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Neural-Networks-Machine-Learning

Notebooks in the area of Neural Networks and Machine learning. All the notebooks in this repo should have an open in colab button at the top of the notebook.

Note not all of these notebooks are finished!

Table of Contents

Gradient Descent

  • Notebook discussing the basics of gradient descent

  • Notebook discussing a more in depth example with gradient descent

Linear Regression

  • Notebook going over how to do linear regression using gradient descent by hand

  • Notebook going over how to do linear regression using Least Squares Method along with using matrix theory and the normal equation

  • Notebook showing the different packages out there that can do linear regression. There are of course other packages that could do it, but this shows the 3 most popular

  • Logan's notebook for linear regression

Logistic Regression

  • Notebook going over how to do logistic regression using gradient descent by hand

  • Notebook going over how to do logistic regression using PyTorch

  • Logan's notebook for logistic regression

PCA

  • A Notebook showing how PCA works using a handmade example

  • Shows how to do PCA using packages and shows a use case to understand how PCA can be useful

XOR examples

  • Covers what the super position theory is and gives an example

Convolutional Neural Network

  • An Introduction notebook to what a Convoluational Neural Network or CNN is

  • Example of how to code a CNN directly from PyTorch

  • Another example of how to code a CNN

  • An Example of how to use a CNN on a time series

Long Short Term Memory Networks

  • Introduction to what an LSTM is

  • Advanced example of how to use a LSTM network

Other Network Structures

  • An introduction notebook over autoencoders

  • An example notebook over Variational Autoencoder

  • Another example notebook over Variational Autoencoder

  • An introduction notebook over Recurrent networks

  • An example of how to use a RRN network

  • An example how to make a gan network

  • An example how to make a gan network

  • Notebook over random forest trees

Stats review

  • A notebook reviewing those two stats topics

  • A notebook reviewing those two stats topics

  • A notebook reviewing covariance

  • A notebook reviewing maximum likelihood estimation (MLE)

  • A notebook showing how to generate different distributions and small details about each distribution

Control systems

  • Covers what the super position theory is and gives an example

  • Gives an example of summed impulse response vs np convolve function

  • An example notebook showing the different types of simulus to a system

  • Shows an example of different simulus to a first and second order system

  • shows the differences for the two types of inputs when modeling an input

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collect of notebooks in the area of neural networks and machine learning

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