After the rather long post on how to implement a neural network, here is a brief summary on how each hyper-parameter affects the network.
Today to conclude my series on neural network I am going to write down some guidelines and methodology for developing, testing and debugging a neural network. As we will see (or as you already experienced) implementing a neural network is tricky and there is often a thin line between failure and success – between something […]
Apache Spark is a computation engine for large scale data processing. Over the past few months a couple of new data structures have been available. In this post I am going to review each data structure trying to highlight their forces and weaknesses. I also compares how to express a basic word count example using […]
After introducing the convolutional neural networks I continue my serie on neural networks with another kind of specialised network: the recurrent neural network. Principle The recurrent neural network is a kind of neural network that specialises in sequential input data. With traditional neural network sequential data (e.g. time series) are split into fixed-sized windows and only the data […]