This repository is for a project that involves the construction of a basic neural network (MLP), and the performance comparison against the commonly used libraries (Py Torch and Tensorflow Keras)

Machine learning from scratch in Unity - HS Thesis

The final project is mainly based on convolutional neural networks and deep learning built with TensorFlow, and developed both LSTM and shallow/deep CNN.

This repository contains the exercise files in the LinkedIn Learning course "Neural Networks and Convolutional Neural Networks Essential Training" by Jonathan Fernandes.

Breast Cancer Classifier

CIFAR-10 is an established computer-vision dataset used for object recognition. It is a subset of the 80 million tiny images dataset and consists of 60,000 32x32 color images containing one of 10 object classes, with 6000 images per class. Using this we develop a model that can classify images

Neural networks are a type of machine learning algorithm modeled after the structure and function of the human brain. They are used for tasks such as image and speech recognition, natural language processing, and decision making. Neural networks consist of layers of interconnected nodes, called artificial neurons, that process information

Neural Network Library for Python (under construction)

Basic neural network built using Python and Numpy. Created to better understand neural networks.

Use neural networks and deep learning models to make predictions about a charity funding company appropriately allocating funds.

A collection of code written while working through the book: 'Neural Networks from Scratch'.

Resources for Designing and Utilizing Neural Networks

Time series forecasting using NN models.

🤖 Artificial intelligence, deep learning and neural networks with Python & PyTorch

Enhasing DOT image using Deep neural networks

This is an activity in Neural Networks and Deep Learning Models using TensorFlow and Pandas libraries in Python to preprocess datasets and create a predictive binary classifier. Topics

Used machine learning and neural networks, particularly TensorFlow, to analyze a CSV of 34,000+organizations to predict funding success, requiring skills in deep machine learning, neural networks, pandas, and Python.

A small repository showcasing neural networks implemented from scratch in NumPy for educational purposes.