Master
Neural Networks
from the Ground Up
A clear, hands-on guide to neural networks and large language models. Learn the theory, explore the architectures, and write real code — all in one place.
Everything you need to understand AI
Structured learning paths from foundational concepts to cutting-edge architectures, with code you can actually run.
Neural Networks Basics
Understand the building blocks — neurons, layers, activation functions, and how networks learn through backpropagation.
Deep Learning
Explore CNNs, RNNs, Transformers, and other architectures that power modern AI applications.
Introduction to LLMs
Discover how large language models like GPT work — from tokenization to attention mechanisms.
Code Examples
Hands-on Python and JavaScript code snippets you can run, modify, and learn from.
Resources
Curated links to research papers, open-source tools, courses, and community projects.
Learn by writing real code
Every concept comes with practical code examples in Python and JavaScript. Build a neural network from scratch, implement backpropagation, and explore transformer architectures — all with runnable code.
- Python examples with NumPy, PyTorch, and TensorFlow
- JavaScript examples with TensorFlow.js and ONNX Runtime
- Step-by-step explanations for every code block
import numpy as np
class NeuralNetwork:
def __init__(self, layers):
self.weights = [
np.random.randn(layers[i], layers[i+1])
for i in range(len(layers) - 1)
]
def forward(self, x):
for w in self.weights:
x = np.maximum(0, x @ w) # ReLU
return x
# Create a 3-layer network
nn = NeuralNetwork([784, 128, 64, 10])Your learning path
Follow a structured progression from fundamentals to state-of-the-art models.
Neural Network Fundamentals
Start with perceptrons, understand neurons, weights, biases, and activation functions. Build your first network from scratch.
Deep Learning Architectures
Explore convolutional networks, recurrent networks, and attention mechanisms. Understand when to use each architecture.
Large Language Models
Dive into transformers, tokenization, self-attention, and the training process behind models like GPT and BERT.
Hands-on Practice
Apply everything with real code examples, projects, and experiments using industry-standard frameworks.
Ready to understand how AI really works?
Start with the basics or jump straight into the topic that interests you most. Every concept is explained clearly with visual examples and runnable code.