Oreilly – Math and Architectures of Deep Learning 2024-5

Math and Architectures of Deep Learning course. In today’s world, deep learning models are widely used in solving various problems. However, a deep understanding of how these models work is often challenging for engineers. This course will help you to easily understand the complex concepts of deep learning by providing simple explanations and practical examples. This comprehensive and detailed training course examines the mathematical concepts and architectures behind deep learning models. By participating in this course, you can better understand the performance of deep learning models and optimize them. In a nutshell, this course will help you open the deep learning black box and gain a deep understanding of how it works.

What you will learn:

• The relationship between mathematical concepts, theory and programming in deep learning
• Linear algebra, vector calculus and multivariate statistics for deep learning
• The structure of neural networks
• Implementation of deep learning architectures with Python and Python
• Design and implement your own deep learning models.
• Diagnose and troubleshoot problems with deep learning models.
• Understand the latest research in deep learning and apply it to your projects.
• Fixed problems with models with poor performance
• Practical and downloadable codes in the form of Jupyter notebooks

This course is suitable for people who:

• They are familiar with Python programming language.
• They know the basic concepts of algebra and arithmetic.