Free download скачать Generative Ai In Healthcare - Practical & Hands-On Learning
Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.40 GB | Duration: 2h 44m
Revolutionize Healthcare with Generative AI: Practical Projects and Real-world Applications

What you'll learn
Understand the fundamentals of Generative AI and its applications in healthcare.
Implement hands-on projects to create AI models tailored for medical imaging and diagnostics.
Analyze real-world healthcare datasets to generate AI-driven insights and solutions.
Apply ethical considerations and best practices when using AI technologies in healthcare environments.
Requirements
Basic understanding of Artificial Intelligence and Machine Learning concepts (helpful but not required).
Familiarity with Python programming for hands-on projects.
Access to a computer with an internet connection to run AI tools and software.
No prior healthcare experience needed-all necessary concepts will be explained during the course.
Description
Unlock the transformative potential of Generative AI in the healthcare industry with this comprehensive, hands-on course. Designed for healthcare professionals, AI enthusiasts, data scientists, and tech experts, this course provides a deep dive into the rapidly growing intersection of AI and healthcare. You'll gain both a strong theoretical foundation and practical skills to apply Generative AI models to real-world healthcare challenges.Throughout the course, you will explore various applications of Generative AI in healthcare, from personalized treatment recommendations to medical imaging analysis. You'll work on hands-on projects, implement cutting-edge AI models like Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), and understand how these models can be used to synthesize medical data.You'll also learn about critical topics like data privacy, ethical considerations, and regulatory compliance in deploying AI models in healthcare environments. By the end of the course, you'll be well-prepared to build, implement, and deploy Generative AI solutions that can significantly impact patient care and medical research.What You Will Learn:Overview and applications of Generative AI in healthcare.Implementation of advanced deep generative models such as VAEs and GANs.Personalized treatment recommendation, disease prediction, and medical imaging analysis using Generative AI.Ethical considerations and privacy-preserving techniques in healthcare AI.Advanced topics like transfer learning, adversarial attacks, and interpretability in AI models.Practical skills to deploy Generative AI models in healthcare, including on cloud platforms.Course Highlights:Hands-on Learning: Real-world projects and demos throughout the course, including AI model implementations and medical image analysis.Case Studies & Research: Explore relevant case studies, research papers, and the latest advancements in the use of Generative AI in healthcare.Ethics & Compliance: Understand the critical ethical and privacy concerns when handling healthcare data with AI technologies.Deployment Ready: Learn how to successfully deploy and monitor AI models in healthcare settings, meeting regulatory requirements.Whether you're looking to enhance patient care, work with medical data, or stay at the forefront of AI innovation in healthcare, this course will provide you with the essential tools and knowledge to succeed.
Overview
Section 1: Introduction to Generative AI in Health Care
Lecture 1 Overview of generative AI
Lecture 2 Applications of generative AI in health care
Lecture 3 Challenges and opportunities
Lecture 4 Hands-on: Setting up environment
Lecture 5 Demo: Setting up environment
Section 2: Understanding Deep Generative Models
Lecture 6 Introduction to deep generative models
Lecture 7 Types of deep generative models
Lecture 8 Comparison of different models
Lecture 9 Hands-on: Implementing a variational autoencoder
Lecture 10 Demo: Implementing a variational autoencoder
Lecture 11 Hands-on: Implementing a generative adversarial network
Lecture 12 Demo: Implementing a generative adversarial network
Section 3: Applications of Generative AI in Health care
Lecture 13 Personalized treatment recommendation
Lecture 14 Disease prediction and prevention
Lecture 15 Medical imaging analysis
Lecture 16 Hands-on: Building a Simple Medical Imaging Analysis Model
Section 4: Generative AI for Medical Image Analysis
Lecture 17 Importance of medical image analysis in health care
Lecture 18 Generative models for medical image synthesis
Lecture 19 Hands-on: Generating medical images using GANs
Lecture 20 Demo: Generating medical images using GANs
Section 5: Patient Data Privacy and Ethical Considerations
Lecture 21 Privacy concerns in health care data
Lecture 22 Ethical considerations in generative AI
Lecture 23 Regulations and compliance
Lecture 24 Hands-on: Implementing privacy-preserving techniques
Lecture 25 Demo: Implementing privacy-preserving techniques
Section 6: Ethical considerations in using Generative AI in Health care
Lecture 26 Data privacy and security concerns
Lecture 27 Bias and fairness in AI algorithms
Lecture 28 Regulatory challenges
Section 7: Advanced Topics in Generative AI for Health Care
Lecture 29 Transfer learning in generative models
Lecture 30 Adversarial attacks and defenses
Lecture 31 Interpretability and explainability
Lecture 32 Hands-on: Transfer learning for medical data
Section 8: Deploying Generative AI Models in Health Care
Lecture 33 Challenges of deploying AI models in health care
Lecture 34 Regulatory requirements for deployment
Lecture 35 Monitoring and updating models
Lecture 36 Hands-on: Deploying a generative model on cloud platform
Tech professionals seeking to explore AI-driven solutions in the medical field.,Data scientists and AI enthusiasts looking to specialize in healthcare applications of Generative AI.,Students and researchers curious about the intersection of AI and healthcare and eager to gain hands-on experience with real-world projects.,Healthcare professionals interested in leveraging AI to improve patient care and diagnostics.
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