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.

Multi-layer Perceptron

This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convolutional neural networks, Multi-layer perceptron

[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

Code for a simple MLP (Multi-Layer Perceptron)

The continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.

MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.

Indonesian Chatbot built by Multi Layer Perceptron(Neural Network)

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

⇨ The Speaker Recognition System consists of two phases, Feature Extraction and Recognition. ⇨ In the Extraction phase, the Speaker's voice is recorded and typical number of features are extracted to form a model. ⇨ During the Recognition phase, a speech sample is compared against a previously created voice print stored in the database. ⇨ The highlight of the system is that it can identify the Speaker's voice in a Multi-Speaker Environment too. Multi-layer Perceptron (MLP) Neural Network based on error back propagation training algorithm was used to train and test the system. ⇨ The system response time was 74 µs with an average efficiency of 95%.

ICCV2025-Learning Pixel-adaptive Multi-layer Perceptrons for Real-time Image Enhancement

Detecting Fraudulent Blockchain Accounts on Ethereum with Supervised Machine Learning

A Python module to train simple multi-layer perceptron neural networks using Levenberg-Marquardt training

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

Implementation of Multi Layer Perceptron in C

A Spiking Multi-Layer Perceptron

Trained a Multi-Layer Perceptron, AlexNet and pre-trained InceptionV3 architectures on NVIDIA GPUs to classify Brain MRI images into meningioma, glioma, pituitary tumor which are cancer classes and those images which are healthy into no tumor class.

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

Recognize Digits using Deep Neural Networks in Google Chrome live!

In this project we use RAVDESS Dataset to classify Speech Emotion using Multi Layer Perceptron Classifier

Transformer model based on Kolmogorov–Arnold Network(KAN), which is an alternative of Multi-Layer Perceptron(MLP)

This is a binary classification problem related with Autistic Spectrum Disorder (ASD) screening in Adult individual. Given some attributes of a person, my model can predict whether the person would have a possibility to get ASD using different Supervised Learning Techniques and Multi-Layer Perceptron.

Multi-layer perceptron model for photo-voltaic material properties prediction.