This course covers the essential mathematical concepts for understanding and applying deep learning algorithms. Whether you are a beginner or an experienced AI practitioner, this course will provide you with a solid foundation in the mathematical principles behind deep learning.
Course Outline
- Linear Algebra: Understanding vectors, matrices, eigenvalues, and eigenvectors.
- Calculus: Differentiation, optimization, and gradient descent.
- Probability and Statistics: Probability distributions, Bayes' theorem, and hypothesis testing.
- Numerical Optimization: Conjugate gradient, Newton's method, and optimization algorithms.
Key Concepts
- Backpropagation: A crucial algorithm for training neural networks.
- Convolutional Neural Networks (CNNs): Used for image recognition and processing.
- Recurrent Neural Networks (RNNs): Ideal for sequence data like time series or text.