Neural networks school project on headlines categorization using deep learning and word embedding.
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Updated
Aug 21, 2021 - MATLAB
Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing.
Neural networks school project on headlines categorization using deep learning and word embedding.
MATLAB code of Feedforward Deep Neural Network (FDNN) for Battery State of Charge (SOC) Estimation in Battery Electric Vehicle (BEV)
Scaffold codes for feedforward neural network and autoencoders.
Matlab - plot deep neural network architectures to scale
Implementing Deep Learning Neural Network using Octave and Matlab
Solutions to all the homework assignments with my experiments as well
Ứng dụng nhận diện khuân mặt FaceID
Alzheimer's Disease Classification Research performed at University of Cagliari in 2020.
A framework for more flexible structure of neural networks with auto-differentiation.
This repository holds my completed Octave/Matlab code for the exercises in the Stanford Machine Learning course, offered on the Coursera platform.
Machine learning project, classifying different plants and handwritten numbers using a set of neurons (light-weight deep learning)
Deep neural network processing of double electron-electron resonance data
Neural Network
Repository for my paper with title "Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons."
The Kaggle Digit Recognizer Competition
Coursework - Machine Learning - Stanford - Andrew Ng (As Taught on Coursera)
This is the recent work of my on the importance and application of mathematical function around its Hilbert function theory on artificial intelligence algorithms. The main motivation was the desire of improving the convergence rate and learning rate of various learning algorithms via Generalized Gaussian Radial Basis Function.