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I try to summarize my problem as good as I can, but it is really don't easy for me because I do not really know which key words to ask about...
I am quite new in machine learning and used Keras to solve problems I know the answer.
Example:
As an input have tons of photos of different object like cars, birds... (x-value) and a label what they are (y-value). The program can then recognize objects on new photos. The usual stuff.
How can I write a program that discovers new ways to solve a problem?
Example:
Players play a game, and I record their moves as well as the result/score. How can I write program that finds the optimal strategy? I tried to just sort out games with bad scores but I guess there is a better way, something like
if score is really good, then learn this strongly
if score is slightly over average, then learn this
if score is slightly under average, then do not learn this
if score is really bad,then avoid this strongly
Can you give me a hint? Would be really nice.
I believe you are talking about reinforcement learning, your example about players play a game and return score is what exactly reinforcement learning is. Reinforcement learning works using punish and reward concept, the model will try to do something and when the model returns a good result, the model will get a reward, and otherwise, when the model returns a bad result, it will be punished. Perhaps you can do some research and google about it.
But in my opinion since you mentioned that you are quite new to machine learning and keras, you probably need to learn about supervised learning first. Your case about photo (x) and label (y) is all about supervised learning is.
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My friend is doing his thesis related to fish spawning in rivers, for this, he collects hours of data that he then analysis manually in Audacity and looks for specific patterns in the spectrograms that might indicate the sound of fish spawning.
Since he has days' worth of data I proposed a challenge to myself: to create an algorithm that might help him in detecting these patterns.
I am fairly new to Machine Learning, but a junior in programming and this sounds like a fun learning experience.
I identify the main problems as:
samples are 1 hour in length.
noise in the background (such as cars and the rivers)
Is this achievable with machine learning or should I look into other options? If yes which ones?
Thank you for taking the time to read!
the first step would be to convert the sound signals into features that machines can understand. Maybe look into MFCCs for that.
Given that you have an appropriate feature representation of your problem domain, the main thing to consider would be what kind of machine learning algorithm would you apply? Unless you would like to sit and annotate hours of data, naive supervised learning is out of the window.
I think your best bet would be to modify VAD (voice activity detection) algorithms or better yet, Speaker recognition/Identification modals.
You could also approach it by first having a complex enough representation that allows you to "see" the sound and comparing it with every frame in the test data of the specific length. Might be useful to check out DTW (Dynamic Time warping)
If you have not designed such modals before, it will be a bit difficult and might take quite a long time.
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I'm doing a project where I have data of 100 sensors and its cycles until it breaks. It shows a lot of characteristcs until its failure, and then shows it for the replacement sensor. With this data, I have to built a model where I can predict for how long the sensor will work until its failure, but only with a few data, not the full cycle. I have no idea what machine learning model is suitable for this.
The type of problem you are describing is known as survival analysis. A wide range of both statistical and machine learning methods are available to help you solve these type of problems.
What is great about these methods is it also allow you to use data points where the event you are interested in has not occur. In your example, it means you can possibly extend your dataset by including data from sensors which has not failed yet.
When you look at the methods I suggest you also spend some time examining how to evaluate these types of models, since the evaluation methods are also slightly different then in typical machine learning problems.
A comprehensive range of techniques is available at: http://dmkd.cs.vt.edu/TUTORIAL/Survival/Slides.pdf
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If you see another question with the same wording as this, please ignore, it has unnecessary code
I made a very basic chatbot/program in Python that simulates ordering from a restaurant. I was wondering if there is any way to use Natural Language Processing (NLP) to find out if two words are the same? For example, how can NLP find out that "I'm feeling bad" means the same thing as "I'm feeling horrible" ?
Actually, your question is quite complex. By calculating the distance between word you can measure the distance between two word in an Embedding space, as known as Word2Vec, using something like euclidean distance, cosine similarity, and so on. (you can download pre-trained word2vec such as googlenews)
However, you mention about the similarity between sentence "I'm feeling bad" and sentence "I'm feeling horrible" which in this case it is easy, you just compare these two sentence and find out that they only have 1 part that is different which is the word "horrible" and "bad". Simplest way, you can use a vocab that contain set of synonyms to solve this. This is what we called Rule-base system (I suggest this rule-base method, something like nested of if-else).
Things will get more complicate when the sentence structure is different, now you need to have some kind of algorithm to detect not only word but also structure. You may need something like WMD to measure similarity between sentence. Moreover, if you want to create a model that is not rule-base, you need tons of data, we called it parallel corpus which is just a pair of sentence that have similar meaning to train. For statistical method, you also need lots of data to calculate the probability but less than deep learning model.
Lastly, do not look down toward rule-base method, this is what APPLE SIRI is using right now, super huge rule base. you can also add some complexity to your rule base while you are improving your model. Hope this answer your question
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I have images of the following type
etc..
What would be the easiest way of identifying what piece it is and if it is black or white? Do I need to use machine learning or is there an easier way?
It depends on your inputs. If all of your data looks like this (nice and clean, with contours being identical, just background and color changes), you could probably use some kind of pixel + color matching and you could be good to go.
You definitely know that deep learning and machine learning only approximate function (functions of pixels in this case), if you can find it (the function) without using those methods (with sensible amount of work), you always should.
And no, machine learning is not a silver bullet, you get an image and you throw it into convolutional neural networks black-box magic and you get your results, that's not the point.
Sorry but deep learning might be just an overkill to recognize a known set of images. Use template matching!
https://machinelearningmastery.com/using-opencv-python-and-template-matching-to-play-wheres-waldo/
You could do this using machine learning (plan or convolutional neural nets). This isn't that hard of a problem, but you have to do manual work of creating proper dataset with lots of pictures.
For example: For each piece you need to create picture with white/black field color. And you have to do different combinations, different chess piece sets vs. different table color schema. In order to make the system more robust to color schema you can try different color channels.
There are lots of questions, will the pictures you test always be in same resolution? If they aren't then you should also take that into consideration when creating dataset.
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I am interested in learning the basics of scientific computing, but sadly in my daily life I don't encounter problems or situations in which I get to code something to solve a mathematical problem (Or any problem whatsoever, other than the basic programming things we are asked to do).
I know the basics of python and I'm not worried about it since I can always learn what I need on the go, but I need a resource (A book, website or mailing list will do) with problems and exercises I can use to start working with the kind of situations I could encounter if I decide to work in this in the future.
I've looked around but I haven't seen anything aimed for people knowing programming but not knowing the specific details of scientific computing, just advanced things I don't understand at the moment.
When I first started learning to program I used Project Euler. It is a series of challenging mathematical/computer programming problems that will require logical and mathematical insights to solve. Although mathematics will help you arrive at elegant and efficient methods, the use of a computer and programming skills will be required to solve most problems. The problems themselves are interesting, and they gradually increase in difficulty.
Be careful though - it can become quite addictive!
Here's the link: https://projecteuler.net
You might try [About Project Euler] (http://projecteuler.net/) and Problems
The problem are interesting and can be done using different languages. Since the problems have different complexities, and the different languages may allow better (or not as good) programming solutions, this would seem to be a good site to study.