I need to find a deep learning based prediction model, where can I find it?
You can use Pytorch and Tensorflow pretrained models.
https://pytorch.org/docs/stable/torchvision/models.html
They can be automatically downloaded. There are some sample codes, that you can try:
https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py
If you are interested in deep learning, I suggest you review the basics of it in cs231n stanford. Your question is a bit odd, because you first need to define your task specifically. Prediction is not a good description. You could look for models for classification, segmentation, object detection, sequence2sequence(like translation), and so on...
Then you need to know how to search through projects on github, and then you need to know python (in most cases), and then use a pretrained model or use your own dataset to train or fine-tune the model for that task. Then you could pray that you have found a good model for your task, after that you need to validate the results on a test set. However, implementation of a model for real-life scenarios is another thing that you need to consider many other things, and you usually need some online-learning strategy, like Federated Learning. I hope that I could help you.
Related
So I'm getting more and more into deep learning using CNNs.
I was wondering if there are examples of "chained" (I don't know what the correct term would be) CNNs - what I mean by that is, using e.g. a first CNN to perform a semantic segmentation task while using its output as input for a second CNN which for example performs a classification task.
My questions would be:
What is the correct term for this sequential use of neural networks?
Is there a way to pack multiple networks into one "big" network which can be trained in one a single step instead of training 2 models and combining them.
Also if anyone could maybe provide a link so I could read about that kind of stuff, I'd really appreciate it.
Thanks a lot in advance!
Sequential use of independent neural networks can have different interpretations:
The first model may be viewed as a feature extractor and the second one is a classifier.
It may be viewed as a special case of stacking (stacked generalization) with a single model on the first level.
It is a common practice in deep learning to chain multiple models together and train them jointly. Usually it calls end-to-end learning. Please see the answer about it: https://ai.stackexchange.com/questions/16575/what-does-end-to-end-training-mean
I am trying to extract relation between two entities (entity1- relation- entity2) from news articles for stock prediction. I have used NER for entity extraction. It would be great if anyone could help me with relationship extraction.
Relation Extraction is a difficult task in NLP and most of the time there's not a one-size-fits-all solution to that. Depending on the task that you're trying to solve, I would suggest reading some literature about it on Google Scholar and see if there's something similar to what you're trying to do.
Sometimes, authors are kind enough to publish the code of their solution, which are mainly PyTorch/Tensorflow models (hopefully) trained on a specific dataset. One example is this paper.
If you want to stick with Spacy, there are some guides that might help you, but I'm not sure how well it could scale with the task that you need to solve.
Another more basic approach could be to just extract the shortest path between two entities in the semantic graph of a sentence. This might be quite limited, but can be fairly easy to implement.
One final idea that comes to mind is to use encoders and compute the similarity between sentences. If you're doing multi-class classification, this could help solving your problem.
Hope you find something useful among these.
I've found a tutorial in Kaggle web site that explains how to use RNN (Recurrent Neural Network) on the titanic data set in order to predict who survived.
my question is - how come RNN is suitable for this problem?
I thought RNN is not suitable for problems with csv file as a data set.
link to the tutorial (you can find the csv files in there) - https://www.kaggle.com/lusob04/titanic-rnn
and here is a sample of the dataset -
and another question - do you think CNN or RL is better suited for this problem?
Just like you suspected, RNN is not suitable for this kind of problem.
The link you shared offer a way to use RNN to solve the problem but I think using RNN in this case is an over-kill and that other, more simple models might even get you better results.
Even though I am quite familiar with the concepts of Machine Learning & Deep Learning, I never needed to create my own dataset before.
Now, for my thesis, I have to create my own dataset with images of an object that there are no datasets available on the internet(just assume that this is ground-truth).
I have limited computational power so I want to use YOLO, SSD or efficientdet.
Do I need to go over every single image I have in my dataset by my human eyes and create bounding box center coordinates and dimensions to log them with their labels?
Thanks
Yes, you will need to do that.
At the same time, though the task is niche, you could benefit from the concept of transfer learning. That is, you can use a pre-trained backbone in order to help your model to learn faster/achieve better results/need fewer annotations example, but you will still need to annotate the new dataset on your own.
You can use software such as LabelBox, as a starting point, it is very good since it allows you to output the format in Pascal(VOC) format, YOLO and COCO format, so it is a matter of choice/what is more suitable for you.
Greetings to everyone,
I want to design a system that is able to generate stories or poetry based on a large dataset of text, without being needed to feed a text description/start/summary as input at inference time.
So far I did this using RNN's, but as you know they have a lot of flaws. My question is, what are the best methods to achieve this task at the time?
I searched for possibilities using Attention mechanisms, but it turns out that they are fitted for translation tasks.
I know about GPT-2, Bert, Transformer, etc., but all of them need a text description as input, before the generation and this is not what I'm seeking. I want a system able to generate stories from scratch after training.
Thanks a lot!
edit
so the comment was: I want to generate text from scratch, not starting from a given sentence at inference time. I hope it makes sense.
yes, you can do that, that's just simple code manipulation on top of the ready models, be it BERT, GPT-2 or LSTM based RNN.
How? You have to provide random input to the model. Such random input can be randomly chosen word or phrase or just a vector of zeroes.
Hope it helps.
You have mixed up several things here.
You can achieve what you want either using LSTM based or transformer based architecture.
When you said you did it with RNN, you probably mean that you have tried LSTM based sequence to sequence model.
Now, there is attention in your question. So you can use attention to improve your RNN but it is not a required condition. However, if you use transformer architecture, then it is built in the transormer blocks.
GPT-2 is nothing but a transformer based model. Its building block is a transformer architecture.
BERT is also another transformer based architecture.
So to answer your question, you should and can try using LSTM based or transformer based architecture to achieve what you want. Sometimes such architecture is called GPT-2, sometimes BERT depending on how it is realized.
I encourage you to read this classic from Karpathy, if you understand it then you have cleared most of your questions:
http://karpathy.github.io/2015/05/21/rnn-effectiveness/