Deep-learning - TechnoScripts

deep-learning

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

DL: First Neural Network with TensorFlow/Keras

Writing a neural network in raw PyTorch means a training loop, gradient zeroing, and a pile of boilerplate before your model learns a thing. TensorFlow Keras throws all of that out: you stack a few layers, call compile() and fit(), and training just runs. In this tutorial you build your first Keras network, add callbacks, and even run it on the PyTorch backend, since TensorFlow itself will not install on the newest Python yet. “The purpose of Keras is to ... Read More

Python: PyTorch vs TensorFlow vs JAX, Deep Learning Frameworks Compared

Ask three machine learning engineers which framework to use and you will likely get three answers, plus a strong opinion for free. The pytorch vs tensorflow argument has run for years, and JAX quietly joined the fight. This guide puts all three side by side on API design, debugging, performance, ecosystem, and industry adoption, then hands you a decision flowchart so you can pick one with confidence. The best framework is the one your team already knows. The second best ... Read More

Dropout and Batch Normalization: DL Regularization Explained

A big neural network is a champion memorizer, and deep learning regularization is how you keep it honest. Give it enough neurons and it will learn your training data by heart, noise and all, then fall flat on data it has never seen. This post shows you how to stop that, using dropout, batch normalization, and weight decay to close the gap between training and test accuracy. Every number below comes from real code run on Python 3.14.6. “Dropout provides ... Read More