Questions tagged [machine-learning]
For questions related to machine learning (ML), which is a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data). ML is usually divided into supervised, unsupervised and reinforcement learning. Deep learning is a subfield of ML that uses deep artificial neural networks.
2,398 questions
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Is tensorflow broken?
I'm currently learning how to use JAX (I'm very new to machine learning), and I've been trying to replicate code examples from Grigory Sapunov's book Deep Learning with JAX, which is copyrighted in ...
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Understanding Why TD Learning Has Lower Variance Despite Using an Estimated Value
In Temporal Difference (TD) learning, the value function is updated using its own estimate, following the rule:$V (S_t) \leftarrow V (S_t) + \alpha[R_{t+1} + \gamma V (S_{t+1}) − V (S_t)]$. It's often ...
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Why do my DNN convergence graphs behave differently on linear vs. dB scales?
I'm working on a deep neural network (DNN) and using the Adam optimizer to train it by learning parameters through backpropagation. My goal is to minimize the objective function. I’ve plotted the ...
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What are some notable connections between quantum mechanics and machine learning?
I have recently started to learn about machine learning, and I feel like there is a lot of connection to quantum mechanics in it. For example, this idea of the predictin through layers of neurons look ...
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How can the exact same model give different confusion matrices for the test dataset and the entire dataset?
I have recently implemented a simple artificial neural network with 1 hidden layer. I split my data using train_test_split and I end up with the following confusion matrix in my test set. ...
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Create a global model from local models
Current Scenario: So I have a task at hand. I have a data which has timestamp, org_id, no_of_calls_on_premise, no_of_calls_cloud, bw_savings. This is aggregated data on a daily basis (Also i have ...
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What are the best practices for using Amazon SageMaker to develop, train, and deploy ML models for beginners?
I'm a developer with some experience in machine learning using local environments (e.g., scikit-learn, TensorFlow) but new to Amazon SageMaker and cloud-based platforms. I want to understand how to ...
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Required background for thorough understanding of Causal ML research papers?
I'm interested in pursuing research in the intersection of causal inference and machine learning, particularly on causal discovery and causal representation learning. Through my exploration so far, I ...
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Learning curve behaviors across double descent regimes
I am learning about double descent phenomenon from here: https://www.di.ens.fr/~fbach/learning_theory_class/lecture9.pdf I was asking myself: When training a system, how can we know in which regime ...
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Is it normal to get zero error for a class in a binary classification problem using Genetic Programming?
I am trying to do highly unbalanced binary classification using Linear Genetic Programming to detect a certain spoken word. I use mel coefficients as features. The instructions include basic ...
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How to use the KL divergence in practice
I have been studying the KL divergence from Bishop's PRML book, and I feel like I have grasped the intuition behind it. However, I don't understand how one could use it when approximating an unknown ...
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What are current research domains fit my ideas in this proposal?
I'm new in initiating a research of my interest. My Very Initial Draft of the Research Proposal My internship experience has influenced me greatly, from my career path to my research interests. By ...
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Does Gödel’s Incompleteness Theorem have implications for AI learning in non-Euclidean spaces?
Gödel’s Incompleteness Theorem revealed that formal systems have inherent limitations—there will always be true statements that cannot be proven within a given system. This has profound implications ...
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Are we really misunderstanding VC theory as arXiv:2205.15549 suggests?
arXiv:2205.15549 claims that the machine learning community misunderstood VC (Vapnik–Chervonenkis) theory and VC-theoretical understandings are sufficient to understand the double descent phenomenon. ...
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Machine Learning model design setup
I'm looking for the best design based on the following requirements. I'd appreciate an explanation of a possible high level route I can follow. I need to deploy an Image classifier that takes in an ...
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