You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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.
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
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 histo…
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.
Realtime Fall Detection and Human Activity Recognition using Multilayer Perceptron Neural Network from gyroscope and accelerometer sensor sent from a ESP-32 Microcontroller
Multilayer neural network framework implementation, used for classification and regression task. Can use multiple activation functions with backpropagation based on autograd library. Contains polynomial activation function for regression task.
A Multilayer Perceptron from scratch using NumPy. Offers almost all basic functionalities . Suitable for classification and regression tasks. 一个用NumPy从零实现的多层感知机。提供几乎所有基本功能。适用于分类和回归任务。
This repository has all of the programming assignments in the Foundations of Artificial Intelligence class at the University of Southern California in the Fall 2021 semester.
SDSS telescopes have captured over 40 TB worth of galaxy images and classification of these images is the first step towards obtaining a deeper understanding of physical processes within them, star formation, and the nature of the universe. Since we could not find an easily accessible dataset for galaxy classification, we compiled a dataset for …