Deep Learning Summit 2018

Why Deep Learning Summit? Aegis School of Data Science believes any conference outcome is based on its learning and experience for the delegates. There is already vast content accessible in the open world and we believe there is a need for the event where delegates can build their own particular application with the assistance of practitioners in the business. Deep Learning Summit concentrates no discussion no attempt to sell something, no point of view about a subject, however, more on helping the data aficionados who will create Artificial Intelligence application using Deep Learning. Workshop based model to ensure productive outcome from various development tracks. What is Deep Learning? Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is the study of artificial neural networks that contain more than one hidden layer. ������It is part of a more extensive group of machine learning techniques based learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of a particular shape, etc. Some representations are better than others at simplifying the learning task for example face recognition or facial expression recognition. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research around there endeavors to improve representations and make models learn these representations from large-scale unlabeled data. A portion of the representations are inspired by the advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. 7 September - Fundamentals 10:00 AM to 1:00 PM ������| Fundamental Deep Learning 2:00 PM to 5:00 PM | Data conversion from unstructured to structure 8 September - AI application Development Choice of any one track Track # 1 8:00 AM to 4:00 PM | Deep Learning with Images Track # 2 10:00 AM to 5:00 PM | Deep Learning for Natural Language Processing Track # 3 10:00 AM to 5:00 PM | Video Analysis with Deep Learning Track # 4 10:00 AM to 5:00 PM | ������Building a recommendation system