Constructing Neural Networks From Scratch: Part 1

Neural networks are increasingly accessible due to frameworks like TensorFlow and PyTorch, but building them from scratch offers deeper insights. The article shows how to create a basic neural network using NumPy to solve logic gate problems, covering concepts like forward propagation, backpropagation, and training loops. It then compares this approach to using TensorFlow/Keras, demonstrating the efficiency of frameworks while emphasizing the importance of understanding fundamental principles.