TensorFlow and Deep Learning Tutorials

Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Official implementation of MLP Singer: Towards Rapid Parallel Korean Singing Voice Synthesis (IEEE MLSP 2021)

An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.

[ICLR 2023] MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization

Stock price trend prediction with news sentiment analysis using deep learning

我的笔记和Demo，包含分类，检测、分割、知识蒸馏。

Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine

Neuron class provides LNU, QNU, RBF, MLP, MLP-ELM neurons

Detecting Fraudulent Blockchain Accounts on Ethereum with Supervised Machine Learning

Deep Convolutional Neural Networks for Raman Spectrum Recognition. (RRUFF dataset)

C++ demo of deep neural networks (MLP, CNN)

Recognize Digits using Deep Neural Networks in Google Chrome live!

Assemble an efficient interpretable machine learning workflow.

Minerva project includes the minerva package that aids in the fitting and testing of neural network models. Includes pre and post-processing of land cover data. Designed for use with torchgeo datasets.

EE7207 Neural & Fuzzy Systems

Pixel based classification of satellite imagery - feature generation using Orfeo Toolbox, feature selection using Learning Vector Quantization, CLassification using Decision Tree, Neural Networks, Random Forests, KNN and Naive Bayes Classifier

Image Classification on CIFAR-10 Dataset using Multi Layer Perceptrons in Python from Scratch.

3 versions of Perceptron: normal Perceptron; Perceptron GUI; Multilayer Perceptron GUI, back propagation，感知机，感知器，BP 神经网络，反向传播，多层感知器，多层感知机

Deep Learning Models implemented in python.

MetaPerceptron: A Standardized Framework For Metaheuristic-Driven Multi-layer Perceptron Optimization

This is the repository containing machine learning and deep learning projects, as well as some presentation slides on these topics.

A Multi Layer Perceptron (MLP) Artificial Neural Network (ANN) Framework Developed in C for Machine Learning (ML) and Deep Learning (DL)

A from-scratch neural network and transformers library, with speeds rivaling PyTorch

Constructed a MLP Regression, along with data analysis in order to generalize a robust model for predicting the compressive strength of concrete.

In this project, we will explore the implementation of a Multi Layer Perceptron (MLP) using PyTorch. MLP is a type of feedforward neural network that consists of multiple layers of nodes (neurons) connected in a sequential manner.

A multilayer perceptron (MLP) is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate outputs. An MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one.

Tour of Machine Learning Algorithms for Binary/Multiclass Classification

Jupyter notebook that outlines the process of creating a machine learning predictive model. Predicts the peak "Wins Shared" by the current draft prospects based on numerous features such as college stats, projected draft pick, physical profile and age. I try out multiple models and pick the best performing one for the data from my judgement.