Build logistic regression, neural network models for classification
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Updated
Jan 31, 2019
Build logistic regression, neural network models for classification
[BMVC'23 Oral] Offical repository of "Rethinking Transfer Learning for Medical Image Classification"
Source code for the numerical experiments presented in the paper "Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks".
In recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied…
Libreria didattica per la creazione, addestramento e test di reti neurali fino a tre strati in linguaggio C
Predicting if a mushroom is edible or poisonous with a shallow neural network with Keras and TensorFlow 2.
[NeurReps 2024, TMLR 2025] Can Kernel Methods Explain How the Data Affects Neural Collapse?
Notebooks of programming assignments of Neural Networks and Deep Learning course of deeplearning.ai on coursera in August-2019
Human Data Analytics (Optional Project)
Comparative Analysis of Activation Functions in Shallow Neural Networks for Multi-Class Image Classification Using MNIST Digits and CIFAR-10 Datasets with Fixed Architectural Parameters
Deep learning Specialization on Coursera
High-throughput detection and enumeration of tumor cells in blood using Digital Holographic Microscopy (DHM) and Deep Learning.
Logistic Regression Implementations - ML, Shallow NN and Enhanced Deep Neural Network for Structured and Unstructured Data Classification
study of scene classification with different MLP layer types
A shallow CNN model that is trained on X-ray chest images with preprocessing step of adaptive histogram equalization.
In this project, we propose a cervical cancer detection and classification system using CNNs . We employ transfer learning and fine-tuning for enhanced performance. Classifiers like ELM and AE are added to increase the efficiency.
A Python-based Machine Learning repository for the purpose of developing and testing a type of Shallow Deep Networks.
Challenge of shallow neural network approximation with one-dimensional input.
Design of an one hidden layer neural network using numpy only,
Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.
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