Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

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 ...

Neural network dropout is a technique that can be used during training. It is designed to reduce the likelihood of model overfitting. You can think of a neural network as a complex math equation that ...

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 ...

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 ...

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 ...