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            課程目錄:Deep Learning for Medicine培訓
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            Introduction to Deep Learning

            Impact on the Medical Industry
            Successes and Failures in Deep Learning in Various Industries
            Understanding Deep Learning

            Artificial Intelligence and Machine Learning
            Basic Concepts of Deep Learning
            Applications for Deep Learning
            The role of Big Data in Deep Learning
            Overview of Common Deep Learning Techniques

            Neural Networks
            Natural Language Processing
            Image Recognition
            Speech Recognition
            Sentiment Analysis
            Applying Deep Learning Techniques to Issues in Medicine

            Exploring the Opportunities for Improvement in the Medical Field
            Examining the Applicability of Deep Learning Techniques to the Cited Issues
            Exploring Deep Learning Case Studies for Medicine

            DeepVentricle Algorithm for Ventricular Segmentation in Cardiac MR by Arterys
            Skin Cancer Diagnosis Algorithm by Stanford
            Heart Failure Prediction Algorithm by Sutter Health and the Georgia Institute of Technology
            Radiology Scans Diagnoses Across All Modalities by Behold.AI
            Clinical Decision Support Technologies by Enlitic
            Personalized Medicine and Therapies by Deep Genomics
            Decoding Cancer with Freenome
            Detection of Diabetic Retinopathy by Google
            Chatbot for Prevention and Diagnosis of Disease by Babylon Health
            Limitations of Deep Learning

            Ethical Implications and Data Privacy Concerns in Deep Learning

            Creating New Business Models Based on Deep Learning-Enabled Platforms and Ecosystems

            Bringing it All Together

            Choosing Deep Learning Solutions that Fit Your Needs
            Strategies for Adoption of Deep Learning Technologies
            Team Communication and Managerial Buy-In

            Conversations with Managers and Leaders
            Conversations with Engineers and Data Scientists
            Summary and Conclusion

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