, convolutional neural networks are used to identify images that have letters and where the letters are present in the scene. Once it is identified, they can be turned into text, translated and the image recreated with the translated text. This is usually called instant visual translation.

Final Thoughts

Deep Learning is going to pave the way for many more neoteric technologies in the forthcoming years. Hope you would have gained a few facts on Deep Learning!

Stay Tuned for more such topics..!