As technology evolves rapidly with fear and enthusiasm, terms such as artificial intelligence, machine learning, and deep learning can be disconcerting. We hope this simple guide will help you dispel the confusion. What is deep learning?
What is Deep Learning?
Deep Learning is a subdomain of machine learning that deals with algorithms inspired by the structure and function of the brain, called artificial neural networks. In-depth learning is a key technology behind driverless cars, which allows them to recognize a stop sign or distinguish a pedestrian from a lamppost. This is the key to tablets, hands- free speakers, televisions and voice control in consumer devices such as phones.
Most machine learning algorithms work well in data sets that contain up to a few hundred entities or columns. However, an unstructured data set, such as an image, has so many characteristics that this process becomes tedious or totally unrealizable.
How does it work?
Deep learning has evolved along with the digital era, which has resulted in an explosion of data in all its forms and in all regions of the world. These data, known simply as Big Data, come from sources such as social networks, e-commerce platforms, and online theaters, among others.
Computer programs that use deep learning follow virtually the same process as the child who learns to identify the dog. Each algorithm in the hierarchy applies a non-linear transformation to its input and uses what it has learned to create a statistical model output. The iterations continue until the output reaches an acceptable level of accuracy. The number of processing layers that the data must pass through is what has deeply inspired the label.
Deep Learning vs Machine Learning
One of the most used artificial intelligence techniques to deal with big data is machine learning, a self-adaptive algorithm that allows increasingly sophisticated analyzes and models, with new experience or data. So the difference between these two are as follows,
|Properties||Deep Learning||Machine Learning|
|Data Dependencies||It has excellent performance on small or medium Dataset||Excellent Performance only for Large Dataset|
|Execution Time||It takes from several minutes to an hour||It takes up to weeks|
|Hardware Dependencies||It can work on low-end machines||It needs powerful machines with GUI|
|Interpretability||Some algorithms are easy to interpret, some are almost impossible.||Difficult to Interpret|
Deep Learning Methods
Different methods can be used to create solid models of deep learning. These techniques include as follows,
- Learning Rate Decay
- Transfer of Learning
- Training from Scratch
Some Examples of Deep Learning
Virtual Assistants are one of the best applications of the Deep Learning Method. Whether it is Alexa, Siri, Cortana, It can use the deep learning algorithm to interact with humans and understand their language.
Similarly, Deep Learning Algorithms can automatically translate between the languages. It’s main use for the Travelers, Business People, and the government staff.
Deep Learning used in Facial Recognition not only for security purposes but also for tagging people on Facebook. But the major challenge in Deep Learning for Facial Recognition is the same person even they changed their hairstyle, image with poor quality or in bad lighting, a grown or shaved off a breed.
Personalized Shopping and Entertainment
Have you ever wondered how Netflix offers suggestions on what to look for next? Or does Amazon offer ideas on what to buy next and that these suggestions are exactly what you need but have never known before? Yes, deep learning algorithms are working.
As deeper learning algorithms gain experience, they become better. They should be extraordinary years as technology continues to mature.
Chatbots and service robots that provide customer services for many companies can intelligently and effectively answer a growing number of auditory and textual questions through in-depth learning.