Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Is a key technology behind driverless 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. It’s getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
A computer model learns to perform classification tasks directly from images, text, or sound. Those 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.
While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful:
- DL requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video.
- DL requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for DL. When combined with clusters or cloud computing, this enables development teams to reduce training time for a DL network from weeks to hours or less.
- Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach
- DL with Python by Francois Chollet
- Neural Networks and Deep Learning: A Textbook by Charu C. Aggarwal
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