Basics of Neural Networks: Your Ultimate Beginner's Guide

Welcome to the most beginner-friendly introduction to Neural Networks!

My name is Rim Zakhama, your instructor for this course. I am an AI expert with a PhD in Applied Mathematics and Computer Science. I also hold a Master’s degree in Computer Science and an Engineering degree. My passion is to make complex AI concepts accessible and easy to understand for everyone.

If you're looking to understand the basics of Neural Networks in a simplified and time-efficient way, you're in the right place. This course is tailored for absolute beginners, requiring no prior knowledge of machine learning or deep learning.

With clear and concise explanations, this course breaks down key Neural Network concepts into digestible pieces. You’ll learn through simple explanations and relatable examples, making it easy to grasp the core ideas. While the course includes many examples to illustrate the concepts, it does not include exercises, allowing you to focus entirely on understanding the material.

By the end of this journey, you’ll have a solid understanding of the fundamental concepts of neural networks and how they work. This knowledge will empower you to build systems that can learn and make decisions from data.

We will cover foundational concepts such as:

• What neural networks are and how they mimic the human brain.

• Key components like activation functions, weights, biases, and loss functions.

• The process of forward propagation and how neural networks make predictions.

• Steps involved in training a neural network.

While we will cover the steps involved in training a neural network, we will avoid delving into complex mathematics, such as gradient descent algorithm, to ensure the material remains accessible to all learners.