Gesture Recognition by CNN created using Networks Library created by me.
Lightweight neural network library written in ANSI-C supporting prediction and backpropagation for Convolutional- and Fully Connected neural networks
Built MLP with ReLU and Adam optimization with 2 layers, 3 layers and 5 layers and observed how it works.
Projects Related to Deep Learning Nanodegree
Use of word embeddings to classify sentiments of sentences and automatically attach emojis
The process of computationally identifying and categorizing opinions expressed in a piece of text, especially to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral. Understanding people’s emotions is essential for businesses since customers are able to express their thoughts and fe…
Files are part of my coursework from the course DSCI 6671 Deep Learning
MULTICLASS CLASSIFICATION WITH SOFTMAX FUNCTION
An algorithm to compute the softmax layer in neural networks using low floating-point precision arithmetic.
This project implements a handwritten digit detector using neural networks.
Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".
This repository contains code that implemented Mask Detection using MobileNet as the base model and Neural Network as the head model. Code draws a rectangular box over the person's face in red if no mask, green if the mask is on, with 99% accuracy in real-time using a live webcam. Refer to README for demo
My own implementation/experiments with a local softmax
Library which can be used to build feed forward NN, Convolutional Nets, Linear Regression, and Logistic Regression Models.
This is a pytorch implementation of the am_softmax, this softmax layer includes the class assignment fully connected layer, as it is required for it to be normalized.
Plots how the logit values that are passed into the softmax function change over time as the model is trained.
Sentiment analysis for Twitter's tweet (in Indonesia language) was built with 3 models to get a comparison in determining which model gives the best results for predicting a tweet to have a positive or negative meaning.
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