Neural Networks Mastery

From Biological Neurons to Transformers — Built from Scratch

Understand neural networks at the deepest level by building every architecture from scratch with NumPy. No framework magic — pure mathematics and Python. From single perceptrons through CNNs, RNNs, LSTMs, Autoencoders, GANs, and Transformers.

9Parts
9-Part Main Series

All Articles in This Series

A bottom-up journey through neural network theory and implementation. Every architecture is built from scratch with NumPy so you understand exactly what happens inside. Framework-based implementations are cross-referenced to our PyTorch Mastery and TensorFlow Mastery series.

Part 1
Why Neural Networks & Their History
30 min read
MotivationHistoryClassical ML
Why classical ML falls short, how the brain inspired artificial neurons, the AI winters, and the modern deep learning renaissance.
Read Article →
Part 2
Building Blocks: Neurons, Weights & Activations
35 min read
PerceptronSigmoidReLU
Build artificial neurons from scratch. Understand weights, biases, activation functions (Sigmoid, ReLU, Tanh), and network layers.
Read Article →
Part 3
How Neural Networks Learn
35 min read
BackpropGradient DescentOptimizers
Master loss functions, gradient descent, backpropagation mathematics, and modern optimizers (SGD, Adam, RMSprop) — all implemented from scratch.
Read Article →
Part 4
Build Your First Neural Network from Scratch
30 min read
XORNumPyVisualization
Solve the XOR problem with a multi-layer network built entirely in NumPy. Visualize decision boundaries evolving during training.
Read Article →
Part 5
Neural Network Architectures Overview
25 min read
FNNCNNRNN
Survey all major architectures — Feedforward, CNN, RNN, Autoencoders, GANs, Transformers — when to use each, and how they connect.
Read Article →
Part 6
CNNs Deep Dive: Convolutions from Scratch
40 min read
ConvolutionPoolingFrom Scratch
Implement convolution, pooling, and a full CNN from scratch with NumPy. Understand every operation before using frameworks.
Read Article →
Part 7
RNNs & LSTMs Deep Dive
40 min read
RNNLSTMGRU
Build recurrent networks, LSTM gates, and GRU cells from scratch. Understand vanishing gradients and how gating solves them.
Read Article →
Part 8
Autoencoders & GANs Deep Dive
40 min read
AutoencoderVAEGAN
Implement autoencoders, variational autoencoders, and GANs from scratch. Understand latent spaces and adversarial training.
Read Article →
Part 9
Transformers & Best Practices
40 min read
AttentionTransformerProduction
Build self-attention, multi-head attention, and a Transformer encoder from scratch. Plus best practices, debugging, and real-world applications.
Read Article →