Currently Deep Learning is the most exciting field of Machine Learning. Deep Learning algorithms are giving state of the art result in almost every domain like computer vision, natural language processing, speech analysis, robotics etc.. This deep learning course is designed to introduce deep learning to students from basic to advance. After Completing this course students will be able to design the Deep Neural Network architecture for various application.
How does computers recognize objects, people, actions, animals, places, etc from images? This is a trivial task for humans but remained one of the core problems in Computer Vision. Recent advances in representation learning using multiple layers of abstraction (deep learning) have demonstrated to be an important aspect for designing artificial systems for visual recognition. In this class we will study and implement deep learning models and learning algorithms for visual recognition. After this class you will be able to understand, design, implement, and assess the impact of deep learning techniques for a diverse range of visual recognition tasks.
Understanding foundational concepts for representation learning using neural networks
Become familiar with state-of-the-art models for tasks such as image classification, object detection, image segmentation etc.
Obtain practical experience in the implementation of visual recognition models using deep learning.
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