Deep Learning Fundamentals for Healthcare
Deep Learning Fundamentals for Healthcare
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 26m | 1.1 GB
Instructor: Wuraola Oyewusi
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 26m | 1.1 GB
Instructor: Wuraola Oyewusi
Explore the exciting world of deep learning applications in healthcare through this in-depth course. Learn how to classify and detect abnormalities in X-ray images through convolutional neural networks (CNNs), fine-tuning pre-trained models, and leveraging zero-shot learning. Understand the basics of deep learning, including neural networks, model training, and hyperparameter tuning tailored specifically to healthcare. Engage in hands-on activities where you'll preprocess data, build models with Python, and utilize frameworks like TensorFlow and PyTorch. Develop practical skills in object detection and segmentation to diagnose and detect medical conditions effectively. Gain insights into ethical considerations and data limitations pertinent to applying AI in a medical context. By the end of this course, you will be equipped to apply deep learning techniques to real-world healthcare challenges, improving diagnostic accuracy and patient outcomes.
Learning objectives
- Understand the intricacies of deep learning and how to implement it in a healthcare context.
- Build confidence in core competencies like hyperparameter tuning and fine-tuning an existing model on a new task.
- Navigate the health-focused pretrained model ecosystem and learn how to iterate to find the one that works well for your use case.
Deep Learning Fundamentals for Healthcare
