Deep learning, also called Deep Structured Learning, is a machine learning method that enables computers to emulate the human brain to accomplish classification tasks. It's an essential component in data science, which includes predictive modeling and statistics. In particular, this can be beneficial to data scientists who collect, analyze, and interpret a large amount of data because it helps make these tasks faster and easier. When applying deep learning algorithms, artificial neural networks play a significant role; an artificial neural network mimics biology, particularly the human brain's behavioral patterns. In combination, deep learning and neural network methods enable computers to learn things by example, similar to the human brain.

Since the last decade or so, the developments in information technology have been propelled by advancements in areas of Artificial intelligence and Machine learning. Recently, there is a healthy debate going on regarding potential advantages and disadvantages of same between two powerhouses -- Elon Musk of Tesla and Mark Zuckerberg. While the media is jumping on the bandwagon, it is important to understand some basic concepts of AI, ML and Deep Learning to get a better sense of What they do and How they can be useful. Refer to the picture below to get a better sense of co-relation between AI, ML and Deep Learning and how do Artificial Neural Networks work. How does Deep Learning work?

In this blog, I will try to explain what is deep learning and how it relates to machine learning. Even though there's a lot of knowledge about the subject online, I could not point to one source that was both exhaustive and concise. Before I begin explaining what exactly is deep learning, I'll first explain a little about artificial intelligence, machine learning, neural networks and the relationship between these topics Artificial Intelligence (AI) is the scientific branch that emphasizes the development of creating machines that can operate similarly to humans. Type 1, the simplest type, it can decide without memory and the use of prior experience. This type of intelligence is reactive, meaning it can only react to a current situation.

Deep learning is a vast field that's generating massive interest these days. It's popularly used in research but has slowly gained market penetration in the industry in the last few years. But what essentially is deep learning? Deep learning refers to neural networks with lots of layers. It's still quite a buzzword, but the technology behind it is real and quite sophisticated.

Tensors and new machine learning tools such as TensorFlow are hot topics these days, especially among people looking for ways to dive into deep learning. Turns out, when you look past all the buzz, there's really some fundamentally powerful, useful and usable methods that take advantage of what tensors have to offer, and not just for deep learning situations. If computing can be said to have traditions, then numerical computing using linear algebra is one of the most venerable. Packages like LINPACK and the later LAPACK, are now very old, but are still going strong. At its core, linear algebra consists of fairly simple and very regular operations involving repeated multiplication and addition operations on one- and two-dimensional arrays of numbers (often called vectors and matrices in this context) and it is tremendously general in the sense that many problems can be solved or approximated by linear methods. The absolutely fundamental operation of linear algebra as implemented on computers is the dot product of two vectors.

The Deep Learning 101 series is a companion piece to a talk given as part of the Department of Biomedical Informatics @ Harvard Medical School'Open Insights' series. Each post in this series is a collection of explanations, references and pointers meant to help someone new to the field quickly bootstrap their knowledge of key events, people, and terms in deep learning. In the same way that neural nets use a distributed representation to process data, reference materials for deep learning are scattered across the far flung corners of the internet and embedded in the dark ether of social media. The hope is that coalescing at least some of these materials into a central location will make it easier for new comers to start their own walk over this knowledge graph. This collection is intentionally peppered with trivia and articles from the popular press that are relevant to deep learning to keep things interesting and to provide context.

Tensors and new machine learning tools such as TensorFlow are hot topics these days, especially among people looking for ways to dive into deep learning. Turns out, when you look past all the buzz, there's really some fundamentally powerful, useful and usable methods that take advantage of what tensors have to offer, and not just for deep learning situations. If computing can be said to have traditions, then numerical computing using linear algebra is one of the most venerable. Packages like LINPACK and the later LAPACK, are now very old, but are still going strong. At its core, linear algebra consists of fairly simple and very regular operations involving repeated multiplication and addition operations on one- and two-dimensional arrays of numbers (often called vectors and matrices in this context) and it is tremendously general in the sense that many problems can be solved or approximated by linear methods. The absolutely fundamental operation of linear algebra as implemented on computers is the dot product of two vectors.

In this AI Podcast, Host Michael Copeland speaks with NVIDIA's Will Ramey about the history behind today's AI boom and the key concepts you need to know to get your head around a technology that's reshaping the world.

Researchers have tried for decades to create computers capable of learning. Recently, using the human brain as a model, they have had some success. Complicated algorithms have been developed, allowing computers to learn on a limited scale. Deep Learning (DL) is the name used for the process of computers "learning" appropriate responses as they interact with their users, or seek patterns in Big Data. This Big Data "pattern seeking aspect" has the potential to replace Data Scientists as Big Data pattern seekers.

Raw data in its unprocessed state does not offer much value, but with the right analytics techniques can offer rich insights that can aid various aspects of life such as making business decisions, political campaigns, and advancing medical science. As shown in Figure 1, the analytics cycle can be broadly classified into four categories or phases: descriptive, diagnostic, predictive and prescriptive. Machine Learning is an approach to data analysis that automates analytical model building and is used in all four types of analytics. The relevance and the growing use of analytics using machine learning can be demonstrated by its widespread use in the 2016 US presidential election campaign. Unprecedented growth in the availability of useful information coupled with advancements in technology are making it attractive to use analytics to build and run a better campaign.