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- Level Intermediate
- Duration 3 hours
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In this 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Deep Learning, Convolutional Neural Networks (CNNs) and Residual Neural Networks. - Import Key libraries, dataset and visualize images. - Perform data augmentation to increase the size of the dataset and improve model generalization capability. - Build a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend. - Compile and fit Deep Learning model to training data. - Assess the performance of trained CNN and ensure its generalization using various KPIs. - Improve network performance using regularization techniques such as dropout.Auto Summary
Embark on an enlightening journey into the world of Emotion AI with our course on Facial Key-points Detection, expertly designed for those keen on elevating their understanding of Data Science and AI. Guided by the renowned platform Coursera, this intermediate-level, project-based course spans an efficient 1-hour duration, making it perfect for busy professionals and learners aiming to quickly grasp advanced concepts. Dive deep into the theory and practical applications of Deep Learning, Convolutional Neural Networks (CNNs), and Residual Neural Networks. This course emphasizes the essential techniques and intuition needed to effectively detect facial key-points, a critical component in the evolving field of Emotion AI. Available for free, this course provides an accessible yet comprehensive learning experience, tailored for individuals with a foundational knowledge in data science and AI who are eager to expand their expertise. Join us and transform your understanding of AI-driven facial recognition technologies in just one hour!