๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ ๐ก๐ฒ๐๐ฟ๐ฎ๐น ๐ก๐ฒ๐๐๐ผ๐ฟ๐ธ ๐๐ฟ๐ผ๐บ ๐ฆ๐ฐ๐ฟ๐ฎ๐๐ฐ๐ต
Libraries make deep learning easy. You write a few lines of code. The model trains. Results look great.
But libraries hide the mechanics. I wanted to see the math. I built a Multilayer Perceptron (MLP) using NumPy. I used no deep learning frameworks.
This taught me how neural networks work.
The Basics
- Weights start random.
- Training adjusts them.
- Biases add flexibility.
- Activation functions keep the model non-linear.
Forward Propagation Neural networks are sequences of matrix operations. Input goes in. Math happens. Output comes out.
Backpropagation This is the hard part. Many call it magic. It is not magic. It is calculus. The chain rule shows how weights affect the error. The network updates weights to reduce this error.
The Struggles
- Wrong tensor shapes break everything.
- Bad learning rates make loss explode.
- Finding the balance takes time.
The Lesson Now I see the machinery inside PyTorch. Functions like loss.backward() are no longer black boxes. I know what happens inside.
My Advice Build one simple network from scratch. You do not need a GPU. You do not need millions of parameters. It teaches you more than a dozen tutorials.
You will learn:
- Linear algebra.
- Optimization.
- Gradient computation.
Understanding the math makes you a better engineer.
Source: https://dev.to/shridipa_dhar_079d540328a/building-a-multilayer-perceptron-from-scratch-what-it-taught-me-about-neural-networks-1dgj Optional learning community: https://t.me/GyaanSetuAi