MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 15 lectures (1h 51m) | Size: 2.71 GB
Master Deep Machine Learning via AlexNet, ResNet, Inception, RNNs, LSTM, GANs using Keras, Pytorch, Qiskit, & TensorFlow
What you’ll learn:
The students shall learn about Artificial Intelligence and what is refueling it. How deep learning is making use of Big Data computing to transform the AI healthcare landscape. They would further be exposed to…
Requirements
Basic familiarity with and programming and probability distributions, mean, standard deviation, etc would be helpful but not required
Description
AI is an enabler in transforming healthcare delivery in terms of treatment modalities and their outcomes, electronic health records-based prediction, diagnosis and prognosis and precision medicine. This course will introduce you to the cutting edge advances in AI concerning healthcare by exploiting deep learning architectures.
The course aims to provide students from diverse backgrounds with both conceptual understanding and technical grounding of leading research on AI in healthcare. Highlighted topics to be covered in this course are listed below;
1. AI functionality and what is refueling AI in healthcare.
2. Deep Learning Convolutional Networks for AI Healthcare.
3. Role and Management of Big Data Computing in AI Healthcare.
4. How Hadoop and Python is cultivating AI based wellbeing.
5. AI based solutions for Neurological Diseases using Deep Learning.
6. AI for Brain Computer Interfacing and Neuromodulation.
7. AI algorithms for diagnosis, prognosis and treatment plans for Tumors.
8. How to model an AI problem in Healthcare.
9. How to create, preprocess and augment a data set for AI based Healthcare.
10. How to use transfer learning in multiclass classification healthcare problems.
11. Optimizers to be used in Deep learning Healthcare Problems.
12. Leading Convolutional Neural Networks (ALEXNET & INCEPTION) and validation indices.
12. Recurrent Neural Networks extending to Long Short Term Memory.
13. An understanding of Green AI.
14. Implementations of Neural Networks in Keras and Pytorch.
15. Introduction to Quantum Machine Learning.
16. Algorithms related to Quantum Machine Learning in TensorFlow Quantum and Qiskit.
Who this course is for
Undergraduate final year students, MS, PhD and Postdoctoral students
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