Skip to content
#

dense-layers

Here are 13 public repositories matching this topic...

Overparameterization and overfitting are common concerns when designing and training deep neural networks. Network pruning is an effective strategy used to reduce or limit the network complexity, but often suffers from time and computational intensive procedures to identify the most important connections and best performing hyperparameters. We s…

  • Updated Sep 1, 2020
  • Python

In this repository I have utilised 6 different NLP Models to predict the sentiments of the user as per the twitter reviews on airline. The dataset is Twitter US Airline Sentiment. The best models each from ML and DL have been deployed. It employs text preprocessing,

  • Updated Apr 30, 2021
  • Jupyter Notebook

A supermarket chain called Good Seed wanted to see if Data Science could help them comply with the law by ensuring that they did not sell age-restricted products to underage customers. My task was to build and evaluate a model to verify a person's age.

  • Updated Jul 3, 2024
  • Jupyter Notebook

Content: Structure of CNN, Convolutional layer, Pooling layer, Fully connected layer, Dense layer, output, Image classification, Creating, compiling and training the model on epochs, testing the model on gradio

  • Updated Apr 30, 2024
  • Jupyter Notebook

NLP-FinHeadlines-MoodTracker is a NLP project utilising sentiment analysis on financial news headlines. It employs a combination of CNN and LSTM layers to predict sentiment (positive, negative, neutral). The model incorporates an embedding layer, 1D convolution, max pooling, bidirectional LSTM, dropout, and dense layer for sentiment classification.

  • Updated Jul 14, 2023
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the dense-layers topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the dense-layers topic, visit your repo's landing page and select "manage topics."

Learn more