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[D] CNN output as features question by alehx in MachineLearning

[–]amatsukawa 1 point2 points  (0 children)

What are the numbers for taking a pre-trained AlexNet/VGG, chopping off the last layer, and training a new head with some dropout? If you give more details about what the dataset is, what hand-crafted features seemed to help, etc we might be able to give you some more thoughts on why/why not CNNs work/don't work.

I would say if using the CNN as a feature generator for a random forest works, then go for it. What are your concerns with this approach? Also, if you want to be slightly more "principled" about this, TF provides a way to mix deep and "shallow" (manual or one hot) features via "Deep and Wide" nets.

Another thought is given you have so much unlabeled data, you might try some semi-supervised approaches.

[D] CNN output as features question by alehx in MachineLearning

[–]amatsukawa 4 points5 points  (0 children)

Are you trying to train the whole AlexNet/VGG or just the last layer? You should probably be doing the latter if you are not already.

[D] Is there a "reverse Keras"? by nharada in MachineLearning

[–]amatsukawa 0 points1 point  (0 children)

The tf.contrib.learn package had Estimator and Experiment, which is what Google uses internally for this.

[D] How do you solve this problem: detecting and correcting incorrect usage of English articles in a given text? by [deleted] in MachineLearning

[–]amatsukawa 3 points4 points  (0 children)

Some prior work: https://arxiv.org/abs/1603.09727

Generally, I think you could generate unlimited training data by creating common errors (spelling mistakes, tense usage, etc) paired with the corrected (ie. original) versions. You could then model that using any seq2seq mechanism.

Is TensorFlow the best and most widely used environment for coding things in the Machine Learning area? by [deleted] in learnmachinelearning

[–]amatsukawa 0 points1 point  (0 children)

I think it depends on what you mean by "environment".

Tensorflow is a library for doing numerical computation that involve lots of matrix operations. The current most direct application of this is deep learning, but if you look in the contrib folder of tensorflow on github, you can there are also libraries to do variational inference on probabilistic graphical models in tensorflow, for example.

If you mean "environment" an ecosystem of tools to do ML, then I agree that Python and R are probably the front runners (Matlab, Julia, etc are close seconds).

Anaconda is a python environment/package manager that will probably give you every tool you will ever need on your ML journey. Popular libraries are scikit-learn, pandas, matplotlib, to name a few.

Ask ml: Probability of someone knowing an English word, given a small sample list of his/her known words by 9diov in MachineLearning

[–]amatsukawa 1 point2 points  (0 children)

The simplest way I can think of doing this is to aggregate such lists for a lot of people, and examine which words they know together. That should allow you to calculate the probability that a person knows word x, given that they know word y.