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Berkeley Global
This is a foundational Artificial Intelligence course to help you understand the deep learning frameworks that build upon the underpinning neural network architecture. Artificial Intelligence is pervasive across all domains and can be used for meaningful applications in multiple fields, including cancer detection using MRI scans, autonomous vehicles, speech recognition, weather forecasting and more. You will gain an understanding of versatile AI algorithms such as CNN, RNN, and implement them using frameworks such as keras, pytorch and more.
Prerequisites:
- An introductory statistics course such as Introduction to Statistics STAT X10, or equivalent
- An introductory course on Data Science with the Python programming language such as Introduction to Data Science COMPSCI X415.2, or equivalent
- An introductory course in Machine Learning with the Python programming language such as Introduction to Machine Learning Using Python COMPSCI X433.6, or equivalent.
Learner Outcomes
After successfully completing this course, you will be able to:
- Understand the fundamental neural networks architecture.
- Differentiate Neural networks from Deep Learning.
- Gain the knowledge of gradient descent, regularization techniques and hyper parameter techniques etc.
- Comprehend and implement Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), autoencoders.
- Understand and apply generative adversarial networks.
- Differentiate and implement different types of reinforcement learning.
- Implement the AI algorithms for applications such as image classification, speech recognition, recommender systems, autonomous cars, weather forecasting etc.
- Explore the emerging and advanced AI algorithms
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Spring enrollment opens on October 16!