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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⢠Introduction to Deep Learning: Understanding the basics of deep learning, including its history, key concepts, and differences from traditional machine learning.
⢠Neural Networks: Learning about artificial neural networks, including feedforward and recurrent neural networks, and their applications in public health.
⢠Convolutional Neural Networks (CNNs): Exploring CNNs, their architecture, and their applications in image recognition and public health.
⢠Deep Learning Frameworks: Getting hands-on experience with popular deep learning frameworks, such as TensorFlow, PyTorch, and Keras, for public health applications.
⢠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.
⢠Transfer Learning and Pre-trained Models: Learning about transfer learning, fine-tuning, and using pre-trained models for public health applications.
⢠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.
⢠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.
⢠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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