Machine Learning Resources

ℹ️ Introduction:

A personal collection of Machine Learning / Data Science / AI resources. This collections consists of papers, blog posts, tutorials and also self-trained models or reimplementations of widespread machine learning algorithms.

I develop most mockups inside of this repository and also prepare blog posts about the topics collected in this repository. Feel free to try out some of my code examples or read some of the resources which are included in this repository!

Resources about Scalability of big systems:

• Click

📝 Blog:

This repository also includes blog posts which I am working on and also blog posts which were already released.

Currently working on:

• Using Neural Networks to learn some predictions for political contexts

Some finished blog posts can be found here:

• Text Prediction using Bigrams and Markov Models

• Text Generation using Bigrams

There are also live demos of the code snippets from my blog posts. Check out:

• Text prediction demo

• Text generation demo

🔧 Implementation:

Interested in how some principles from Machine Learning can be implemented without any frameworks? Then this section is your place to be.

Click the following link to find implementations:

click here

Text and Speech:

• text-prediction

• auto-completion like on android

• text-generation

Models:

• training models to cluster political parties and decide on their political orientation

🤯 Examples:

Projects:

Some projects are already included in this repository. Projects which are suffixed with a (*) are still being implemented.

Implementations:

• Text prediction using N-grams

🤯 ML5:

In this section I am going to use ML5 (built on Tensorflow) to try some Machine Learning concepts.

Current projects:

• image classification using ml5

• video detection using ml5

• knn-classification using 2d data

Click the following link to find examples:

click here

Tensorflow.js:

Current projects:

• XOR

• x_squared(*)

Click the following link to get to my Tensorflow.js projects

click here

Background Subtraction:

click here

Papers:

AI papers in collections:

• Implementations of annotated deep learning papers!

• OCR papers

• Deep learning paper implementations

Papers which I read or plan to read:

• https://www.sciencedirect.com/science/article/abs/pii/S0925231219313803

• https://arxiv.org/abs/1902.02771

• https://www.sciencedirect.com/science/article/pii/S2666827021000633#fig2

• Perceptrons, Rosenblatt 1958

Blog Posts:

• Gabor Filters

• Interesting Blog

• About dimensionality reduction

• More about dimensionality reduction (MNIST)

• Visualization of MNIST

• Basic explanation of NN’s

• Dropout in CNN’s

• Recommendation systems

• More on recommendation systems

• Quantum Neural Networks

• tfjs models

• Impacts of Background Removal on Convolutional Neural

• Networks for Plant Disease Classification In-Situ

• Models for strings