How to Learn Neural Networks From Scratch? [Step-by-Step] 2024

Do you want to know How to Learn Neural Networks From Scratch?… If yes, this blog is for you. In this blog, I will share a step-by-step roadmap to Learn Neural Networks From Scratch.

So, take a few minutes and find the complete roadmap to Learn Neural Networks From Scratch. You can bookmark this article so that you can refer to this article later.

Now without any further ado, let’s get started-

How to Learn Neural Networks From Scratch?

Table Of Contents

Step 1- Understanding the Basics
Step 2- Setting the Foundation
Neural Network Learning Resources
Step 3- Building Blocks of Neural Networks
Step 4- Coding Neural Networks
Step 5- Training and Fine-Tuning
Step 6- Troubleshooting and Debugging
Step 7- Staying Updated and Engaged
Learning Plan for Neural Networks: A Step-by-Step Guide
Conclusion

Step 1- Understanding the Basics

What Are Neural Networks?

Neural networks are like computer brains inspired by our human thinking. They have special parts (called neurons) that work together to understand things. These networks are super cool because they can learn from patterns and make predictions, which is a big deal in the world of smart machines.

Why Learn Neural Networks From Scratch?

Learning about neural networks from the beginning is like building a house from the ground up. It helps you understand how everything works. Plus, it gives you the skills to fix things and come up with new ideas – important stuff in the ever-changing world of smart computers.

Step 2- Setting the Foundation

Brushing Up on the Math

Before we dive into neural networks, let’s talk about some math basics. Don’t worry if math sounds scary – there are lots of online videos and games that can help make it easier to understand.

Programming Fundamentals

Knowing how to tell a computer what to do is crucial. Python is a great language for this, and it’s not too hard to learn. Think of it like learning a new language, and the more you practice, the better you’ll get.

Neural Network Learning Resources

Neural Networks and Deep Learning
Deep Learning– Udacity
Deep Learning A-Z™: Hands-On Artificial Neural Networks– Udemy
Intro to Deep Learning with PyTorch– Udacity FREE Courses
Introduction to Deep Learning & Neural Networks with Keras– Coursera
Deep Neural Networks with PyTorch– Coursera
Intro to TensorFlow for Deep Learning– Udacity FREE Course
Introduction to Deep Learning-edX
Deep Learning in Python– Datacamp
Deep Learning: Convolutional Neural Networks in Python– Udemy
Convolutional Neural Networks– Coursera
Complete Guide to TensorFlow for Deep Learning with Python– Udemy
Deep Learning with Python and PyTorch- edX
Introduction to Deep Learning– Coursera
Introduction to Deep Learning with PyTorch– DataCamp

Step 3- Building Blocks of Neural Networks

Neurons and Activation Functions

Neurons are like the building blocks of neural networks. They take in information, think about it, and give an answer. Activation functions help them decide what answer to give. It’s a bit like learning the ABCs before making words.

Layers and Architectures

Neural networks have layers that do different jobs. Some take in information, some think about it, and some give the final answer. Architectures, like building plans, decide how these layers work together. It’s like putting together puzzle pieces to make something amazing.

Loss Functions and Optimization

To make our neural network smarter, we need a way to check how well it’s doing. Loss functions do this by measuring the difference between what the computer thinks and what’s right. Optimization helps the computer get better over time. It’s like tuning a musical instrument to make it sound just right.

Step 4- Coding Neural Networks

Choosing a Programming Language

For talking to computers, Python is like our superhero language. It’s easy to read, and there are special tools (like TensorFlow or PyTorch) that make building neural networks easier. Think of it as having the right tools for a job.

Hands-On Coding Exercises

Now, let’s get our hands dirty with some coding! Start with simple exercises to practice what you’ve learned. It’s okay to make mistakes – that’s how we learn. Think of it like playing a game; the more you play, the better you become.

Step 5- Training and Fine-Tuning

Dataset Preparation

Our neural network needs good data to learn from. Collecting, cleaning, and preparing data is like giving the computer good ingredients for a recipe. There are lots of practice datasets online to play with.

Model Training

Teaching our neural network is like training a pet. We show it lots of examples, let it make guesses, and help it get better each time. It takes time and patience – don’t rush it!

Fine-Tuning for Performance

Once our network has learned the basics, it’s time to make it even better. Think of it like adding special moves to a video game character. Tweak and adjust until you get the results you want.

Step 6- Troubleshooting and Debugging

Common Issues

Just like solving puzzles, learning about neural networks comes with challenges. Overfitting, underfitting, and vanishing gradients might sound like big problems, but they’re just bumps in the road. Treat them like fun puzzles to solve.

Debugging Strategies

When things go wrong, it’s time to put on our detective hats. Break the problem into smaller parts, check the data, and use tools to find where things went wonky. Each problem we solve is a step toward becoming a pro.

Step 7- Staying Updated and Engaged

Community Involvement

Learning about neural networks is like being in a big, friendly club. Talk to others, ask questions, and stay connected. The more friends you have in the club, the more you’ll learn.

Continuous Learning

This isn’t a one-time thing; it’s a lifelong journey. Stay curious, explore new things, and try real-world projects. The more you dive into the world of neural networks, the more confident and awesome you’ll become.

Learning Plan for Neural Networks: A Step-by-Step Guide

Learning Stage	Time Frame	Tips and Resources
Understanding the Basics	1-2 weeks	– Watch beginner-friendly videos to grasp the concept.
		– Read introductory articles to understand the importance of neural networks.
Setting the Foundation	2-4 weeks	– Brush up on math basics using online resources.
		– Learn the basics of Python programming or strengthen existing skills.
Building Blocks of Neural Networks	3-4 weeks	– Dive into neurons, activation functions, layers, and architectures one step at a time.
		– Understand each concept before moving on to the next.
Coding Neural Networks	4-6 weeks	– Choose Python and explore TensorFlow or PyTorch.
		– Practice coding with progressively complex exercises.
Training and Fine-Tuning	6-8 weeks	– Learn the importance of good data and practice dataset preparation.
		– Start with basic model training and gradually fine-tune for better performance.
Troubleshooting and Debugging	2-3 weeks	– Encounter and solve common issues, enhancing problem-solving skills.
		– Utilize debugging strategies to understand and fix errors in your code.
Staying Updated and Engaged	Ongoing	– Engage with the neural network community through forums, social media, and conferences.
		– Continue learning and explore advanced topics over time.

Conclusion

In this article, I have discussed a step-by-step roadmap on How to Learn Neural Networks From Scratch. If you have any doubts or queries, feel free to ask me in the comment section. I am here to help you.

All the Best for your Career!

Happy Learning!