full visualization of netflix and movielense datasets with 89% accuraccy item2vec

Breast Cancer Classification with Logistic Regression

Digital Image Processing Course | Home Works Design| Fall 2021 | Dr. MohammadReza Mohammadi

Evaluated the word vectors learned from both nce and cross entropy loss functions using word analogy tests

In the project, the aim is to generate new song lyrics based on the artist’s previously released song’s context and style. We have chosen a Kaggle dataset of over 57,000 songs, having over 650 artists. The dataset contains artist name, song name, a link of the song for reference & lyrics of that song. We tend to create an RNN character-level la…

Comparison of common loss functions in PyTorch using MNIST dataset

Neural network-based character recognition using MATLAB. The algorithm does not rely on external ML modules, and is rigorously defined from scratch. A report is included which explains the theory, algorithm performance comparisons, and hyperparameter optimization.

A classifier to differentiate between Cat and Non-Cat Images

Random experiments with scikit-learn.

In this notebook, we want to create a machine learning model to accurately predict whether patients have a database of diabetes or not.

Word2Vec implementation using tensorflow

I implemented a CNN to train and test a handwritten digit recognition system using the MNIST dataset. I also read the paper “Backpropagation Applied to Handwritten Zip Code Recognition” by LeCun et al. 1989 for more details, but my architecture does not mirror everything mentioned in the paper. I also carried out a few experiments such as adding…

In this X-ray classification assignment, we built a deep learning model to classify chest X-ray images into "nofinding" and "effusion" classes. We tackled challenges like data augmentation, imbalanced classes, and used weighted cross-entropy to improve model performance. The goal was to identify abnormalities with high accuracy.

Predict whether the cancer is benign or malignant using logistic regression model.

Machine Learning: Regression and Classification. Andrew presents a course in introduction to machine learning, with practice in regression and classification for the first and second course, and the third course focuses on recommender systems and reinforcement learning.

Code for the Paper : NBC-Softmax : Darkweb Author fingerprinting and migration tracking (https://arxiv.org/abs/2212.08184)

Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".

Multiclass Classification using Softmax from scratch without any famous library like Tensorflow, Pytorch, etc.

A simple demo in which a Neural Network is trained to recognize hand-written digits