i want to implement the federated learning framework.
for finding a set of optimal hyperparameter values, How should I implement cross-validation procedure?
Should I train a traditional machine learning model or Federated learning process?
please guide me !!!! please
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I’m new to machine learning. I am working with a public dataset of medical images and Google’s AutoML vision. My goal is to create a multi-class classifier using single-label classification to diagnose different diseases based on labeled photos.
I was wondering if it was possible to obtain heat maps of regions in the photos that the learning algorithm relied on to make its prediction. This would make the algorithm more clinician-friendly and more understandable. In CNNs, I think this concept is called gradient-weighted class activation mapping (grad-cam).
Please let me know if this is possible with Google AutoML. Thanks a lot
Using sentiment analysis API and want to know how the AI bias that gets in through the training set of data and other biases quantified. Any help would be appreciated.
There are several tools developed to deal with it:
Fair Learn https://fairlearn.github.io/
Interpretability Toolkit https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability
In Fair Learn you can see how biased a ML model is after it has been trained with the data set and choose a maybe less accurate model which performs better with biases. The explainable ML models provide different correlation of inputs with outputs and combined with Fair Learn can give an idea of the health of the ML model.
We are looking at building recommender system for our brand-new Learning Management System. There are a bunch of users and items (learning modules) onboarded, but no ratings yet - typical cold start problem.
To begin with, we are thinking of using a simple item-based similarity using item attributes (tags, category, etc.) The idea is to switch to more robust collaborative filtering as the ratings start coming in.
Questions:
Is this a good approach? Is there a recommended ML pattern to handle such cold-start conditions?
To realise item-based similarity, which is the right algorithm? Say, cosine similarity. However, please note there is no "matrix". Should we try to use a standard ML algorithm or maybe roll our own?
Your approach is good. I would start with an unsupervised learning algorithm such as 'k-Nearest Neighbors classifier'. If your team doesn't know the first thing about ML, I recommend you to read this tutorial http://www.astroml.org/sklearn_tutorial/general_concepts.html . It uses python and a great library called scikit-learn. From there you could do Andrew's NG course (https://www.coursera.org/learn/machine-learning/) although it does not cover any recommendation systems.
I usually go with a Pearson Correlation algorithm (https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient) and that suffices me for my problems. The problem with this approach is that it is linear. I have read that the Orange data mining tool provides many correlation measures. Using it you could find which one is best for your data. I would advice against using your own algorithm.
There is an older question which provides further information on the matter: How can I implement a recommendation engine?
I am developing an application for analyizing the sentiment of the movie reviews (good/bad/neutral). The frontend will be C# dot net and backend will be MySQL.
The algorithm I am using is Genetic Algorithm and I am only directed by my guide that I need to use Rank Selection method.
I'm looking at how to approach this and if there are any existing source codes which I can refer.
Thanks in advance!
The best way is to use external API for sentiment analysis for example text2data.org
There is also free option
nltk.org
I'm studying Reinforcement Learning and reading Sutton's book for a university course. Beside the classic PD, MC, TD and Q-Learning algorithms, I'm reading about policy gradient methods and genetic algorithms for the resolution of decision problems.
I have never had experience before in this topic and I'm having problems understanding when a technique should be preferred over another. I have a few ideas, but I'm not sure about them. Can someone briefly explain or tell me a source where I can find something about typical situation where a certain methods should be used? As far as I understand:
Dynamic Programming and Linear Programming should be used only when the MDP has few actions and states and the model is known, since it's very expensive. But when DP is better than LP?
Monte Carlo methods are used when I don't have the model of the problem but I can generate samples. It does not have bias but has high variance.
Temporal Difference methods should be used when MC methods need too many samples to have low variance. But when should I use TD and when Q-Learning?
Policy Gradient and Genetic algorithms are good for continuous MDPs. But when one is better than the other?
More precisely, I think that to choose a learning methods a programmer should ask himlself the following questions:
does the agent learn online or offline?
can we separate exploring and exploiting phases?
can we perform enough exploration?
is the horizon of the MDP finite or infinite?
are states and actions continuous?
But I don't know how these details of the problem affect the choice of a learning method.
I hope that some programmer has already had some experience about RL methods and can help me to better understand their applications.
Briefly:
does the agent learn online or offline? helps you to decide either using on-line or off-line algorithms. (e.g. on-line: SARSA, off-line: Q-learning). On-line methods have more limitations and need more attention to pay.
can we separate exploring and exploiting phases? These two phase are normally in a balance. For example in epsilon-greedy action selection, you use an (epsilon) probability for exploiting and (1-epsilon) probability for exploring. You can separate these two and ask the algorithm just explore first (e.g. choosing random actions) and then exploit. But this situation is possible when you are learning off-line and probably using a model for the dynamics of the system. And it normally means collecting a lot of sample data in advance.
can we perform enough exploration? The level of exploration can be decided depending on the definition of the problem. For example, if you have a simulation model of the problem in memory, then you can explore as you want. But real exploring is limited to amount of resources you have. (e.g. energy, time, ...)
are states and actions continuous? Considering this assumption helps to choose the right approach (algorithm). There are both discrete and continuous algorithms developed for RL. Some of "continuous" algorithms internally discretize the state or action spaces.