Now that more than a year has passed since our first deep learning project emerged, we have had to keep moving forward and delivering the best models we can. Doing so has involved a lot of research, trying out different models, from as simple as bag-of-words, LSTM and CNN, to the more advanced attention, MDN and multi-task learning. Even the simplest model we tried has many hyperparameters, and tuning these might be even more important than the actual architecture we ended up using – in terms of the model’s accuracy. Although there’s a lot of active research in the field of hyperparameter tuning (see 1, 2, 3), implementing this tuning process has evaded the spotlight. If you go around and ask people how they tune their models, their most likely answer will be “just write a script that does it for you”. Well, that’s easier said than done… Apparently, there […]
Back in 2012, when neural networks regained popularity, people were excited about the possibility of training models without having to worry about feature engineering. Indeed, most of the earliest breakthroughs were in computer vision, in which raw pixels were used as input for networks. Soon enough it turned out that if you wanted to use textual data, clickstream data, or pretty much any data with categorical features, at some point you’d have to ask yourself – how do I represent my categorical features as vectors that my network can work with? The most popular approach is embedding layers – you add an extra layer to your network, which assigns a vector to each value of the categorical feature. During training the network learns the weights for the different layers, including those embeddings. In this post I will show examples of when this approach will fail, introduce category2vec, an alternative method […]
Large production pipelines in TensorFlow are quite difficult to pull off. Training small models is easy, and we mostly do this at first, but as soon as we get to the rest of the pipeline, complexity rapidly mounts. One reason is that the “Computation Graph” abstraction used by TensorFlow is a close, but not exact match for the ML model we expect to train and use. How so? Typically a model will be used in at least three ways: Training – finding the correct weights or parameters for the model given some training data. Often done periodically as new data arrives. Evaluation – calculating various metrics during training on a different data set to evaluate training quality or for cross validation. Serving – on-demand prediction for new data There could be more modes. For example we could re-train an existing model or apply the model to a large amount of […]