Brian Wade (@brianwade1)
github

brianwade1

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  • Stars
    star
    89
  • Global Rank 225,995 (Top 8 %)
  • Followers 14
  • Following 5
  • Registered almost 5 years ago
  • Most used languages
    MATLAB
         70.0 %
    Python
         20.0 %
    Jupyter Notebook
         10.0 %

Top repositories

1

quadcopter_with_PID_controller

Quadcopter dynamics simulation with two proportional–integral–derivative (PID) controllers that adjust the motor speeds of the quadcopter and enable control of position and orientation.
Python
27
star
2

Ballistics_Simulation

MATLAB Simulation of a six degree of freedom (6-DOF) ballistic flight of a 120mm mortar round.
MATLAB
22
star
3

actor_critic_quadcopter

Advantage Actor-Critic (A2C) reinforcement learning agent used to control the motor speeds on a quadcopter in order to keep the quadcopter in a stable hover following a random angular acceleration perturbation between 0-3 degrees per second in each of the control axes: pitch, roll, and yaw.
MATLAB
16
star
4

Bayesian_Opt_NN_Building_Heating_Loads

This project uses Bayesian Optimization to find the optimal hyperparameters for a fully-connected feed-forward neural network used to estimate the heating load on a building given eight different input features.
MATLAB
8
star
5

actor_critic_quadcopter_continuous

MATLAB
5
star
6

sonar_mine_NN_v_RF

This project uses a grid search optimization and then trains two models: a fully connected feed-forward neural network and a random forest to classify the returns from a sonar examining simulated mines (metal cylinders) and standard rocks (false mines).
MATLAB
5
star
7

ReinforcementLearning_ActorCritic_Practice

Actor-Critic model trained using value advantages on the OpenAI Gym CartPole-V0 environment.
Python
2
star
8

Bayesian_Optimization_NN_HeartFailure

This project predicts the likelihood for heart failure. The project takes place in three parts: exploratory data analysis (EDA) and data preparation, the creation of three initial binary classification models including logistic regression, random forests, and a neural network. Then, the hyperparameters of the neural net were optimized using Bayesian Optimization.
Jupyter Notebook
2
star
9

ML_model_shootout_MATLAB

Grid search for hyperpameters on multiple supervised learning models to include neural networks, random forests, and tree ensemble models.
MATLAB
1
star
10

MATLAB_Regression_Results_Statistics_Plots

The function calculates fit statistics and creates charts to understand how well a supervised learning regression model was able to predict target data.
MATLAB
1
star