Deep learning, a branch of machine learning, employs algorithms to process data and provide output. Deep learning uses layers to feed-forward data from one layer to the next layer. These layers learn to automatically extract meaningful features from the data and use those features to construct the results. In essence, deep learning is a technique for learning by example. The key feature of deep learning is it eliminates the need for human intervention.

We all know about Artificial Intelligence (AI), and how it has revolutionized our daily lives. From translating languages to summarising lengthy books to self-driving automobiles-- It's all downright exceptional, Thanks to the advancement done in AI. The history of Deep Learning dates back to 1943 when Walter Pitts and Warren McCulloch created a model based on the neural networks of the human brain. Deep Learning kept progressing since then and amazingly, it still is expanding! Deep learning is the subset of Machine Learning -- in other words'Advanced Machine Learning, but that begs the question -- What is Machine Learning?

Have you ever wondered how Google translates an entire webpage into another language in a matter of seconds? Or, how your phone gallery groups photographs based on their location? All of this is a product of Deep Learning. Deep Learning is a subset of Machine Learning, which in turn is a subset of Artificial Intelligence. Artificial Intelligence is a technique that enables machines to mimic human behavior, and machine learning is a technique that achieves AI through algorithms trained with data. Finally, Deep Learning is a type of machine learning that is inspired by the structure of the human brain.

Deep learning is an advanced branch of machine learning that enables computers to solve complex problems from driving a car to successfully flying a helicopter without strictly learning from simulations. Deep learning is a combination of mathematics and Neurobiology, the artificial intelligence science aims to project the human learning process on computers so that computers could learn and improve from experience. As mentioned earlier artificial intelligence science is based on how we learn, deep learning is accomplished by imitating human brain architecture. When the child a signal is transmitted from the child's eyes, ears, and hands to the brain, that signals reach the brain and begin their journey in the nodes, after reaching the final node the brain concludes to identify this new object by its characteristics illuminating, heat, and voice. For simplicity we assumed that the child only learns the object characteristics after pain and this is not true in real life learning process is always on and each aspect of the object will be learned in the precise second of the encounter.

Yesterday, I talked about Machine Learning, and the huge impact it will have in the world in the future. Today, I'd like to talk about a similar paradigm, that often gets mixed up with it, but that is not the same thing at all.

The term "Artificial Intelligence" has been floating around for a while. We see it in sci-fi movies, "AI" game bots we play against, Google search, and, oh yeah, those robots that are some day going to take over the world. Off late, though, "Machine Learning" and "Deep Learning" have surfaced, with many asking what exactly each of these are. Artificial Intelligence is the general category, common to all three. In a diagram, Artificial Intelligence would be the bigger, encapsulating circle that contains Machine and Deep Learning.

Yesterday, I talked about Machine Learning, and the huge impact it will have in the world in the future. Today, I'd like to talk about a similar paradigm, that often gets mixed up with it, but that is not the same thing at all. First off, let me say: this topic is vast. In my article, I'll try to boil down the main facts, but be warned, you should investigate the matter on your own to learn more. Hope I can set you on the right path at least.

First, data is divided into many nodes (taking the place of neurons). This data gets transmitted to other nodes, which manipulate this data in some ways, so as to make it better usable for the end goal of the system. The most often way the data gets manipulated, is by assigning a weight to it. The weight determines how valuable that information is for the desired output. However, this is where the differences between neurons and Deep Learning come into play.