get a step into deep learning
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driver-less cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images.
While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful:
- Deep learning requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video.
- Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. When combined with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less.
Arivazhagan on 21st Nov 2017, 10:13 AMI there any difference between MI and Deep Learning ?
priyadharshini on 21st Nov 2017, 10:16 AM
Yeah.. That's a good question. Deep learning technically is machine learning, but while a standard machine learning model would need to be told how it should make an accurate prediction (by feeding it more data), a deep learning model is able to learn that through it’s own computing… essentially, it learns it all with its own “brain”. It’s similar to how a human would perceive something, think about it, and then draw a conclusion. To achieve that, deep learning uses a layered structure of algorithms called an artificial neural network. It’s design is inspired by the biological neural network that the human brain uses.
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