This repo contains all the notebooks mentioned in blog.

Allstate Kaggle Competition ML Capstone Project

Cambridge UK temperature forecast python notebooks

Some useful examples of Deep Learning (.ipynb)

MLP Regression model to predict quantitative data

Implementing neural networks from scratch for a deeper understanding of concepts, featuring a Jupyter notebook with derivative-based implementations.

Notebooks for "AI 응용-음성"

Curated collection of notebooks and code files I have worked on while learning a wide range of data science subfields, such as Reinforcement Learning, Natural Language Processing, Deep Neural Networks, Genetic Algorithms, etc. Some of these are accompanied by a pdf and/or article.

This repository contains my personal notes and Jupyter notebooks on Deep Learning Specialization course at the university Haute-Alsace.

🧠Training a MLP neural network using Jupyter Notebook and Python.

This notebook shows you one way to add batch normalization to a neural network built in PyTorch.

MLP classification model

Repository containing TensorFlow tutorial Notebooks

Some coding stuff from various machine learning books

This repository contains a notebook to take you through step by step guide on recognizing handwritten digits using a Multi-Layer-Perceptron(MLP)

🧠Training many MLP neural networks with Juptyer Notebook and Python

This repository contains a Jupyter Notebook file equipped with code for identifying the position of digits in a Sudoku puzzle and solving it. The notebook implements algorithms for Sudoku puzzle solving, offering a practical tool for enthusiasts and learners alike to explore and understand Sudoku solving techniques. 2-2 METHODS FOR BOTH TASKS.

In this project we will convert the problem we solved using regression notebook into a classification problem instead. So we will assign each wine quality rating to a different class. So quality of 0 will belong to a different class to quality of 1. The biggest difference between a regression solution and multi-class classification in terms of i…