Deep-learning - TechnoScripts

deep-learning

Deep learning tutorials in Python covering neural networks, training, and common frameworks.

DL: Activation Functions: sigmoid, tanh, ReLU, softmax

Python activation functions are what let a neural network learn curves instead of straight lines, and this hands-on guide covers the five you will meet most in deep learning: sigmoid, tanh, ReLU, Leaky ReLU, and softmax. You will learn what each one does, when to reach for it, why ReLU became the default for hidden layers, and how the vanishing gradient problem quietly killed early deep networks. “Programming is the art of telling another human being what one wants the ... Read More

Backpropagation Explained: Gradient Descent in Python Made Simple

Python backpropagation is the algorithm that makes neural networks actually learn. We follow the chain rule through a network one layer at a time, work out the gradients by hand, then build a complete training loop from scratch with NumPy. By the end the word “backprop” will feel like something you could rebuild on a napkin. “Backpropagation is just the chain rule from calculus, applied repeatedly. The genius is not in the math: it is in the software that automates ... Read More

DL: First Neural Network with PyTorch

Remember spelling out the chain rule by hand in the NumPy posts just to train one tiny network? This PyTorch tutorial hands you the calculator instead: tensors that run on a GPU, autograd that computes gradients for you, and the one training loop every project reuses, from a 50-line classifier to a giant language model. “PyTorch is Python first. It feels like NumPy with GPU support and automatic differentiation built in. That is why researchers love it.” Soumith Chintala, PyTorch ... Read More