Face detection and recognition into 6 classes of some famous personalities.
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Updated
Aug 1, 2020 - Jupyter Notebook
Face detection and recognition into 6 classes of some famous personalities.
The softmax function or normalized exponential function is a generalization of the logistic function to multiple dimensions. In this example (X is weight, Y is height) where (0,0) is top left corner.
Using advanced deep learning techniques on the MNIST dataset. Over 98% validation set accuracy.
including Softmax Regression, Neural Network (regularized), KNN, LDA
Spring 2021 Machine Learning (CS 181) Homework 2
This repository is a compilation of machine learning algorithms implemented by me on differnet datasets and I'm currently working on it. The algorithms are categorized based on the types of data they are designed to handle and some of the codes are just a basic descriptions about the algorithms.
Deep Learning basics in Python using NumPy, PyTorch, and TensorFlow/Keras: linear regression, softmax regression, multilayer perceptron, etc.
Work on Bayesian growth mixture models including hidden Markov chains and softmax regressions for representing latent class memberships.
A softmax regression model to classify images as neutral or smiling by different facial expressions.
Tensorflow simple project using MNIST dataset and softmax-regression
CS224n : Natural Language Processing with Deep Learning Assignments, Winter 2017, Stanford University.
Support vector machines flexible framework
😊 TensorFlow Hello World Program
Implementation from scratch supervised and unsupervised ML algorithms.
Machine Learning Model to predict student graduation grade
This project has a comprehensive exploration of two key topics: Softmax Regression and Contrastive Representation Learning. The dataset used for this project is the CIFAR-10 dataset, which can be accessed by link given below
SAGA with Perturbations
A list of machine leaarning tasks carried out in a set of series spread across 3 Colab Notebooks
This project utilizes neural networks to recognize handwritten digits (0-9) through multiclass classification, employing ReLU activation and Softmax function for accurate predictions.
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