Certificate in Deep Learning for Public Health Essentials
-- ViewingNowThe Certificate in Deep Learning for Public Health Essentials is a comprehensive course that empowers learners with the essential skills needed to apply deep learning techniques in public health. This program is vital in today's world, where data-driven decision-making is critical in healthcare.
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โข Introduction to Deep Learning: Understanding the basics of deep learning, including its history, key concepts, and differences from traditional machine learning.
โข Neural Networks: Learning about artificial neural networks, including feedforward and recurrent neural networks, and their applications in public health.
โข Convolutional Neural Networks (CNNs): Exploring CNNs, their architecture, and their applications in image recognition and public health.
โข Deep Learning Frameworks: Getting hands-on experience with popular deep learning frameworks, such as TensorFlow, PyTorch, and Keras, for public health applications.
โข Natural Language Processing (NLP) with Deep Learning: Understanding NLP and its applications in public health, and learning how to implement NLP techniques using deep learning.
โข Transfer Learning and Pre-trained Models: Learning about transfer learning, fine-tuning, and using pre-trained models for public health applications.
โข Evaluation Metrics for Deep Learning Models: Understanding the key evaluation metrics for deep learning models, including accuracy, precision, recall, and F1 score, and how to use them to compare models.
โข Data Augmentation Techniques for Deep Learning: Learning about data augmentation techniques, such as rotation, flipping, and zooming, and how to use them to improve model performance and generalization.
โข Ethics in Deep Learning for Public Health: Exploring the ethical considerations of using deep learning in public health, including data privacy, bias, and fairness.
Note: The above list of units is not exhaustive and may vary depending on the specific course content and objectives.
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