Deep learing is the dark art of this agetask that a human being can. At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.Much alike the nurons in human brain they compute batch tasks in parallel SIMD. Computer programs that use deep learning go through much the same process. Each algorithm in the hierarchy applies a nonlinear transformation on its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep..We are a port from the research group associated with just deep learing at radii
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Machine enabled reasoning has been there since years Internet of Things (IoT). 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. Deep learning requires large amounts of labeled data. Deep learning requires substantial computing power. Due to the availablity of both resources in abundance, nowadays this technique is in use.
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