for epoch in range(10000): # Forward pass hidden_layer = sigmoid(np.dot(X, weights1) + bias1) output_layer = sigmoid(np.dot(hidden_layer, weights2) + bias2)
def sigmoid_derivative(x): return x * (1 - x)
Make Your Own Neural Network by Tariq Rashid: A Definitive Guide make your own neural network by tariq rashid
Tariq Rashid’s primary mission is to demystify "scary" mathematics. The book assumes only a secondary school-level understanding of math and provides a "gentle" introduction to the necessary calculus and linear algebra required for neural networks. The book is structured into three distinct phases:
Neural networks are a fundamental concept in machine learning, inspired by the structure and function of the human brain. A neural network is a network of interconnected nodes or "neurons" that process and transmit information. Each node applies a non-linear transformation to the input data, allowing the network to learn complex patterns and relationships. for epoch in range(10000): # Forward pass hidden_layer
The book focuses on the "universal building blocks" of any neural network:
# Define the activation functions def sigmoid(x): return 1 / (1 + np.exp(-x)) A neural network is a network of interconnected
Rashid’s approach is intentionally designed for those who might be intimidated by advanced mathematics. He breaks the journey into three distinct phases:
“If you can understand basic arithmetic and a little bit of calculus, you can understand and build a neural network.” — Tariq Rashid