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Topic: "model-complexity"

Decadz/Genetic-Programming-with-Rademacher-Complexity

Code for the GP-RC algorithm presented in "Genetic Programming with Rademacher Complexity for Symbolic Regression" (IEEE CEC2019). Paper Link: https://ieeexplore.ieee.org/document/8790341

Language: Python - Size: 35.1 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 11 - Forks: 4

sushant1827/Predicting-Boston-Housing-Prices

Machine Learning Nano-degree Project : To assist a real estate agent and his/her client with finding the best selling price for their home

Language: Jupyter Notebook - Size: 160 KB - Last synced at: 17 days ago - Pushed at: over 5 years ago - Stars: 3 - Forks: 5

dhingratul/Model-Compression

Reduce the model complexity by 612 times, and memory footprint by 19.5 times compared to base model, while achieving worst case accuracy threshold.

Language: Jupyter Notebook - Size: 11 MB - Last synced at: about 2 years ago - Pushed at: about 7 years ago - Stars: 3 - Forks: 2

rexxy-sasori/nnutils

Pipeline for training and evaluating CNNs as well as analyzing layerwise computational complexity

Language: Python - Size: 77.1 KB - Last synced at: 27 days ago - Pushed at: about 3 years ago - Stars: 2 - Forks: 0

Ohara124c41/MLND-Predicting_Boston_Housing_Pricing

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Language: HTML - Size: 820 KB - Last synced at: 12 months ago - Pushed at: almost 7 years ago - Stars: 1 - Forks: 2

prateekiiest/boston_housing

Udacity Machine Learning Nano degree Program. Project Predicting House prices in Boston

Language: HTML - Size: 1.18 MB - Last synced at: about 2 months ago - Pushed at: about 7 years ago - Stars: 1 - Forks: 4

fl0wbar/rnn_clv

Compute Lyapunov exponents and Covariant-Lyapunov-Vectors of an RNN update trajectory

Language: Python - Size: 53.7 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

Faroja/Machine-Learning-Practice-5

Practice Machine Learning Model Complexity in Linear Model

Language: Jupyter Notebook - Size: 61.5 KB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

ashutoshtiwari13/BostonHousing-Predictor

Predicting Boston Housing Prices using Machine Learning

Language: Jupyter Notebook - Size: 1.34 MB - Last synced at: about 1 year ago - Pushed at: about 6 years ago - Stars: 0 - Forks: 0

Ohara124c41/MLND-Customer_Segments Fork of nd009/creating_customer_segments

Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.

Language: Jupyter Notebook - Size: 768 KB - Last synced at: 12 months ago - Pushed at: over 6 years ago - Stars: 0 - Forks: 0

miguelangelnieto/Predicting-Boston-Housing-Prices

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Language: HTML - Size: 216 KB - Last synced at: about 2 years ago - Pushed at: about 7 years ago - Stars: 0 - Forks: 0

enesozi/ML-course-HW1

Bias/Variance dilemma, cross-validation and work on Iris Data Set from UCI Machine Learning Repository

Language: Matlab - Size: 95.7 KB - Last synced at: about 1 year ago - Pushed at: over 7 years ago - Stars: 0 - Forks: 0

rebeccak1/boston-housing

Predicting Boston Housing Prices

Language: Jupyter Notebook - Size: 109 KB - Last synced at: 7 months ago - Pushed at: almost 8 years ago - Stars: 0 - Forks: 0

auriml/model_evaluation_exercise

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Language: HTML - Size: 141 KB - Last synced at: over 1 year ago - Pushed at: about 8 years ago - Stars: 0 - Forks: 1