Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

The data science doctor continues his exploration of techniques used to reduce the likelihood of model overfitting, caused by training a neural network for too many iterations. Regularization is a ...

Although neural networks have been studied for decades, over the past couple of years there have been many small but significant changes in the default techniques used. For example, ReLU (rectified ...

Neural networks are all the rage right now with increasing numbers of hackers, students, researchers, and businesses getting involved. The last resurgence was in the 80s and 90s, when there was little ...

PyTorch 1.0 shines for rapid prototyping with dynamic neural networks, auto-differentiation, deep Python integration, and strong support for GPUs Deep learning is an important part of the business of ...

Google's open source framework for machine learning and neural networks is fast and flexible, rich in models, and easy to run on CPUs or GPUs What makes Google Google? Arguably it is machine ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead ...