Machine Learning and Data Science

How to rewrite a jax model with stacked parameters

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using Flux function stacked_model(params, x) y = x for p in params y = p(y) end return y end params = [Dense(10, 10, relu) for _ in 1:5] x = rand(10) y

Need help implementing large tensor factorization in julia

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using TensorOperations function large_tensor_factorization(tensor) # implementation code here end tensor = # input tensor result = large_tensor_factorization(tensor) Option 1: Using TensorOperations package The first option to implement large tensor factorization in Julia

Poisson regression in turing

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Option 1: Using the GLM package The GLM package in Julia provides a convenient way to perform Poisson regression. To solve the given problem, we can use the GLM package to fit

Automatic differentiation

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Automatic differentiation is a powerful technique used in numerical optimization and machine learning. It allows us to compute derivatives of functions with respect to their inputs efficiently and accurately. In this article,

Predict impact locations of astroid 2006qv89

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Astroid 2006qv89 is a celestial object that has been of interest to astronomers due to its potential impact with Earth. Predicting the impact locations of this astroid is crucial for assessing the

Clustering and distance calculation in julia

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Clustering and distance calculation are common tasks in data analysis and machine learning. In Julia, there are several ways to solve this problem efficiently. In this article, we will explore three different

How to create a neural network with multiple output layers julia flux

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Creating a neural network with multiple output layers in Julia using Flux can be achieved in different ways. In this article, we will explore three different options to solve this problem. Option

How to use deepqlearning in julia for very large states

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Deep Q-Learning is a popular reinforcement learning algorithm that can be used to solve problems with large state spaces. In this article, we will explore three different ways to use deep Q-learning

Dynamic time warping in julia

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Dynamic time warping (DTW) is a popular algorithm used in time series analysis to measure the similarity between two sequences. In Julia, there are several ways to implement DTW. In this article,

Hyperbolic deep learning

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Hyperbolic deep learning is a fascinating field that combines the power of deep learning with hyperbolic geometry. In this article, we will explore different ways to solve a Julia question related to

Category: Machine Learning and Data Science

How to rewrite a jax model with stacked parameters

Need help implementing large tensor factorization in julia

Poisson regression in turing

Automatic differentiation

Predict impact locations of astroid 2006qv89

Clustering and distance calculation in julia

How to create a neural network with multiple output layers julia flux

How to use deepqlearning in julia for very large states

Dynamic time warping in julia

Hyperbolic deep learning

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