📘 Day 1 - My Journey into Neural Networks (from the book Make Your Own Neural Network)

So today I started reading Make Your Own Neural Network by Tariq Rashid — and it’s already feeling like the right kind of book for someone like me who's starting fresh, with zero prior knowledge of neural networks.

Here’s a raw summary of what I got from today’s reading:

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🔹 The Book’s Vibe & Intent

The author made it super clear: this book is for complete beginners. No fancy background required. It promises to cover just the right amount of Python and just enough math to help you actually build a neural network from scratch. That already made me feel like, “Okay, I’m in the right place.”

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🧠 AI Wasn’t Born Yesterday…

The book gives a short history of AI. Turns out, AI as an idea has been around since the 1950s. Back then, people even predicted AI would become real in a decade. But reality hit hard.

Because of:

• The complexity of the idea

• Budget limitations

• Lack of understanding

…progress stalled hard around the 1970s.
There was this hype — and then silence.

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🤖 Deep Blue, Scary Cool Stuff

Fast-forward to the 90s: IBM’s Deep Blue defeated a world chess champion (in 1997 or 1999, I think). That blew minds — and honestly, scared people too. The excitement came with fear, and the myths about AI just kept growing.

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🐝 Learning from Nature

Engineers realized: building AI from pure logic gates (0s and 1s) wasn’t enough. They decided to take inspiration from nature, like actual biological brains.

They looked into:

• How pigeons can navigate, hunt, and survive with a small brain

• How bees operate with more than 900,000 neurons

That shift in thinking led to the idea of neural networks — inspired by biological computing, especially how our brain works.

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💡 Google’s DeepMind was mentioned too

The book casually drops how Google’s DeepMind is able to learn how to play a game and beat it, just by trying over and over. That’s when I realized how crazy powerful neural nets can be.

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🙌 Why This Book Exists

The author, Tariq Rashid, said he personally struggled to learn AI and neural networks as a student. All the resources back then assumed you were a math nerd or already deep into the field.

This book is for people like us — curious, ambitious, but starting from scratch. No gatekeeping here. Just good learning.

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🧩 Book Structure

The book is divided into 3 main parts:

1. Intro + General & Mathematical Concepts

2. Python Essentials for Neural Nets

3. Advanced Neural Net Ideas + Optimization

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That’s it for Day 1. I’m hyped to keep going.