In this article, we will look at the development of AI and the field of deep learning. Deep learning originated in the era of vacuum tube computers. In 1958, Frank Rosenblatt of Cornell University designed the first artificial neural network. This was later named "deep learning". Rosenblatt knew that this technology surpassed the computing power at that time.

Master AI, ML and DL What you'll learn Learn what the difference is between AI, Machine Learning & Deep Learning Description Crash course for everybody that wants to understand how Artificial Intelligence and Machine Learning will change our lives. After completing this course, participants will be able to prioritise, lead and manage AI initiatives at department and/or company level. Every day we get confronted with news of how AI is revolutionising the way we live, work and play. As the topic of AI is not well understood the spread of information through vlogs, articles and blogs is highly biased so that there is confusion about what is applicable today and more importantly, what is still considered a research area. Companies spend a massive amount of time and money on chasing so-called'use cases' often with disappointing outcomes.

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"Deep Learning Is Setting Records!!" There is tremendous growth in people searching or showing interests about deep learning & AI in last few years. Every day hundreds of new articles get published on it in social media & press media. Above chart broadly explains as why search trend is ever growing for deep learning & AI. Fundamentally deep learning is a subset of Machine Learning. The reason as why it is exciting is that more data you give to deep learning usually you get more accuracy out from the model.

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Artificial intelligence (AI is intelligence displayed by machines. An example of this is machine learning. There are two categories of machine learning: supervised (learning by example) and unsupervised (self-learning). Supervised machine learning requires "labels" in the data. "Labels" are the answers that enable learning by example, such as malware.

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In the low oil price environment, oil and gas operators need to reduce costs and boost operational efficiency through the efficient and effective use of data. Companies are investing in predictive technology to become more productive – and to be ready for the inevitable increase in barrel prices. Artificial Intelligence and Machine Learning have enabled operators to augment human capabilities – to automate processes and gain previously unobtainable outcomes. With massive amounts of computational power, machines can now analyze large sets of data points and apply relationship modeling in a predictive way and in real time. Big Data technology has the potential to leverage machine learning capabilities enabling accurate and real time decision making improving overall operating efficiency and reducing unnecessary cost.