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“Deep Learning”: Deep Dive Into Large Data


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Understand why we need deep learning and how it is relevant to current engineering problems. Get introduced to application areas of deep learning related to signal processing and computer vision. Kick start your deep learning and Caffe framework.

Machine learning uses data to find an equivalent model underlying a physical process. This is usually achieved by hand-crafting features and training a learning algorithm on top of it. However, in deep learning, the algorithm tries to learn the features along with the algorithm, making the entire learning process largely data dependent. Deep learning approach is the most trending machine learning approach for large sets of “unstructured data” like images, speech and videos. Consider a ‘Visual Servoing System’ where an algorithm extracts information from a visual sensor that controls the robot. An intelligent control algorithm will try to learn various attributes about its surrounding and will generate a response based on the collective information. Engineers and programmers have an approach to program the robot for all possible scenarios it may or may not encounter. However, through deep learning, the robot is likely to learn a desired behaviour. Deep learning with large data is interesting because it somehow finds the underlying pattern in a process. It is interesting to explore some fields those are immensely impacted by ‘Deep Learning’.

• Learning and its importance.
• Deep Learning and ‘Shallow learning’: What they imply?
• Cognitive Processes and Concepts of Deep Architecture
• Application: Deep Learning in field of Robotics
• Open source libraries available for Deep Learning
• Caffe framework

Presented by: Pallab Maji, Senior Engineer, Continental Automotive



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