This paper shows that gradually pruning deep neural networks boosts efficiency and performance in resource-limited settings, outperforming dense models

Learn about batch normalization and dropout techniques for neural networks. Guide covering how they work, when to use them, and best practices

Learn how a neural network works with Python: linear regression, loss function, gradient, and training. Hands-on tutorial with code

New blog post by @twiecki

Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics

TensorFlow is a powerful tool for deep learning, and the Bayesian neural network is a key component. This guide covers the basics of TensorFlow and Bayesian

This is the first in a series of posts looking at the 'top 100 awesome deep learning papers.' Deviating from the normal one-paper-per-day format, I'll take the papers mostly in their groupings as found in the list (with some subdivision, plus a few extras thrown in) - thus we'll be looking at multiple papers each…

Time Series with Deep Learning Quick Bite

# Learning both Weights and Connections for Efficient Neural Network Song Han, Jeff Pool, John Tran, William Dally Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude

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