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I'm new in NLP and text mining and I'm trying to build a documents classifier.
Once the model is trained, we test it on new documents (they, test-data, don't have labels). It is expected that the model is not 100% accurate; so for misclassified documents, we want interact with a user to correct these bad predictions.
I've two ideas:
Retrain the model where: traindata = old_traindata + data corrected by the user.
After each user's rectification, update model parameters.
Does this sound correct? in the second case, which kind of algorithms should I use? How efficiently can we solve this problem?
You can do this but it will be a very intensive task if you plan on retraining the model on the whole data again and again if it is on a daily basis. Instead of retraining the model completely, you should try transfer learning. Save your model and then load it back and train it on the data corrected by the user. The model will be able to correct it mistakes without losing what it has already learned. The problem with transfer learning is that after some time, it will get fine tuned to the new data that you will have to retrain it from scratch. But this is far better then retraining the model every day.
You should have proper metrics in place to check if your models accuracy starts dropping in the old data after several iterations of "transfer learning". If the accuracy drops, just retrain the model on all of the data till date and you will be good to go.
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Is it recommended to train my own models for things like sentiment analysis, despite only having a very small dataset (5000 reviews), or is it best to use pretrained models which were trained on way larger datasets, however aren't "specialized" on my data.
Also, how could I train my model on my data and then later use it on it too? I was thinking of an iterative approach where the training data would be randomly selected subset of my total data for each learning epoch.
I would go like this:
Try the pre-trained model and see how it goes
If results are non satisfactory, you can fine tune it (see this tutorial). Basically, you are using your own examples to change the weights of the pre-trained model. This should improve the results, but it depends on how your data is and how many examples you can provide. The more you have, the better it should be (I would try to use 10-20k at least)
Also, how could I train my model on my data and then later use it on it too?
Be careful to distinguish between pre-train and fine-tuning.
For pre-training you need a huge amount of text (like billions of characters), it is very resource demanding, and tipically you don't want to do that, unless for a very good reason (for example, a model for your target language does not exist).
Fine-tuning requires much much less examples (some tents of thousands), it take tipycally less than a day on a single GPU and allow you to exploit pre-trained model created by someone else.
From what you write, I would go with fine-tune.
Of course you can save the model for later, as you can see in the tutorial I linked above:
model.save_pretrained("my_imdb_model")
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Why does a small dataset have a high variance? Our professor once said it. I just did not understand it. Any help would be greatly appreciated.
Thanks in advance.
if your data set is small and you train your model to fit the data set ,it is easy to have overfitting problems.If your data set is big enough,a little overfitting may not a big problem ,but not in a small data set.
Every single one of us, by the time we are entering our professional careers, have been exposed to a larger visual dataset then the largest dataset available for AI researchers. On top of this, we have sound, smell, touch, and taste data all coming in from our external senses. In summary, humans have a lot of context on the human world. We have a general common-sense understanding of human situations. When analyzing a dataset, we combine the data itself with our past knowledge in order to come up with an analysis.
The typical machine learning algorithm has none of that — it has only the data you show to it, and that data must be in a standardized format. If a pattern isn’t present in the data, there is no way for the algorithm to learn it. That's why when given a small dataset it is more prone to error.
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Having some understanding on the concepts and steps of building a ML model covering these aspects -
Understanding and categorising the problem as - Supervised or
Unsupervised, Regression or Classification or Clustering, etc.
Feature designing i.e. features/input parameters to consider
Splitting the data into train and test sets. (Cross-Validation is
another important concept in here.)
Comparing various models (like KNN, SVM, Random Forest, etc.) and understand which fares well. Basically, cross validate the scores and understand the prediction capabilities.
Doubt::
How the newer data is being fed to ML to keep it updated and better prediction?
nothing has to be finalized, once you get new data, you can retrain your model with all relevant data, or update your model(another iteration of gradient descent for linear regression for example).
if it is a relevant data., i.e. data from the same distribution, it shouldn't "hurt" the model.
this is essentially the same question as #1. the details depends on the model. some models you just have to retrain with all relevant data. some model you can just update with new data.
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I have quite a challenging use case for image recognition. I want to detect composition of mixed recycling e.g. Crushed cans,paper,bottles and detect any anomalies such as glass, bags, shoes etc.
Trying images with the google vision api the results are mainly "trash", "recycling" "plastic" etc likely because the api hasn't been trained on mixed and broken material like this?.
For something like this would I have to go for something like tensor flow and build a neural network from my own images? I guess I wouldn't need to use google for this as tensor flow is open source?
Thanks.
So generally, when ever you apply machine learning to a new, real world use case, it is a good idea to get your hands on a representative dataset, in your case it would be images of these trash materials.
Then you can pick an appropriate detection model (VGG, Inception, ResNet), modify the final classification layer to output as many category labels as you require (maybe 'normal' or 'anomaly' in your case, so 2 classes).
Then you load the pre-trained weights for this network, because the learned features generalize (google 'Transfer Learning'), initialize your modified classification layer randomly, and then train the last layer, maybe train the last two layers, or last three layers (depending on what works best, how much data you have, generalization) etc.
So, in short:
1. Pick a pretrained model.
2. Modify it for your problem.
3. Finetune the weights on your own dataset.
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New to machine learning so looking for some direction how to get started. The end goal is to be able to train a model to count the number of objects in an image using Tensorflow. My initial focus will be to train the model to count one specific type of object. So lets say I take coins. I will only train the model to count coins. Not worried about creating a generic counter for all different types of objects. I've only done Google's example of image classification of flowers and I understand the basics of that. So looking for clues how to get started. Is this an image classification problem and I can use the same logic as the flowers...etc etc?
Probably the best performing solution for the coin problem would be to use a regression to solve this. Annotate 5k images with the amount of objects in the scene and run your model on it. Then your model just outputs the correct number. (Hopefully)
Another way is to classify if an image shows a coin and use a sliding window approach like this one: https://arxiv.org/pdf/1312.6229.pdf to classify for each window if it shows a coin. Then you count the found regions. This one is easier to annotate and learn and better extensible. But you have the problem of choosing good windows and using the result of those windows in a concise way.