🧠 Deep learning algorithms implemented from scratch using only NumPy — no frameworks, just math, backpropagation, and fundamentals.
From Markov Fields to Deep Belief Networks theory and experimentation on Google Landmark Recognition.
Cognition and Computation Course Project
Utilisation de modèles génératifs comme tâche prétexte pour pré-entrainement de DNN pour classification.
Makine Öğrenmesi dersi kapsamında geliştirilen Derin Öğrenme uygulamaları. CNN, RNN, DBN, RBM ve Autoencoder mimarileri
A repository for generating synthetic data (images) using various DL/ML models.
This repository is dedicated to my collaboration in the "AUTOMOTIVE" Project. This project's objective is to development automatic face image/video-based drowsiness recognition.
A framework that focuses on using bayesian and Dynamic Bayesian Networks to perform Learning from observation on Discrete Domains
A web app for training and analysing Deep Belief Networks
ECG Classification of Normal and Abnormal with GB-DBN Model (pytorch)
An pytorch implementation of Deep Belief Network with sklearn compatibility for classification. The training process consists the pretraining of DBN, fine-tuning as an unrolled autoencoder-decoder, and supervised fine-tuning as a classifier.
Uncover the secrets of deep learning with FashionDBN - implementing PyTorch's Deep Belief Network for accurate image classification and beyond.
A version of the learnergy package to deal with video datasets
Simple Keras-inspired DeepLearning Framework implemented in Python with Numpy backend: MLP, CNN, RNN, RBF, SOM, DBN...
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