English | Size: 4.05 GB
Genre: eLearning
What you’ll learn
Basics of AI and fundamentals of deep learning in AI using Python (Keras and TensorFlow)
Deep Machine Learning codes for training and testing in Python for different applications
How to use Data Augmentation and Transfer Learning Techniques in Deep Learning using Keras and TensorFlow
TensorFlow Quantum for training and testing of Quantum Neural Networks (Python)
AI is an enabler in transforming diverse realms by exploiting deep learning architectures.
The course aims to expose students to cutting-edge algorithms, techniques, and codes related to AI and particularly the deep learning routines. This course encompasses multidimensional implementations on the themes listed below;
1. Deep Learning: A subset of Artificial Intelligence
2. Big Data is Fueling AI.
3. How to model a problem in AI using datasets in Python (Keras & TensorFlow Libraries).
4. Data Augmentation in Deep Learning Networks.
5. How to use Transfer Learning in Deep Learning Networks.
6. How to use transfer learning in multiclass classification healthcare problems.
6. Backward Propagation and Optimization of hyper- parameters in AI.
7. Leading Convolutional Neural Networks (ALEXNET & INCEPTION) and validation indices.
8. Recurrent Neural Networks extending to Long Short Term Memory.
9. An understanding of Green AI.
10. Implementations of Neural Networks in Keras and Pytorch and introduction to Quantum Machine Learning.
11. Algorithms related to Quantum Machine Learning in TensorFlow Quantum and Qiskit.
12. AI based solutions for Neurological Diseases using Deep Learning.
Who this course is for:
Beginner students curious about artificial intelligence and deep learning in python
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