Deep learning is a branch of machine learning that uses multi-layered networks of computational nodes called neurons, creating an artificial neural network that mimics the way human brains process information.
Deep learning is a popular technique for tackling tasks such as computer vision, speech recognition, drug design, cancer diagnostics, automated translations, chatbots and self-driving cars.
A branch of machine learning that uses multi-layered neural networks, capable of producing better results than previous ML models or even human expert performance.
Deep learning can enable you to train better models to give you new insights, improve productivity or improve productivity by automating some tasks.
Deep learning isn’t needed for every data problem. It’s also harder to explain how models arrive at their solutions, increasing the risk of unwanted biased results.
Deep learning is used in systems like image classification, document translation and autonomous vehicles.
What is it?
Deep learning is a subset of machine learning that uses multi-layered neural networks, capable of producing better results than previous models or even human expert performance. It can identify patterns in the data and apply those patterns to make predictions.
It is widely used today, and is delivering innovation in areas such Tesla’s self-driving car, Google's translation and image search and game engines such as AlphaGo (which beat an expert human at the game of Go).
Deep learning has grown in interest as access to compute power via the cloud has allowed processing and learning from larger volumes of data. Deep learning can solve more complex tasks than traditional methods of data science and get higher degrees of accuracy.
What’s in for you?
Deep learning is enabling enterprises to offer innovative products and services — from autonomous vehicles to personalized news feeds. It can also improve staff productivity, by automating some tasks — for instance, some customer service queries — enabling staff to take on more value-added work.
Deep learning can reduce operational costs, improve your bottom line, increase your workforce productivity and enable you to make better decisions faster.
What are the trade offs?
Not every data problem is suited to deep learning. In many cases, it will be overkill; in others, better tools are available.
Deep learning also produces results that are difficult to explain. If the data sets you’re using for the computation have errors or worse, bias, the results you get will be flawed.
Many of today’s best deep learning models are trained and sometimes executed on custom hardware, which can add to the operational costs.
Deep learning models typically take longer to train than other machine learning tools and the amount of data needed to train them is higher. Given their size, it’s also costly and time consuming to move models around — say from testing environments to production.
How is it being used?
Deep learning is a fast moving space, so the number of use cases is growing all the time. That said, many of the advances are coming from bleeding edge research: it would be highly unusual to see enterprise use of deep learning models that hadn’t been proven in research.
Deep meaning models are idea for image and video classification, as well as natural language processing, where computer systems try to interpret human language — both verbal and written. It has been used to develop intelligent agents for games (such as Chess, Go, poker and others), as well as for speech recognition, like Siri and other virtual assistants.
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