AI-Face-Mask-Detector
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Updated
May 13, 2024 - Python
AI-Face-Mask-Detector
From linear regression towards neural networks...
Comparison of the Momentum, RMSprop, and Adam optimization methods to GD and SGD for machine learning models using synthetic data to evaluate convergence speed and accuracy.
A research project on enhancing gradient optimization methods
"Simulations for the paper 'A Review Article On Gradient Descent Optimization Algorithms' by Sebastian Roeder"
This project aims to create a deep learning model for classifying fashion items using the Fashion MNIST dataset. Below, you can find the steps of the project and the results obtained.
Using Transfer-learning and fine-tuning multiple models (Mobilenetv2, ResNet50, VGG, and building a CNN model from scratch) and comparing results to build a live facial emotion classification from your camera. we use the FER-2013 dataset for emotion classification to train a deep neural network to classify 7 emotions
Siamese Neural Network used for signature verification with three different datasets
Constructed time series analysis and recurrent neural networks in GDP prediction under the global pandemic. Grasped data from remote data access, added in employment rate, cases of infection and indices for better representation, evaluation and optimization.
Notes about LLaMA 2 model
Data Structures, Algorithms and Machine Learning Optimization
Implemented optimization algorithms, including Momentum, AdaGrad, RMSProp, and Adam, from scratch using only NumPy in Python. Implemented the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimizer and conducted a comparative analysis of its results with those obtained using Adam.
Beginner Machine Learning - submission task for beginner Machine Learning class
This is an implementation of different optimization algorithms such as: - Gradient Descent (stochastic - mini-batch - batch) - Momentum - NAG - Adagrad - RMS-prop - BFGS - Adam Also, most of them are implemented in vectorized form for multi-variate problems
Object recognition AI using deep learning
A collection of various gradient descent algorithms implemented in Python from scratch
This is the implementation of neural network with few hidden layers. These implementation is inspired by the course I took on Coursera with deeplearning.ai.
Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".
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