The past, present and future of deep learning

The past, present and future of deep learning

TLDR; In this blog, you’ll be learning the theoretical aspects of deep learning (DL) and how it has evolved, right from the study of the human brain to building complex algorithms. Next, you’ll be looking at a few pieces of research that have been carried by renowned deep learning folks who have then sown the sapling in the fields of DL which has now grown into a gigantic tree. Lastly, you’ll be introduced to the applications and the areas where deep learning has set a strong foothold.

TLDR; In this blog, you’ll be learning the theoretical aspects of deep learning (DL) and how it has evolved, right from the study of the human brain to building complex algorithms. Next, you’ll be looking at a few pieces of research that have been carried by renowned deep learning folks who have then sown the sapling in the fields of DL which has now grown into a gigantic tree. Lastly, you’ll be introduced to the applications and the areas where deep learning has set a strong foothold.

Deep Learning: A brief history

Over the past decade, no other technologies were important than Artificial Intelligence. Stanford’s Andrew NG called it the “New Electricity” and several tech giants, including Google, Microsoft and Apple, have changed their business strategies to become “AI-first” companies. And we can thank Deep Learning for all of that. Before getting started, let’s understand what DL is about and the reason behind its hype.

Deep learning, a subset of AI, is a computer technique to extract and transform data using multiple artificial layers of neural networks. These layers contain a set of artificial neurons which exist in a particular state. When data is sent into these layers, each layer takes input from the preceding layers and progressively refines them. The layers are then trained by algorithms that consistently reduce the errors and improve their accuracy of predictions. In this way, the network learns to perform a specific task.

DL is slow compared to traditional AI and Machine Learning (ML) algorithms yet more straightforward and powerful. Hence, these are used across different domains ranging from medicine, science, social, manufacturing, supply chain, robotics and many more based on a single innovative type of model: the neural network. If you're wondering as to when DL dates back to, let me clear you that it isn't after all NEW; it has existed since the 1940s. Let’s dig into history to see how these were evolved from time to time.

artificial-intelligence deep-learning computer-science ai future the past, present

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