How Neural Networks Actually Work — A Thread for Curious Minds

<p>Before transformers, before backprop, there was <strong>one neuron</strong> — Frank Rosenblatt's 1958 Perceptron. Build it and you understand the atom that every deep network

<p>Multilayer Perceptrons (MLPs) are the foundation of deep learning. This guide explains MLP intuition, real-world usage, and when you should (and shouldn’t) use it.</p>

<p>NLP models cannot process words directly, which is why tokenization[1] exists. Tokenization ends with a list of integers — token IDs like <code>[30642, 1634, 318, ...]</code>.

<p>Every modern deep learning framework — PyTorch, TensorFlow, JAX — does one thing brilliantly: it computes gradients for you. Call <code>loss.backward()</code> and millions of

<p>In my previous series of articles, we mainly explored the <strong>theory behind various neural network concepts</strong>.</p> <p>In this new series, we will focus on

<h1> Introduction </h1> <p>I studied CNN (Convolutional Neural Network) in deep learning, and I'm writing down what I understood as a beginner.</p> <h1> References </h1> <p>These