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Deep learning, a subset of Machine Learning has gained strong grounds since 2012- when Alexnet achieved the very low error rate during Imagenet competition.
Deep learning models results in state-of-the-art accuracy. It is the key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamp post but its applications are used more widely in various industries. For instance in the industrial settings, the computer vision is used for automated monitoring of cycle time , carry various safety audits, and to find physical defects/ anomalies in the processes – with accuracy easily outperforming human-level accuracy.
With availability of large volume of data and super computational power, deep learning models have made real breakthrough in the areas of object detection, classification, localization and labelling.RCNN &YOLOare thecommonly used techniques in these areas.
Automatic speech recognition (ASR)is about understanding the human speech and converting it into the text. With availability of large volume ofsequence data in form of an audio clip, Recurrent Neural network are generally trained using Connectionist Temporal Classification algorithm to get text transcript as output.
Get images captioned through deep learning model is still one of the challenging areas but with availability of high computational power and huge dataset have led to improved results. Using One to Many RNN architecture model, from a single images, captions (sequence of words)are received as output.
The most common use of video analytics is anomaly detection. To find anomaly in the clips, Convolutional Long Short-Term Memory models are trained on theregularity aspects in the video clipping. Accordingly, reconstruction erroris calculated in the reconstructed videos to define normal or abnormal clips
Typically convolution neural network is used to classify the sound clips. The sound classification is being used in multitude application including security systems, predictive maintenance,& musical genres identification.
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