Basics Of Neural Networks: Your Ultimate Beginner'S Guide

Learn the basics of Neural Networks quickly and easily with machine learning examples for every concept.

What you'll learn

Learn the fundamentals of Neural Networks through clear and intuitive examples.

Gain a solid understanding of neural networks and how they work.

Explore the roles of activation functions and biases in shaping a network's behavior.

Uncover the process of forward propagation and its significance in neural computation.

Learn the essential steps to train a neural network effectively.

Delve into the concept of loss functions and their role in evaluating performance.

Discover practical machine learning examples to bring each concept to life.

Requirements

No prerequisites

Description

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.

Overview

Section 1: Introduction to Neural Networks

Lecture 1 What is a neural network ?

Lecture 2 Biological Neural Network vs Artificial Neural Network

Lecture 3 How does it work ?

Section 2: Activation functions

Lecture 4 What is an activation function?

Lecture 5 Common activation functions

Lecture 6 Activation functions in binary classification

Lecture 7 Activation functions in Multi-class classification

Lecture 8 Activation functions in regression

Section 3: Bias

Lecture 9 What is the Bias?

Section 4: How to train a Neural Network?

Lecture 10 Forward propagation

Lecture 11 Steps to train a Neural Network

Section 5: Loss function

Lecture 12 What is the loss function?

Lecture 13 Loss vs Cost

Lecture 14 Binary Cross Entropy Loss

Lecture 15 Categorical Cross Entropy Loss

Section 6: Conclusion

Lecture 16 Conclusion

This course is designed for anyone eager to quickly gain a solid foundation in the basics of neural networks.,Beginners to neural netwokrs.,Students or professionals.