pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.

Fake Image Detection Using Machine Learning

Gene2Vec: Distributed Representation of Genes Based on Co-Expression

PyTorch tutorial for learners

In this project is presented a simple method to train an MLP neural network for audio signals. The trained model can be exported on a Raspberry Pi (2 or superior suggested) to classify audio signal registered with USB microphone

Python package for Granger causality test with nonlinear forecasting methods.

Simple multilayer perceptron c++ implementation.

Multi class audio classification using Deep Learning (MLP, CNN): The objective of this project is to build a multi class classifier to identify sound of a bee, cricket or noise.

Developed a deep learning model that allows trading firms to analyze large patterns of stock market data and look for possible permutations to increase returns and reduce risk. Trained the model using a Multilayer Perceptron Neural Network on a vast set of features that influence the stock market indices. Performed technical analysis using historical stock prices and fundamental analysis using social media dat

Machine Learning Library, written in J

The goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.

EDUX is a developer friendly Java library for machine learning educational tasks

Detection of IoT devices infected by malwares from their network communications, using federated machine learning

Algorithms & Data Structures Guide

Java 23, SpringBoot 3.4.1 Examples using Deep Learning 4 Java & LangChain4J for Generative AI using ChatGPT LLM, RAG and other open source LLMs. Sentiment Analysis, Application Context based ChatBots. Custom Data Handling. LLMs - GPT 3.5 / 4o, Gemini Pro 1.5, Claude 3, Llama 3.1, Phi-3, Gemma 2, Falcon 3, Qwen 2.5, Mistral Nemo, Wizard Math

A trading bitcoin agent was created with deep reinforcement learning implementations.

Various implementations and projects on CNN, RNN, LSTM, GAN, etc

Tensorflow Examples

From linear regression towards neural networks...

algorithm for study: multi-layer-perceptron, cluster-graph, cnn, rnn, restricted boltzmann machine, bayesian network

A comparison between humans, neuroevolution and multilayer perceptrons playing Flapy Bird implemented in Python

Silver Medal Solution for Shopee - Price Match Guarantee competition on Kaggle

A Command-Line Program of Feedforward Neural Networks

A program that allows you to translate neural networks created with Keras to fuzzy logic programs, in order to tune these networks from a given dataset.

Image Encryption and Decryption using Neural Networks

Diabetes Mellitus (DM), commonly known as diabetes, is a group of metabolic disorders characterized by high blood sugar levels over a prolonged period. Artificial Intelligence in Medical Science refers to real-world medical domains, considered and discussed at the proper depth, from both the technical and the medical points of view. Data Science and Machine Learning is helping medical professionals make diagnosis easier by bridging the gap between huge data sets and human knowledge. We can begin to apply Machine L earning techniques for classification in a dataset that describes a population that is under a high risk of the onset of diabetes. Given the medical data we can gather about people, we should be able to make better predictions on how likely a person is to suffer the onset of diabetes, and therefore act appropriately to help. We can start analyzing data and experimenting with algorithms that will help us study the onset of diabetes.

face recognition with deep learning

Machine learning library for classification tasks

Realtime Fall Detection and Human Activity Recognition using Multilayer Perceptron Neural Network from gyroscope and accelerometer sensor sent from a ESP-32 Microcontroller

Finding explainable models to predict Formula 1 Qualifying Results