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Category Archives: Neural Networks
LSTMs
In past posts, I’ve described how Recurrent Neural Networks (RNNs) can be used to learn patterns in sequences of inputs, and how the idea of unrolling can be used to train them. It turns out that there are some significant … Continue reading
Posted in Neural Networks
3 Comments
The TensorFlow perspective on neural networks
A few weeks ago, Google announced that it wasÂ open sourcing an internal system called TensorFlow that allows one to build neural networks, as well as other types of machine learning models. (Disclaimer: I work for Google.) Because TensorFlow is designed … Continue reading
Posted in Neural Networks
2 Comments
Neural networks, linear transformations and word embeddings
In past posts, I’ve described the geometry of artificial neural networks by thinking of the output from each neuron in the network as defining a probability density function on the space of input vectors. This is useful for understanding how … Continue reading
Posted in Neural Networks
10 Comments
Recurrent Neural Networks
So far on this blog, we’ve mostly looked at data in two forms – vectors in which each data point is defined by a fixed set of features, and graphs in which each data point is defined by its connections … Continue reading
Posted in Neural Networks
5 Comments
GPUs and Neural Networks
Artificial neural networks have been around for a long time – since either the 1940s or the 1950s, depending on how you count. But they’ve only started to be used for practical applications such as image recognition in the last … Continue reading
Posted in Neural Networks
11 Comments