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MIT Sloan AI Adoption: Build a Playbook That Drives Real Business ROI
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Embark on a comprehensive live stream that initiates a series on neural networks, covering the fundamentals of multi-layer perceptrons and essential linear algebra concepts for machine learning. Delve into the intricacies of neural network architecture, exploring topics such as multilayered perceptrons and their implementation. Gain insights into the crucial role of linear algebra in neural networks, with a focus on matrix mathematics. Benefit from a structured approach that includes an introduction to neural networks, in-depth discussions on multilayered perceptrons, and practical applications of linear algebra in neural network design. Conclude the session with a Q&A segment to reinforce understanding and address specific queries.
Syllabus
- Intro to Neural Networks.
- Multilayered Perceptron Part 1.
- Multilayered Perceptron Part 2.
- Linear Algebra for Neural Networks Part 1.
- Linear Algebra for Neural Networks Part 2.
- Conclusion/Q&A.
Taught by
The Coding Train