Course Outline

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


  • Experience in the medical industry
  • No programming experience is required
  14 Hours


Related Courses

Introduction to Stable Diffusion for Text-to-Image Generation

  21 hours

Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation

  21 hours


  7 hours

TensorFlow Lite for Embedded Linux

  21 hours

TensorFlow Lite for Android

  21 hours

Tensorflow Lite for Microcontrollers

  21 hours

TensorFlow Lite for iOS

  21 hours

Deep Learning Neural Networks with Chainer

  14 hours

Distributed Deep Learning with Horovod

  7 hours

Accelerating Deep Learning with FPGA and OpenVINO

  35 hours

Building Deep Learning Models with Apache MXNet

  21 hours

Deep Learning with Keras

  21 hours

Deep Learning for Self Driving Cars

  21 hours

Advanced Deep Learning with Keras and Python

  14 hours

Deep Learning for Vision with Caffe

  21 hours