[unable to retrieve full-text content]So yesterday someone told me you can build a (deep) neural network in 15 minutes in Keras. Of course, I didn’t believe that at all. So the next day I set out to play with Keras on my own data.

Original Post: Today I Built a Neural Network During My Lunch Break with Keras

# Neural Networks

## Some Musings on Capsule Networks and DLPaper2Code

[unable to retrieve full-text content]Only the Godfather of Deep Learning did it again and came up with something brilliant — adding layers inside existing layers instead of adding more layers i.e nested layers…. giving rise to the Capsule Networks!

Original Post: Some Musings on Capsule Networks and DLPaper2Code

## What is a Bayesian Neural Network?

[unable to retrieve full-text content]BNNs are important in specific settings, especially when we care about uncertainty very much.

Original Post: What is a Bayesian Neural Network?

## Using Deep Learning to Solve Real World Problems

[unable to retrieve full-text content]Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.

Original Post: Using Deep Learning to Solve Real World Problems

## Exploring Recurrent Neural Networks

[unable to retrieve full-text content]We explore recurrent neural networks, starting with the basics, using a motivating weather modeling problem, and implement and train an RNN in TensorFlow.

Original Post: Exploring Recurrent Neural Networks

## InfoGAN - Generative Adversarial Networks Part III

[unable to retrieve full-text content]In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.

Original Post: InfoGAN - Generative Adversarial Networks Part III

## InfoGAN - Generative Adversarial Networks Part III

[unable to retrieve full-text content]In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.

Original Post: InfoGAN - Generative Adversarial Networks Part III

## Interpreting Deep Neural Networks with SVCCA

Original Post: Interpreting Deep Neural Networks with SVCCA

## Interpreting Deep Neural Networks with SVCCA

Original Post: Interpreting Deep Neural Networks with SVCCA

## How To Unit Test Machine Learning Code

[unable to retrieve full-text content]One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.

Original Post: How To Unit Test Machine Learning Code