model-complexity

There are 16 repositories under model-complexity topic.

  • Decadz/Genetic-Programming-with-Rademacher-Complexity

    Python code for the GP-RC algorithm presented in "Genetic Programming with Rademacher Complexity for Symbolic Regression" (CEC-2019). Paper Link: https://ieeexplore.ieee.org/document/8790341

    Language:Python14104

  • 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 Notebook4402

  • PAL-UH/transferAL

    Domain Adaptation by Transferring Model-Complexity Priors Across Tasks Paper Experiments

    Language:MATLAB3401

  • rexxy-sasori/nnutils

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

    Language:Python2100
  • Predicting-Boston-Housing-Prices

    sushantdhumak/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 Notebook2105

  • fl0wbar/rnn_clv

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

    Language:Python1200

  • liyijin-data-PM/Applied-Machine-Learning-in-Python-Coursera

    A wide variety of supervised and unsupervised machine learning methods using the scikit-learn library

    Language:Jupyter Notebook1201

  • 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:HTML1202

  • prateekiiest/boston_housing

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

    Language:HTML1324

  • ashutoshtiwari13/BostonHousing-Predictor

    Predicting Boston Housing Prices using Machine Learning

    Language:Jupyter Notebook10

  • 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:HTML001

  • enesozi/ML-course-HW1

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

    Language:MATLAB

  • Faroja/Machine-Learning-Practice-5

    Practice Machine Learning Model Complexity in Linear Model

    Language:Jupyter Notebook10

  • 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:HTML10

  • Ohara124c41/MLND-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 Notebook20

  • rebeccak1/boston-housing

    Predicting Boston Housing Prices

    Language:Jupyter Notebook20