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The “Maths For LLM/Deep-Learning” course is designed to provide a comprehensive foundation in mathematical concepts and techniques essential for understanding and effectively applying deep learning algorithms and running Large Language Model. This course is tailored for students, researchers, and professionals seeking to delve into the intricate world of deep learning, which is at the forefront of modern artificial intelligence (AI) and machine learning (ML) advancements.
Course Objectives:
1. Linear Algebra: The course begins by exploring fundamental concepts in linear algebra, such as vectors, matrices, matrix operations, and vector spaces. Participants will learn how to manipulate and transform data using linear algebra techniques, which are essential for understanding neural networks.
2. Calculus: Participants will delve into calculus, including derivatives and integrals, to comprehend the optimization processes behind deep learning algorithms. Understanding gradients and how they are used in back-propagation is crucial for training neural networks.
3. Multivariate Calculus: Building upon the calculus foundation, multivariate calculus topics, such as partial derivatives and gradients, are covered. These are vital for understanding the mathematics behind deep learning architectures with multiple parameters.
4. Neural Networks Mathematics: The course will explore the mathematical principles underlying neural network architectures, including feed-forward and convolutional neural networks. Students will learn how to formulate neural network equations and understand their inner workings.
5. Deep Learning Architectures: Participants will gain insights into advanced deep learning architectures such as recurrent neural networks (RNNs) and generative adversarial networks (GANs). They will understand the mathematical foundations behind these specialized models.
6. Practical Applications: Throughout the course, mathematical concepts are applied to practical deep learning problems. Students will implement mathematical techniques in real-world scenarios, gaining hands-on experience in solving complex AI challenges.
By the end of the “Maths For LLM/Deep-Learning” course, participants will have a solid understanding of the mathematical foundations underpinning deep learning algorithms. They will be equipped with the knowledge and skills required to design, implement, and optimize neural networks for a wide range of applications in artificial intelligence and machine learning.
